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Collision avoidance system
Collision avoidance system
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Nissan Leaf approaching a movable target performing an Automated Emergency Braking (AEB) test. The vehicle has AEB Pedestrian, AEB Cyclist, AEB City, and AEB Interurban as standard in 2018.[1]
Schematic of a collision avoidance system

A collision avoidance system (CAS), also known as a pre-crash system, forward collision warning system (FCW), or collision mitigation system, is an advanced driver-assistance system designed to prevent or reduce the severity of a collision.[2] In its basic form, a forward collision warning system monitors a vehicle's speed, the speed of the vehicle in front of it, and the distance between the vehicles, so that it can provide a warning to the driver if the vehicles get too close, potentially helping to avoid a crash.[3] Various technologies and sensors that are used include radar (all-weather) and sometimes laser (LIDAR) and cameras (employing image recognition) to detect an imminent crash. GPS sensors can detect fixed dangers such as approaching stop signs through a location database.[2][4][5][6] Pedestrian detection can also be a feature of these types of systems.

Collision avoidance systems range from widespread systems mandatory in some countries, such as automated emergency braking (AEB) in the EU, agreements between carmakers and safety officials to make crash avoidance systems eventually standard, such as in the United States,[7] to research projects including some manufacturer specific devices.[citation needed]

Similar systems exist in aviation (such as TCAS and ACAS X)[8] and maritime (such as MCAS).[9]

Advanced emergency braking system (AEBS)

[edit]
Main article: Automated emergency braking system

The World Forum for Harmonization of Vehicle Regulations defines AEBS (also automated emergency braking in some jurisdictions). UN ECE Regulation 131 requires a system that can automatically detect a potential forward collision and activate the vehicle braking system to decelerate a vehicle to avoid or mitigate a collision.[10] UN ECE regulation 152 says deceleration can be 5 meters per second squared.[11]

Once an impending collision is detected, these systems provide a warning to the driver. When the collision becomes imminent, they can take action autonomously without any driver input (by braking or steering or both). Collision avoidance by braking is appropriate at low vehicle speeds (e.g. below 50 km/h (31 mph)), while collision avoidance by steering may be more appropriate at higher vehicle speeds if lanes are clear.[12] Cars with collision avoidance may also be equipped with adaptive cruise control, using the same forward-looking sensors.

AEB differs from forward collision warning: FCW alerts the driver with a warning but does not by itself brake the vehicle.[13]

According to Euro NCAP, AEB has three characteristics:[14]

  • Autonomous: the system acts independently of the driver to avoid or mitigate the accident.
  • Emergency: the system will intervene only in a critical situation.
  • Braking: the system tries to avoid the accident by applying the brakes.

Time-to-collision could be a way to choose which avoidance method (braking or steering) is most appropriate.[15]

A collision avoidance system by steering is a new concept. It is considered by some research projects.[15] Collision avoidance system by steering has some limitations: over-dependence on lane markings, sensor limitations, and interaction between driver and system.[16]

Emergency steering function

[edit]

The emergency steering function, known as ESF, is an automated steering function that detects a potential collision and automatically activates the steering system for a limited duration to avoid or mitigate a collision.[17]

The emergency steering function for UNECE countries is described by regulation 79.[17]

Automated lane keeping Systems

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Main article: Automated lane keeping systems

Automated lane keeping systems (ALKS) deal with avoiding some cases of collisions.

ALKS[18] defines some concepts:

Imminent collision risk describes a situation or an event which leads to a collision of the vehicle with another road user or an obstacle which cannot be avoided by a braking demand with lower than 5 m/s

— Uniform provisions concerning the approval of vehicles about automated lane-keeping systems

Emergency Manoeuvre (EM) is a maneuver performed by the system in case of an event in which the vehicle is at imminent collision risk and has the purpose of avoiding or mitigating a collision.

— Uniform provisions concerning the approval of vehicles about automated lane-keeping systems

The activated system shall not cause any collisions that are reasonably foreseeable and preventable. If a collision can be safely avoided without causing another one, it shall be avoided. When the vehicle is involved in a detectable collision, the vehicle shall be brought to a standstill.

— Uniform provisions concerning the approval of vehicles concerning automated lane-keeping systems

The activated system shall detect the distance to the next vehicle in front as defined in paragraph 7.1.1. and shall adapt the vehicle speed to avoid collision.

— Uniform provisions concerning the approval of vehicles about automated lane-keeping systems

The activated system shall be able to bring the vehicle to a complete stop behind a stationary vehicle, a stationary road user, or a blocked lane of travel to avoid a collision. This shall be ensured up to the maximum operational speed of the system.

— Uniform provisions concerning the approval of vehicles concerning automated lane-keeping systems

The activated system shall avoid a collision with a leading vehicle (...)

The activated system shall avoid a collision with a cutting in the vehicle (...)

The activated system shall avoid a collision with an unobstructed crossing pedestrian in front of the vehicle.

— Uniform provisions concerning the approval of vehicles concerning automated lane-keeping systems

This document clarifies the derivation process to define conditions under which automated lane-keeping systems (ALKS) shall avoid a collision

— Uniform provisions concerning the approval of vehicles about automated lane-keeping systems, Guidance on traffic disturbance critical scenarios for ALKS

Regulations

[edit]

AEB and ALKS are each defined by one or several UN-ECE regulations.

Requirements related to AEB are regulations 131 and 152.[19]

Regulation 157 is related to ALKS.

Japan has required AEB since 2020 and ALKS since 2021. The European Union requires AEB since 2022 but did not define a date for ALKS.

Automobile manufacturers

[edit]

Various vendors provide AEB components to automakers.[20] The global automotive AEB system market consists of a few established companies that are manufacturers or suppliers of specialized AEB components or systems.[21] For example, the main vendors for radar systems include Bosch, Delphi, Denso, Mobileye, TRW, and Continental.[22] Automobile manufactures may describe the systems installed on their vehicles using different names to differentiate their marketing efforts.[13] A particular automaker may have systems and sensors sourced from a variety of suppliers.[23] Therefore, even a single car brand may offer various levels of technology sophistication and the: frequency of false alerts can be different from model to model and trim level to trim level, depending on the types of camera and/or laser-based systems installed.[23]

In countries, such as the UK, one-quarter of new vehicles might have some kind of AEB system; but only 1% of previously sold cars might have AEB.[13]

Audi

[edit]

"Pre sense" autonomous emergency braking system uses twin radar and monocular camera sensors[24] and was introduced in 2010 on the 2011 Audi A8.[25] "Pre sense plus" works in four phases. The system first provides warning of an impending accident, activating hazard warning lights, closing windows and sunroof, and pre-tensioning front seat belts. The warning is followed by light braking to get the driver's attention. The third phase initiates autonomous partial braking at a rate of 3 m/s2 (9.8 ft/s2). The fourth phase increases braking to 5 m/s2 (16.4 ft/s2) followed by automatic full braking power, roughly half a second before projected impact. "Pre sense rear", is designed to reduce the consequences of rear-end collisions. The sunroof and windows are closed and seat belts are prepared for impact. The seats are moved forward to protect the car's occupants. 2015 introduced the "avoidance assistant" system that intervenes in the steering to help the driver avoid an obstacle. If an accident occurs, the "turning assistant" monitors opposing traffic when turning left at low speeds. In critical situations, it stops the car. "Multi collision brake assist" uses controlled braking maneuvers during the accident to aid the driver. Both systems were introduced on the Second generation Q7.[26]

BMW

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In 2012 BMW introduced two systems on the 7 Series. "Active Protection" detects imminent accidents by pretensioning safety belts, closes windows and moonroof, brings the backrest of the front passenger seat to an upright position, and activates post-crash braking. A driver drowsiness detection includes advice to take a break from driving. An "Active Driving Assistant" combines lane departure warning, pedestrian protection, and city collision mitigation.[27]

In 2013, "Driving Assistant Plus" was introduced on most models combining the front-facing camera, lane-departure warning, and in some cases front radar sensors to detect vehicles ahead. Should the driver not react to the warning of a potential collision, the system would gradually prime brake pressure and apply – with maximum deceleration power – if necessary. In the case of a crash, the system can bring the vehicle to a standstill. Later iterations of the system on cars equipped with an Automatic Cruise Control system are improved by combining radar and camera detection during fog, rain, and other situations where normal camera operations may be compromised.[28]

Ford

[edit]
Collision warning and brake support on the 2009 Lincoln MKS

Beginning on the 2012 Ford Focus, Active City Stop was offered on the range-topping Titanium model, under the optional Sports Executive Pack. The system used windscreen-mounted cameras, radars, and lidars to monitor the road ahead. The system doesn't provide a warning, rather, it can prevent a crash from occurring at speeds between 3.6 and 30 km/h (2.2 and 18.6 mph). This speed was later raised to 50 km/h (31 mph) and was available on all models, the Trend, Sport, Titanium, ST, and RS (Limited Edition only).

General Motors

[edit]

General Motors' collision alert system was introduced in GMC Terrain SUVs in 2012. It uses a camera to provide a warning when there is a vehicle ahead or there is a lane departure.[29] The 2014 Chevrolet Impala received the radar- and camera-based crash imminent braking (radar technology detects a possible crash threat and alerts the driver. If the driver does not appear to react quickly enough or doesn't react at all, this feature intervenes to apply the brakes to avoid the crash. Forward collision alert, lane departure warning, side blind zone alert (using radar sensors on both sides of the vehicle, the system "looks" for other vehicles in the blind zone areas of the Impala and indicates their presence with LED-lit symbols in the outside mirrors. Rear cross-traffic alert features.[30]

Honda

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2003: Honda introduced autonomous braking (Collision Mitigation Brake System CMBS, originally CMS) front collision avoidance system on the Inspire[31] and later in Acura, using a radar-based system to monitor the situation ahead and provide brake assistance if the driver reacts with insufficient force on the brake pedal after a warning in the instrument cluster and a tightening of the seat belts.[32][33] The Honda system was the first production system to provide automatic braking.[33]

The system also incorporated an "E-Pretensioner", which worked in conjunction with the CMBS system with electric motors on the seat belts. When activated, the CMBS has three warning stages. The first warning stage includes audible and visual warnings to brake. If ignored, the second stage would include the E-Pretensioner's tugging on the shoulder portion of the seat belt two to three times as an additional tactile warning to the driver to take action. The third stage, in which the CMBS predicts that a collision is unavoidable, includes full seat belt slack take-up by the E-Pretensioner for more effective seat belt protection and automatic application of the brakes to lessen the severity of the predicted crash. The E-Pretensioner would also work to reduce seat belt slack whenever the brakes are applied, and the brake assist system is activated.[33]

2013: Honda introduced new driver-assistance system called Honda SENSING in 2014 Honda Legend, with the tagline, "Safety for Everyone".[34] The system uses two types of sensors: a millimeter-wave radar in the front grille and a monocular camera on the windshield. These sensors detect pedestrians and other objects with improved accuracy. Enhanced data processing capabilities allow the system to recognize surroundings, driver intentions, and vehicle conditions, enabling collaborative control over braking and steering. The system now includes six new functions, such as the Pedestrian Collision Mitigation Steering System, and assists in vehicle speed, following distance, and lane keeping. Honda continues to evolve its driver-assistive technologies globally to enhance safety and comfort.

2020: Honda announced the flagship variation of Honda SENSING called, Honda SENSING Elite. The system was first introduced in the new Honda Legend Hybrid EX. This system uses 3D maps, GNSS data, and sensors to monitor the vehicle's surroundings and driver condition. It can control acceleration, braking, and steering for smooth driving and includes features like hands-off lane change assist and Traffic Jam Pilot for Level 3 automated driving, approved by Japan's Ministry of Land, Infrastructure, Transport, and Tourism (MLIT).[35]

A key feature of Honda SENSING Elite is the hands-off function, which assists in steering the vehicle when the driver has their hands off the wheel, supported by adaptive in-lane driving. This aids in maintaining lane position at a pre-set speed and keeping a proper following distance from the vehicle ahead. The system also includes active lane change assist with hands-off function, which executes lane changes when the driver activates the turn signal and adjusts the vehicle's speed accordingly.[36]

2021: Honda introduced the Honda SENSING 360 system, enhancing the original with omnidirectional sensing. This system helps eliminate blind spots and reduce collisions by using five millimeter-wave radar units and a front camera. Key features include Front Cross Traffic Warning, which alerts drivers to approaching vehicles at intersections; Lane Change Collision Mitigation, which helps avoid collisions during lane changes; and Collision Mitigation Braking, which provides braking assistance at turns. It also features Adaptive Cruise Control with Cornering Speed Assist, adjusting speed during cornering for smoother navigation. The application of Honda SENSING 360 will begin in 2022, starting from China,[36] with the first vehicle equipped with Honda SENSING 360 being the eleventh-generation Honda Accord and sixth-generation Honda CR-V.

2022: Honda updated the SENSING 360 system with new features including Advanced Lane Driving and Lane Change Assist with hands-off capability, a Driver Emergency Support System, and Exit Warning. These additions enhance existing features like collision mitigation braking and front cross traffic warning.

2023: Honda SENSING 360+ builds on existing Honda SENSING 360 by adding a driver monitoring camera and high-definition maps. This updated system aims to reduce accidents caused by driver health issues or errors. It features Advanced Lane Driving with hands-off capability, Active Lane Change Recommendation, Predictive Curve Departure Warning, and an Exit Warning. The system will debut with the Accord in China in 2024 and expand globally. It includes a Driver Emergency Support System that helps stop the vehicle if the driver becomes unresponsive and connects to the HELPNET® emergency call center for added safety.[37]

Jaguar Land Rover

[edit]

As part of the InControl suite of services, Jaguar Land Rover provides several driver assistance technologies, amongst which are autonomous emergency braking, intelligent emergency braking, lane departure warning, blind spot monitor and blind spot assist.[38] The systems variously use both microwave and optical detection methods.[39]

Mercedes-Benz

[edit]

2002: Mercedes' "Pre-Safe" system was exhibited at the Paris Motor Show on the 2003 S-Class. Using electronic stability control sensors to measure steering angle, vehicle yaw, and lateral acceleration and brake assist (BAS) sensors to detect emergency braking, the system can tighten the seat belts, adjust seat positions, including rear seats (if installed), raise folded rear headrests (if installed), and close the sunroof if it detects a possible collision (including rollover).[40] A later version of the Pre-Safe system was supplemented by an additional function that can close any open windows if necessary.

2006: Mercedes-Benz's "Brake Assist BAS Plus" was their first forward warning collision system introduced on the W221 S-Class, it incorporates the autonomous cruise control system and adds a radar-based collision warning.

2006: the "Pre-Safe Brake" on the CL-Class C216[41] was their first to offer partial autonomous braking (40%, or up to 0.4g deceleration) if the driver does not react to the BAS Plus warnings and the system detects a severe danger of an accident.[42][43]

2009: Mercedes introduced the first Pre-Safe Brake with full (100%) autonomous braking with maximum braking force approximately 0.6 seconds before impact, on the Mercedes-Benz E-Class (W212).[44][45]

2013: Mercedes updated Pre-Safe on the W222 S-Class as plus with cross-traffic assist.[46] Pre-Safe with pedestrian detection and City Brake function is a combination of stereo camera and radar sensors to detect pedestrians in front of the vehicle. Visual and acoustic warnings are triggered when a hazard is spotted. If the driver then reacts by braking, the braking power will be boosted as the situation requires, up to a full brake application. Should the driver fail to react, the Pre-Safe Brake triggers autonomous vehicle braking. Pedestrian detection is active up to about 72 km/h (45 mph), and can reduce collisions with pedestrians autonomously from an initial speed of up to 50 km/h (31 mph).[46] A radar sensor in the rear bumper monitors the traffic behind the vehicle. If the risk of an impact from the rear is detected, the rear hazard warning lights are activated to alert the driver of the vehicle behind (not on vehicles with USA/Canada coding). Anticipatory occupant protection measures, such as the reversible belt tensioners, are deployed. If the vehicle is stopped and the driver indicates a wish to remain stationary – by depressing the brake pedal, activating the hold function, or moving the selector lever to "P" – the system increases the brake pressure to keep the vehicle firmly braked during a possible rear-end collision.[46] Pre-Safe Impulse works an early phase of the crash, before the resulting deceleration starts to increase, the front occupants are pulled away from the direction of impact and deeper into their seats by their seat belts. By the time the accident enters the phase when loads peak, the extra distance they are retracted by can be used while dissipating energy in a controlled fashion. Pre-acceleration and force limitation allow the occupants to be temporarily isolated from the effects of the crash, significantly reducing the risk and severity of injuries in a frontal collision.[46]

Nissan

[edit]

Nissan's Infiniti brand offers both laser-based and radar-based systems. Brake assist with preview function anticipates the need to apply emergency braking and pre-pressurize the brake system to help improve brake response. Intelligent brake assist (IBA) with forwarding emergency braking (FEB) (on QX80) uses radar to monitor the approaching speed of the vehicle ahead, helping detect an imminent collision. It provides a two-stage warning to alert the driver, and if the driver takes no action, the system automatically engages the brakes to mitigate the collision speed and impact. A predictive forward collision warning system warns the driver of risks that may be obscured from the driver's view. It senses the relative velocity and distance of a vehicle directly ahead, as well as a vehicle traveling in front of the preceding one. The forward emergency braking system judges that deceleration is required, it alerts the driver using both a screen display and sound, then generates a force that pushes the accelerator pedal up and applies partial braking to assist the driver in slowing the vehicle down. When the system judges that there is the possibility of a collision, it will automatically apply harder braking to help avoid one.

Nissan has been under investigation for collision avoidance systems on late-model Rogue models that allegedly brake the vehicles for no reason, according to the US National Highway Traffic Safety Administration (NHTSA).[47] As of September 2019, Nissan considered the issue strictly as a "performance update" by issuing technical service bulletins—at least three since January 2019—that pertain to reprogramming the radar control unit, according to the agency.[47] At least 553,860 cars are potentially affected[47] from the 2017 and 2018 model years.[48]

Subaru

[edit]

Subaru's system, branded "EyeSight", was announced in May 2008 using stereo camera technology to detect pedestrians and bicyclists. As initially announced, EyeSight enabled pre-collision braking control and adaptive cruise control at all speeds.[49] It was rolled out in Japan to selected models in 2010; in Australia in 2011; and in North America in 2012 for the 2013 model year Legacy and Outback models.[50] An alarm is used to warn the driver of a potential collision hazard in the pre-collision system.

The pre-collision braking control was upgraded in 2010 to allow the vehicle to stop automatically if the speed difference between the EyeSight-equipped vehicle and the object in front is less than 30 km/h (19 mph) and the driver takes no action to slow down or stop. Above 30 km/h (19 mph), the vehicle will reduce its speed automatically.[49] It also allows the vehicle to engage braking assist, if there is a risk of a frontal collision and the driver suddenly applies the brakes.[49] The speed difference to allow an automatic stop was raised to 50 km/h (31 mph) in 2013 with improved cameras.[51] The adaptive cruise control was also upgraded in 2010 to allow automatic emergency braking in traffic, fully stopping the EyeSight vehicle when the car in front has come to a complete stop.[49]

In 2013, color was added to the cameras, allowing the system to recognize brake lights and red stoplights ahead.[51] Subaru also added active lane-keeping (keeping the vehicle in the middle of the lane, and applying steering force to keep the vehicle in the lane when unintentionally crossing lane markers) and throttle management (to prevent sudden unintended acceleration in forward and reverse) systems in 2013 with the improved cameras.[51] EyeSight has been very popular, equipped on approximately 90% of all Legacy and Outbacks sold in Japan at the beginning of 2012,[50] and the engineers responsible for its development won a prize from the Japanese government that year.[52]

As of 2021, EyeSight is standard on the Ascent, Forester, Legacy, and Outback. It is also standard on all CVT-equipped Crosstrek, Impreza, and WRX. It became standard on the automatic-equipped BRZ as of 2022.

Toyota

[edit]
2008 LS 600h forward PCS diagram, with radar (blue) and stereo camera (red) coverage

Toyota's system, branded "Toyota Safety Sense" or "Lexus Safety System", is a radar-based system that uses a forward-facing millimeter-wave radar. When the system determines that a frontal collision is unavoidable, it preemptively tightens the seat belts, removing any slack, and pre-charges the brakes using brake assist to give the driver maximum stopping power when the driver depresses the brake pedal.

2003 February: Toyota launched PCS in the redesigned Japanese domestic market Harrier.[53]

2003 August: added an automatic partial pre-crash braking system to the Celsior.[54]

2003 September: PCS made available in North America on the Lexus LS 430, becoming the first radar-guided forward-collision warning system offered in the US.

2004: In July 2004, the Crown Majesta radar PCS added a single digital camera to improve the accuracy of collision forecast and warning and control levels.[55][56][57]

2006: Pre-collision system with Driver Monitoring System introduced in March 2006 on the Lexus GS 450h[55] using a CCD camera on the steering column. This system monitors the driver's face to determine where the driver is looking. If the driver's head turns away from the road and a frontal obstacle is detected, the system will alert the driver using a buzzer, and if necessary, pre-charge the brakes and tighten the safety belts.

2006: the Lexus LS introduced an advanced pre-collision system (APCS), added a twin-lens stereo camera located on the windshield, and a more sensitive radar to detect smaller "soft" objects such as animals and pedestrians. A near-infrared projector located in the headlights allows the system to work at night. With adaptive variable suspension (AVS) and electric power steering, the system can change the shock absorber firmness, steering gear ratios, and torque to aid the driver's evasive steering measures. The lane departure warning system will make automatic steering adjustments to help ensure that the vehicle maintains its lane in case the driver fails to react. Driver Monitoring System was introduced on the Lexus LS. The rear-end pre-collision system includes a rearward-facing millimeter-wave radar mounted in the rear bumper.[58] The system adjusts the active head restraints by moving them upward and forward to reduce the risk of whiplash injuries if an imminent rear collision is detected.[59]

2008: Improved driver monitoring system added on the Crown for detecting whether the driver's eyes are properly open.[60] It monitors the driver's eyes to detect the driver's level of wakefulness. This system is designed to work even if the driver is wearing sunglasses at night.

2008: PCS with GPS-navigation linked brake assist function on the Crown. The system is designed to determine if the driver is late in decelerating at an approaching stop sign, will then sound an alert, and can also pre-charge the brakes to provide braking force if deemed necessary. This system works in certain Japanese cities and requires Japan-specific road markings that are detected by a camera.

2009: Crown[61] added a front-side millimeter-wave radar to detect potential side collisions primarily at intersections or when another vehicle crosses the center line. The latest version tilts the rear seat upward, placing the passenger in a more ideal crash position if it detects a front or rear impact.[62]

2012: Higher speed APCS on the Lexus LS enables deceleration from up to 60 km/h (37 mph), compared to the previous of 40 km/h (25 mph). The higher-speed APCS uses the same technologies as the current APCs. This system increases the braking force up to twice that applied by average drivers. It was not then available in US markets.

2013: Pre-collision system with pedestrian-avoidance steer assist and steering bypass assist[63] can help prevent collisions in cases where automatic braking alone is not sufficient, such as when the vehicle is traveling too fast or a pedestrian suddenly steps into the vehicle's path. An onboard sensor detects pedestrians and issues a visual alert on the dashboard immediately in front of the driver if the system determines that there is a risk of collision. If the likelihood of a collision increases, the system issues an audio and visual alarm to encourage the driver to take evasive action, and the increased pre-collision braking force and automatic braking functions are activated.[64] If the system determines that a collision cannot be avoided by braking alone and there is sufficient room for avoidance, steer assist is activated to steer the vehicle away from the pedestrian.[65]

2016: Toyota announced it would make Toyota Safety Sense (TSS) and Lexus Safety System+ standard on nearly all Japan, Europe, and US models by the end of 2017.[66][67]

2017: Lexus introduced the updated Lexus Safety System+ 2.0 on the fifth-generation LS. In the US 2017 model year, Toyota sold more vehicles equipped with collision warnings than any other single brand with a total of 1.4 million sold or 56% of their fleet.[68]

2018: Toyota released its updated Toyota Safety Sense 2.0 (TSS 2.0) to include Lane Tracing Assist, Road Sign Assist, and Low Light Pedestrian Detection with Daytime Bicyclist Detection which improves the Pre-Collision System. The first Japanese car model to receive (TSS 2.0) is the executive Crown in its 15th generation.

2021: Lexus introduced the updated Lexus Safety System+ 3.0 in the Lexus NX. The suite contains a Risk Avoidance Emergency Steer Assist, a Right/Left Turn Oncoming Vehicle Detection/Braking, an Oncoming Vehicle Detection, a Dynamic Radar Cruise Control with Curve Speed Management, a Road Sign Assist, a Pre-Collision System, a Lane Assistance and an Intelligent High Beam.[69][70]

Volkswagen

[edit]
Laser sensor of a Volkswagen Up

2010: "Front Assist" on 2011 Volkswagen Touareg can brake the car to a stop in case of an emergency and tension the seat belts as a precautionary measure.[71]

2012: Volkswagen Golf Mk7 introduced a "Proactive Occupant Protection" that will close the windows and retract the safety belts to remove excess slack if the potential for a forward crash is detected. Multi-collision brake system (automatic post-collision braking system) to automatically brake the car after an accident to avoid a second collision. City emergency braking automatically activates brakes at low speeds in urban situations.

2014: Volkswagen Passat (B8) introduced pedestrian recognition as a part of the system. It uses a sensor fusion between a camera and the radar sensor. There is an "emergency assist" in case of a non-reacting driver, the car takes control of the brakes and the steering until a complete stop. This is also found in the Volkswagen Golf Mk8.

Volvo

[edit]
Volvo City Safety multiple cameras

2006: Volvo's "Collision Warning with Auto Brake" was introduced in 2007 S80. This system is powered by a radar/camera sensor fusion and provides a warning through a head up display that visually resembles brake lamps. If the driver does not react, the system pre-charges the brakes and increases the brake assist sensitivity to maximize driver braking performance. Later versions would automatically apply the brakes to minimize pedestrian impacts. In some models of Volvos, the automatic braking system can be manually turned off. The V40 also included the first pedestrian airbag, when it was introduced in 2012.[citation needed]

2013: Volvo introduced the first cyclist detection system. All Volvo automobiles now come standard with a lidar laser sensor that monitors the front of the roadway, and if a potential collision is detected, the safety belts will retract to reduce excess slack. Volvo now includes this safety device as an option in FH series trucks.[72][better source needed]

2015: "IntelliSafe" with auto brake at the intersection. The Volvo XC90 features automatic braking if the driver turns in front of an oncoming car. This is a common scenario at busy city crossings as well as on highways, where the speed limits are higher.[citation needed]

March 2020: Volvo recalled 121,000 cars over auto emergency braking failure.[73] The system may not detect an object and so may not work as intended, increasing the risk of a crash.[73]

List of cars with available collision avoidance features

[edit]

New car assessment program

[edit]

Euro NCAP and C-NCAP and ANCAP are involved in taking into account the Autonomous Emergency Braking (AEB) in their respective New Car Assessment Program.[75]

Since 2016, Euro NCAP has taken into account pedestrians in the AEB rating.[75]

In 2018, Euro NCAP provided assessments for AEB city (since 2014), AEB interurban (since 2014), AEB pedestrian (since 2018), and AEB cyclist (since 2018). Since 2018, ANCAP has also provided assessments for AEB city, AEB interurban, AEB pedestrian, and cyclist.

Cost

[edit]

Many vehicles have AEB fitted as standard. The AEB is not available for every car. When AEB is available as an option, its cost can be in the £180 (AEB city only) – £1300 (regular AEB) range.[13]

The cost of optional AEB will depend, in part, on whether certain other safety systems are installed. The electronic and sensor systems that underpin adaptive cruise control and forward-collision warning systems, for example, are well-suited, if not prerequisites, to an AEB system.[76]

Aviation

[edit]

In aviation, systems such as the Traffic Collision Avoidance System (TCAS) and its successor, Airborne Collision Avoidance System X (ACAS X), are widely used to detect and prevent mid-air collisions. These systems rely on transponder signals and predictive algorithms to provide resolution advisories to pilots, and are standard in most commercial aircraft.[8]

Maritime

[edit]

In the maritime domain, collision avoidance technologies are increasingly integrated into both commercial and recreational vessels. For example, Watchit has developed a smart navigation safety system that uses artificial intelligence to process data from onboard sensors, such as GPS, compass, sonar, wind, and speed, to generate real-time audio and visual alerts. The system is designed to help prevent both vessel-to-vessel collisions and groundings by dynamically adapting alert zones based on the vessel's speed and direction.[9]

These domain-specific systems share similar goals with automotive CAS, aiming to reduce accidents by augmenting human situational awareness and reaction times.

See also

[edit]

References

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Collision avoidance system

View on Grokipedia
from Grokipedia
A collision avoidance system (CAS), also known as an anti-collision system, is an active engineered to detect imminent hazards and either alert operators or autonomously intervene to prevent or lessen the severity of collisions. These systems typically employ sensors such as , , cameras, and ultrasonic devices to monitor the environment, process data in real-time, and generate responses like audible warnings, visual alerts, or automatic braking and steering adjustments. In the automotive sector, CAS features prominently in advanced driver-assistance systems (ADAS), where forward collision warning and automatic emergency braking help mitigate rear-end crashes by monitoring vehicle speed, distance to the leading vehicle, and potential obstacles. The U.S. (NTSB) has evaluated such systems, finding that they can significantly reduce crash frequency when paired with active braking capabilities. In aviation, the (ACAS), including variants like the (TCAS), operates independently of to provide traffic advisories and resolution advisories, recommending vertical maneuvers to avert mid-air collisions. Developed following the 1987 Airport and Airway Safety and Capacity Expansion Act (Public Law 100-223) and the subsequent 1989 FAA rule, TCAS II became mandatory on large passenger aircraft (more than 30 seats) by December 31, 1993, enhancing global airspace safety. CAS implementations extend to maritime navigation, where automatic identification systems (AIS) and radar-based tools prevent vessel collisions, and to industrial settings like mining equipment, governed by standards such as ISO 21815 for earth-moving machinery collision warnings. Key challenges in these systems include sensor reliability in adverse conditions, integration with , and adherence to regulatory frameworks from bodies like for automotive applications and the (ICAO) for aviation. Ongoing advancements, such as ACAS X for accommodating unmanned aerial systems, aim to address increasing air traffic densities into the .

Overview

Definition and Principles

A collision avoidance system (CAS) is an automated integrated into vehicles that employs sensors, software algorithms, and actuators to detect potential collisions and execute evasive maneuvers, such as braking or , independent of intervention. These systems primarily target scenarios like rear-end, , or lane-departure incidents, enhancing overall vehicle by monitoring the surrounding environment in real time. In automotive applications, representative examples include forward collision warning (FCW) and autonomous emergency braking (AEB), which form core components of advanced driver-assistance systems (ADAS). The core principles of CAS revolve around reactive and proactive avoidance strategies, alongside seamless integration with vehicle dynamics control systems like (ESC) and anti-lock braking systems (ABS). Reactive approaches respond to immediately detected threats, such as issuing auditory or visual alerts via FCW when a time-to-collision (TTC) threshold—typically around 2-5 seconds—is breached, or applying partial braking through dynamic brake support if the driver does not react. In contrast, proactive methods anticipate risks by predicting trajectories using data from ongoing sensor inputs, such as adjusting speed in (ACC) to maintain a safe before a threat materializes. This integration ensures that avoidance actions align with the vehicle's stability limits, preventing oversteer or understeer during maneuvers. Key benefits of CAS include substantial reductions in accident rates, particularly for , which account for a significant portion of crashes. Early systems demonstrated a 25% decrease in rear-end collision exposure on freeways at speeds of 35 mph or higher, with overall crash prevention estimates ranging from 6-15% across U.S. roadways based on field operational tests involving over 158,000 km of driving. A commercial fleet study reported a 71% reduction in rear-end incidents, while insurance data indicated 7-14% lower claim frequencies for equipped vehicles. These outcomes underscore CAS's role in mitigating , such as distraction, which contributes to approximately 93% of crashes. The basic operational flow of a CAS consists of three stages: environmental sensing, , and response execution. Sensors continuously scan for obstacles, measuring parameters like relative speed and to compute collision risk via algorithms such as TTC estimation. Processed data triggers appropriate actions—warnings for low-risk scenarios or automated interventions like AEB for imminent threats—ensuring timely mitigation while allowing override to maintain control. This cycle operates above minimum speeds (e.g., 20-25 mph) and disengages in low-speed or non-threatening conditions to avoid unnecessary activations.

Historical Development

The origins of collision avoidance systems trace back to the sector in the 1950s, where -based technologies were developed to mitigate risks. Early efforts focused on ground-based for , evolving into airborne systems like the initial prototypes for the (TCAS), which used transponders and directional antennas to provide pilots with resolution advisories. These innovations laid foundational principles for detecting proximity and issuing automated alerts, influencing later ground vehicle applications. Automotive collision avoidance emerged in the with experimental prototypes integrating sensors for obstacle detection. A notable early example was Cadillac's system, introduced in on the DeVille model, which employed thermal imaging cameras to enhance driver visibility and warn of potential collisions in low-light conditions up to 500 meters ahead. This marked a shift toward active safety in passenger vehicles, though initial systems were limited by sensor range and processing constraints. By the early 2000s, and began appearing in production models, enabling forward collision warning features. The 2000s saw key milestones in commercial deployment, particularly in , where regulatory pressures accelerated adoption. In 2008, launched the Collision Warning with Auto Brake (CWAB) on the S80 sedan, a - and camera-fusion that could autonomously apply brakes to mitigate rear-end collisions at highway speeds, for example reducing impact speed from 60 km/h to 50 km/h. This represented an early form of Advanced Emergency Braking (AEBS), reducing rear-end conflicts by approximately 37% in related studies. Concurrently, influential events like the () began incorporating active safety ratings in 2009, incentivizing manufacturers to integrate collision avoidance to achieve higher scores. In the United States, the (NHTSA) played a pivotal role through mandates for heavy vehicles, finalizing requirements for on heavy trucks and large buses in 2015 (effective model years 2017-2018) and proposing forward collision warning and automatic emergency braking for certain heavy vehicles in 2015, with final mandates issued in 2024. The witnessed widespread ADAS integration, with collision avoidance becoming standard in mid-to-high-end vehicles by 2015, driven by falling sensor costs and improved algorithms. Technological shifts during this period transitioned from rule-based decision-making—relying on predefined thresholds for braking—to models in the late , enabling predictive behaviors like trajectory forecasting. This evolution was bolstered by exponential increases in computing power, aligning with , which allowed real-time processing of multi-sensor data for complex scenarios. Entering the 2020s, post-regulation advancements emphasized AI-driven prediction, with systems incorporating for pedestrian and cyclist detection amid stricter global standards like the UN ECE's 2022 AEBS mandates. In 2024, the NHTSA finalized requirements for AEB systems on passenger cars and light trucks, mandating performance standards effective for 2029. These developments built on prior sensor evolution from single units to fused camera-lidar arrays, enhancing accuracy in diverse environments.

Core Technologies

Sensors and Detection Methods

Collision avoidance systems rely on a variety of sensors to detect and track objects in the vehicle's environment, enabling timely identification of potential hazards. Primary sensors include , , and cameras, each offering distinct capabilities for range, , and visual recognition. These technologies provide the necessary for environmental , with excelling in adverse weather, in precise 3D mapping, and cameras in detailed object classification. Radar sensors operate by emitting radio waves in the 24–79 GHz frequency range and measuring the time-of-flight of echoes to determine , via Doppler shift, and approximate to objects. They typically achieve detection ranges of up to 250 meters for long-range applications, making them suitable for forward collision warning and . Advantages include robust performance in , , and , as well as the ability to penetrate dust and measure speed accurately. However, suffers from average , leading to challenges in distinguishing closely spaced objects, and can produce false positives from environmental clutter like guardrails. LiDAR (Light Detection and Ranging) systems use near-infrared pulses at wavelengths such as 905 nm or 1550 nm to create high-resolution 3D point clouds of the surroundings through time-of-flight measurements. With detection ranges around 200 meters and resolutions down to centimeters, enables detailed mapping of object shapes and positions, which is critical for precise obstacle avoidance. Its strengths lie in superior spatial accuracy for small or complex objects compared to . Drawbacks include vulnerability to in or , which can reduce effective range significantly. Mechanical spinning components in traditional designs also add complexity and potential failure points, although advancements in solid-state mitigate these issues and have decreased costs to hundreds of dollars per unit as of 2025. Cameras, including , , and variants, capture images in the visible (400–780 nm) or near-infrared spectrum to enable computer vision-based , such as identifying pedestrians or vehicles through . They offer detection ranges up to 200 meters with very high resolution for texture and color details, at a relatively low cost. configurations can infer depth via disparity analysis, supporting 3D perception. Limitations are prominent in low-light conditions, glare, or adverse weather like and , where visibility drops sharply—e.g., camera range may reduce to 30 meters in dense —necessitating complementary sensors for reliability. Complementary sensors augment primary detection with specialized short-range or positioning capabilities. Ultrasonic sensors emit sound waves at 40–70 kHz to measure proximity, achieving ranges of about 5 meters with low cost and effectiveness in parking maneuvers or blind-spot monitoring. They tolerate dirt well but have poor and are attenuated by humidity or wind noise. GPS (Global Navigation Satellite System) receivers use L-band signals (~1.575 GHz) for global positioning with 3–10 meter accuracy, aiding in path prediction and localization, while Inertial Measurement Units () track acceleration and orientation for real-time . GPS requires clear sky view and suffers in urban canyons, whereas IMUs accumulate drift errors over time without correction. Sensor fusion techniques integrate data from multiple sources to enhance overall accuracy, robustness, and redundancy, mitigating individual sensor weaknesses. A common method is Kalman filtering, which recursively estimates system states by combining noisy measurements—e.g., fusing velocity with positions or GPS/IMU data—to reduce uncertainty and improve tracking in dynamic environments. This probabilistic approach, often extended to extended or unscented variants for nonlinearities, enables more reliable object localization than single-sensor reliance. Despite these advances, sensor limitations persist, particularly from environmental factors and deployment barriers. Cameras and performance degrades markedly in and heavy rain, with detection ranges and accuracy reduced by up to 50% or more depending on conditions, while remains largely unaffected but may generate clutter. Cost remains a key hurdle for widespread LiDAR adoption, whereas fusion helps but increases computational demands.
Sensor TypeTypical RangeKey AdvantagesKey Limitations
RadarUp to 250 mWeather-resistant; measurementAverage resolution; clutter interference
LiDARUp to 200 mHigh 3D resolution; precise mapping/rain attenuation
CamerasUp to 200 mLow cost; visual recognitionLighting/weather sensitivity
UltrasonicUp to 5 mInexpensive; short-range accuracyPoor in /
GPS/IMUGlobal (GPS); motion-based (IMU)Positioning and dynamics trackingSignal blockage (GPS); drift (IMU)

Algorithms and Decision-Making

Collision avoidance systems rely on core algorithms to process data into actionable insights, beginning with object and trajectory . Object typically employs convolutional neural networks (CNNs) to identify and categorize potential obstacles such as vehicles, pedestrians, or cyclists from visual or fused inputs. For instance, CNN-based models integrate vision and data to achieve robust in autonomous vehicle environments, enabling accurate detection even under varying lighting or occlusion conditions. Following , trajectory uses probabilistic models to forecast the future paths of detected objects, accounting for uncertainties in motion. These models, often based on Kalman filters extended with , predict multi-modal trajectories to anticipate possible collision scenarios with high reliability. Decision-making frameworks in collision avoidance systems integrate these predictions to determine intervention levels, commonly using threshold-based activation criteria. A key metric is the time-to-collision (TTC), which quantifies the urgency of a potential impact; for example, systems may trigger responses when TTC falls below 1.5 seconds to ensure timely evasion. These frameworks often employ hierarchical control strategies that escalate actions progressively: starting with auditory or haptic warnings to alert the driver, advancing to partial braking for deceleration, and culminating in full autonomous or braking if necessary. This layered approach minimizes unnecessary interventions while maximizing safety, as demonstrated in implementations that optimize for . The TTC is derived from basic kinematic principles assuming constant relative velocity between objects. Let dd represent the current between the host vehicle and the , and vrelv_{rel} the (typically the difference in their speeds along the ). The TTC is then given by: TTC=dvrelTTC = \frac{d}{v_{rel}} This formula estimates the time remaining until collision if no corrective action is taken, with vrel>0v_{rel} > 0 indicating closing motion. For example, if d=50d = 50 meters and vrel=20v_{rel} = 20 m/s, TTC = 2.5 seconds, prompting a warning threshold. In practice, extensions account for by incorporating higher-order terms, such as TTC=dvrel+vrel2arelTTC = \frac{d}{v_{rel}} + \frac{v_{rel}}{2a_{rel}} where arela_{rel} is relative , to refine predictions in dynamic scenarios. Recent advancements incorporate (RL) to enable adaptive responses that learn from simulated or real-world interactions, improving in complex, uncertain environments. RL agents, such as those using deep Q-networks, optimize collision avoidance policies by balancing exploration of maneuvers with reward functions penalizing near-misses, outperforming rule-based methods in evasion success rates and adaptability in high-density scenarios. Ethical considerations arise in decision prioritization, particularly in unavoidable collisions analogous to the , where systems must weigh outcomes like minimizing harm to occupants versus vulnerable road users. Frameworks like ethical valence theory propose claim mitigation strategies to resolve such dilemmas, prioritizing deontological rules (e.g., protecting pedestrians) while adhering to legal standards, though real-world implementation remains debated due to cultural variations in moral preferences.

Automotive Applications

Advanced Emergency Braking System (AEBS)

The Advanced Emergency Braking System (AEBS) is an active feature designed to detect an imminent collision with a or other obstacle ahead and automatically apply the brakes to mitigate or avoid the impact. It typically issues a forward collision warning to the driver first, followed by partial or full braking intervention if the driver does not respond adequately. Many modern AEBS implementations include detection capabilities, which extend protection to vulnerable road users by recognizing human shapes in the vehicle's path using camera and fusion. AEBS variants are categorized by operational speed ranges and direction of travel. City-speed AEBS operates at low velocities, typically up to 30-50 km/h, making it suitable for urban environments with frequent stops and short following distances. In contrast, highway-speed AEBS functions at higher velocities, up to 200 km/h or more, addressing scenarios on open roads where closing speeds are greater. Forward-facing AEBS is the most common, focusing on the vehicle's front; however, rear and reverse variants exist to prevent collisions during backing maneuvers, using rear-facing sensors to detect approaching objects or pedestrians. Performance evaluations demonstrate AEBS's effectiveness in reducing collisions, with studies showing a 40-50% decrease in rear-end crashes for equipped vehicles compared to those without. Activation typically occurs when the system predicts an unavoidable collision, applying deceleration forces of at least 0.5g (approximately 5 m/s²) to bring the to a halt or significantly slow it. These systems rely on underlying sensors such as and cameras for detection, though their efficacy can vary. AEBS often integrates with (ACC), enhancing longitudinal control by seamlessly transitioning from speed maintenance to emergency braking during traffic flow disruptions. Limitations include reduced performance on curved roads, where sensor blind spots may fail to detect targets in adjacent lanes, and challenges in multi-object scenarios, such as distinguishing primary threats amid clutter like vehicles and debris. These constraints highlight the need for driver vigilance in complex environments.

Emergency Steering and Lane Keeping

Emergency steering systems, also known as Automatic Emergency (AES), represent a critical lateral control mechanism in collision avoidance by applying corrective steering to the wheels, enabling it to swerve around detected obstacles when a frontal collision is imminent and insufficient space exists for evasion through braking alone. These systems typically activate at speeds exceeding 50 km/h, integrating data from cameras, , or to assess the environment and compute an evasive path that minimizes risk to occupants and other road users. By overlaying steering input on the driver's actions or autonomously directing the , AES facilitates rapid lateral maneuvers, such as shifting to an adjacent or the roadside shoulder, to avert impacts with oncoming traffic, pedestrians, or barriers. Lane keeping systems (LKS), often integrated with or complementary to emergency , provide ongoing lateral control to maintain the vehicle's position within detected lane markings, using continuous steering corrections to counteract unintentional drift caused by driver inattention, , or external factors like wind gusts. These systems rely on forward-facing cameras to identify lane boundaries through algorithms, applying subtle torque via steering to guide the vehicle back toward the lane center without fully overriding driver input. Unlike reactive emergency steering, LKS operates proactively across a range of speeds, typically above 60 km/h, to prevent lane departure incidents that contribute significantly to run-off-road and head-on collisions. At the core of both emergency steering and LKS are path planning algorithms that generate feasible trajectories for lateral control, with the Stanley controller serving as a widely adopted geometric method for computing optimal angles. The Stanley controller calculates the required input by minimizing the combined error between the vehicle's current position and heading relative to the reference path, using the formula δ=θe+arctan⁡(kevx)\delta = \theta_e + \arctan\left(\frac{k_e}{v_x}\right), where δ\delta is the angle, θe\theta_e is the heading error, kek_e is the lateral position error, and vxv_x is the longitudinal velocity; this approach ensures stable tracking even at higher speeds by prioritizing front-axle alignment. In emergency scenarios, the controller adapts to dynamic constraints like and obstacle proximity, generating swerve paths that respect tire grip limits to avoid rollover. For routine lane keeping, it enables smooth corrections by iteratively updating the path based on real-time feedback, though integration with enhances robustness in curved sections. Real-world effectiveness data indicate that LKS and related lane support features can reduce lane departure crashes by 20-30%, particularly head-on and single-vehicle incidents on dry roads with speed limits between 70 and 120 km/h, as evidenced by fleet studies in . Emergency steering complements this by addressing acute evasion needs, with simulations showing up to 80% success in avoiding collisions at highway speeds when combined with timely detection. However, challenges persist, including false activations on unmarked or poorly delineated roads, where the absence of clear edges leads to erroneous corrections or system disengagement, potentially increasing driver frustration and overreliance. Such limitations underscore the need for multi-sensor fusion to improve reliability in adverse conditions like zones or rural areas without standard markings.

Pedestrian and Cyclist Detection

Pedestrian and cyclist detection in collision avoidance systems relies on advanced AI models trained on large-scale datasets to classify vulnerable road users (VRUs) based on , , and motion patterns. The nuScenes dataset, featuring multimodal sensor data from urban environments including 3D annotations for pedestrians and cyclists, serves as a key resource for training these models, enabling robust through techniques like of cameras and . These AI approaches, often employing convolutional neural networks, distinguish VRUs from other objects by analyzing bounding boxes and trajectories, achieving improved accuracy in diverse scenarios such as crossings or sidewalks. Night-time detection is enhanced by (IR) technologies, which capture signatures to identify pedestrians and cyclists in low-light conditions where visible cameras falter. Far-infrared (FIR) systems, for instance, detect heat-emitting VRUs up to 90 away, unaffected by glare from headlights or poor weather, and integrate with AI for real-time classification. This complements daytime vision-based systems, providing redundancy for automatic emergency braking (AEB) activation. Upon detection, response mechanisms include automatic braking tailored to VRU paths, evasive steering to avoid intrusions from sidewalks or crosswalks, and audible warnings to alert drivers of imminent risks. These actions are calibrated for non-motorized users, such as predicting a cyclist's trajectory to initiate partial or full braking, often reducing impact speeds by up to 30 km/h in scenarios. Integration with forward collision warning systems ensures escalating alerts, from visual displays to haptic feedback, prioritizing VRU safety in urban settings. Performance metrics demonstrate effective detection ranges of up to 100 meters in daytime urban conditions using radar-camera fusion, with success rates around 70% for crash avoidance in controlled tests involving crossing s. For cyclists, similar systems achieve 60-80% mitigation in low-speed intersections, though night-time efficacy drops without IR enhancements, highlighting the need for multi-sensor approaches. Studies from the , including IIHS evaluations, show these systems reduce pedestrian crash risks by 25-32% overall in real-world applications. Key challenges include occlusion in crowded urban environments, where partially blocked VRUs reduce detection rates to 65-75%, necessitating advanced models to infer hidden parts from visible cues. Additionally, predicting cyclist speeds benefits from (V2X) communication, which shares trajectory data to enhance time-to-collision estimates and enable proactive warnings, addressing limitations in isolated sensor data.

Regulations and Standards

Global Regulatory Frameworks

The United Nations Economic Commission for (UNECE) World Forum for Harmonization of Vehicle Regulations (WP.29) serves as the primary international body developing global standards for collision avoidance systems, operating under the 1958 Agreement concerning the Adoption of Harmonized Technical Regulations for Wheeled Vehicles, Equipment and Parts, which has 55 contracting parties including the , , and . This framework facilitates the adoption of uniform technical regulations to enhance vehicle safety, with WP.29 establishing UN Regulation No. 131 in 2012 to specify requirements for Advanced Emergency Braking Systems (AEBS) on heavy vehicles such as trucks and buses, which became mandatory for new types in the EU from November 2013 and for all new vehicles from 2015. In 2016, WP.29 authorized further development of AEBS provisions under UN R131 to improve performance in complex scenarios, reflecting ongoing policy evolution toward broader application. For passenger cars, UN Regulation No. 152 was adopted in 2019, extending AEBS mandates to light vehicles and requiring compulsory implementation in the EU for new models from July 2022 and all new cars from July 2024. In the United States, the (NHTSA) oversees (FMVSS), with AEBS remaining voluntary for light vehicles until the finalization of FMVSS No. 127 in April 2024, which mandates AEB systems—including pedestrian detection—on passenger cars and light trucks with a gross rating under 10,000 pounds, effective September 2029. For heavy vehicles, NHTSA and the (FMCSA) proposed a mandate in June 2023 under a new FMVSS, targeting trucks over 10,000 pounds GVWR with a phase-in beginning in 2027 for Class 7-8 vehicles and 2028 for Class 3-6, with the final rule still pending as of November 2025. Prior proposals, such as those in 2018, focused on and voluntary for heavy trucks, but no binding phase-in occurred until the 2023 initiative. Other regions have aligned with or adapted UNECE standards while pursuing localized mandates. In , the national standard GB/T 39901-2021, issued in March 2021, outlines performance requirements and test methods for AEBS on passenger cars, initially as a recommended guideline, with a draft revision for mandatory status undergoing ending November 11, 2025, expected to take effect following approval, potentially in 2026. 's Australian Design Rules (ADR) were updated post-2020 through ADR 98/00, which mandates car-to-car AEBS for all new passenger vehicles and light goods vehicles from March 2025, with extensions proposed in ADR 98/02 for detection to further incentivize advanced systems. For heavy vehicles, ADR 97/00 requires AEBS on new trucks from 2025, harmonizing with UN R131. Enforcement of these frameworks occurs at the national level by contracting parties to the 1958 Agreement, which obligates signatories to apply approved regulations without additional modifications and impose sanctions for non-compliance, such as fines, recalls, or import bans. In the EU, non-adherence to WP.29 regulations like UN R152 can result in administrative fines up to €100,000 per under type-approval directives, alongside market withdrawal. Similarly, NHTSA enforces FMVSS through civil penalties of up to $25,975 per violation for manufacturers, escalating for continued non-compliance, while China's Ministry of Industry and oversees GB standards with penalties including production halts and fines equivalent to 3-5% of annual revenue. Australia's Department of Infrastructure enforces ADRs via fines up to AUD 10,000 for individuals and higher for corporations, ensuring progressive alignment with global safety goals.

Testing and Certification Protocols

Testing and certification protocols for collision avoidance systems in vehicles involve standardized procedures to evaluate system performance under controlled conditions, ensuring reliability in preventing or mitigating collisions. Organizations such as and the (IIHS) define specific test scenarios that simulate real-world crash risks, focusing on autonomous emergency braking (AEB) and related functions. These protocols emphasize repeatable assessments using target vehicles, pedestrian dummies, and environmental variables to measure detection accuracy, braking response, and avoidance efficacy. In Euro NCAP's AEB Car-to-Car () tests, scenarios include approaching a stationary or moving target vehicle at speeds up to 50 km/h, where the system must either fully avoid collision or reduce impact speed significantly. For instance, the C2C rear-end test evaluates braking at closing speeds of 50 km/h against a stopped lead vehicle, awarding points based on speed reduction or avoidance. IIHS complements this with track-based detection tests using adult- and child-sized dummies that cross or walk parallel to the vehicle's path at speeds of 12-25 mph (19-40 km/h), assessing both daytime and nighttime performance. These tests incorporate articulating dummies to mimic human movement, ensuring the system detects and brakes for vulnerable road users. Key performance metrics include response time, defined as the interval from obstacle detection to braking initiation, typically required to be under 0.5 seconds in high-speed scenarios to allow effective avoidance. Avoidance success rates are evaluated against thresholds exceeding 90% for full credit in protocols, with partial mitigation scored based on velocity reduction (e.g., at least 10 km/h decrease). Environmental simulations test robustness, such as nighttime conditions with illuminance below 1 or reduced visibility due to , where systems must maintain detection rates comparable to daylight tests. These metrics prioritize conceptual reliability over exhaustive data, focusing on scenarios that represent common crash types. Certification relies on standards like , which outlines requirements for electrical and electronic systems in vehicles, classifying collision avoidance functions at Automotive Safety Integrity Levels (ASIL) B to D based on hazard severity. Compliance involves , verification through simulation and physical tests, and documentation of fault-tolerant design. Recent extensions, such as ISO/PAS 8800:2022, address AI validation in these systems by integrating safety assurance for components, emphasizing scenario coverage and in edge cases. Bodies like TÜV SÜD and UL conduct independent audits to certify adherence. Challenges in these protocols include achieving reproducibility for edge cases, such as sudden cut-ins or occluded pedestrians, where slight variations in sensor input can alter outcomes. Debates persist on virtual versus physical testing: simulations enable scalable exploration of rare scenarios but struggle with real-world physics fidelity, while physical tests offer authenticity at higher cost and lower throughput. Hybrid approaches are emerging to balance these, as recommended in ongoing ISO updates.

Manufacturer Implementations

European Automakers

European automakers have been pioneers in integrating collision avoidance systems, leveraging stringent safety regulations to advance predictive and reactive technologies across their lineups. These implementations often emphasize seamless and occupant protection, setting benchmarks for luxury and mass-market vehicles alike. Audi's Pre Sense system, a suite of advanced driver assistance technologies, was first introduced in the late 2000s and has evolved to include preemptive measures such as seatbelt tensioning and window closure in anticipation of collisions. Key variants like Pre Sense City and Pre Sense Front use and camera sensors to detect urban obstacles and initiate partial braking, reducing impact severity. The system incorporates predictive elements, such as monitoring for unstable maneuvers to trigger protective actions. In 2025 models, Pre Sense forms a full suite integrated with and lane-keeping, enhancing overall autonomy in vehicles like the Q6 e-tron. BMW launched its Active Guard system in 2016 with the 7 Series, combining advanced emergency braking (AEBS) with lane-keeping assistance to mitigate frontal and lateral risks. This package employs forward collision warning, automatic emergency braking, and side collision avoidance, using cameras and to intervene if the driver does not respond. Active Guard integrates with the iDrive operating system, which receives AI-driven updates for improved , such as contextual lane changes and enhanced monitoring in post-2024 software releases. Mercedes-Benz pioneered preventive safety with the PRE-SAFE system, originating in 2002 on the S-Class as an industry-first occupant protection concept that detects imminent crashes via sensors. Evasive steering capabilities were added later, with the Evasive Steering Assist feature introduced in 2015 to provide support during obstacle avoidance maneuvers, detecting pedestrians or vehicles in the path. The S-Class serves as a benchmark for these technologies, featuring PRE-SAFE Impulse Side for lateral collision preparation, including seat adjustments and belt tightening, which has influenced broader model adoption. Volkswagen's Front Assist and Lane Assist systems provide core collision avoidance, with Front Assist using radar to warn of and brake for impending frontal impacts, including pedestrian detection, while Lane Assist employs camera-based steering corrections to maintain lane position above 40 mph. In the ID series of electric vehicles, launched post-2020, these features are deeply integrated with the IQ.DRIVE suite, offering autonomous emergency braking and lane-keeping tailored for EV dynamics, as seen in the 2025 ID.4 with enhanced cyclist monitoring. Adoption trends among European automakers have been propelled by General Safety Regulation mandates, requiring AEBS on all new vehicle types since 2020 and all new registrations since 2022, resulting in over 95% coverage of rated models by standards and near-universal fitment in new cars by 2025. This compliance has driven innovations, with market analyses projecting continued growth in ADAS penetration to exceed 90% across the fleet by mid-decade.

North American and Asian Automakers

North American automakers have integrated collision avoidance systems into their advanced driver-assistance suites, often emphasizing hands-free operation and integration with existing braking technologies to enhance highway safety. introduced Super Cruise in 2017 as the industry's first hands-free driver assistance system, with significant expansions starting in 2021 across more vehicle models, incorporating features like Enhanced Automatic Emergency Braking and Forward Collision Alert to detect and mitigate potential collisions during hands-free driving on compatible roads. In the , these systems are bundled under Chevy Safety Assist, which became standard and includes Automatic Emergency Braking, Forward Collision Alert, and Front Pedestrian Braking to help avoid or reduce the impact of frontal collisions with vehicles or pedestrians. Ford has similarly advanced its offerings through BlueCruise, a hands-free that builds on Pre-Collision Assist with Automatic Emergency Braking (AEB), using and cameras to detect potential frontal impacts and apply brakes if the driver does not respond, with software updates post-2021 enhancing responsiveness in dynamic conditions like curves or inclement weather. This integration is particularly prominent in trucks like the F-150, where post-2022 models standardize AEB as part of Ford Co-Pilot360, focusing on robust detection for larger vehicles to prevent collisions in work and towing scenarios. Asian automakers, influenced by dense urban traffic and government-backed intelligent initiatives, have prioritized comprehensive sensing suites with forward-looking avoidance capabilities. Honda launched the Honda Sensing suite in 2016 on models like the Accord, featuring Collision Mitigation Braking System (CMBS) that uses a camera and millimeter-wave to detect vehicles ahead and apply emergency braking if needed, with later enhancements from 2018 onward adding cyclist and pedestrian detection to broaden collision avoidance. pioneered its Safety Sense package with origins announced in 2014 for 2015 model-year vehicles, including the Pre-Collision System on the Prius that employs predictive logic via and camera fusion to anticipate and warn of impending collisions, applying partial or full braking to mitigate impacts with vehicles or pedestrians. Regional trends reflect differing regulatory landscapes, with North American adoption of collision avoidance features remaining largely voluntary but accelerating due to consumer demand and insurer incentives; for instance, forward crash prevention systems, including AEB, reached approximately 36% penetration in the U.S. vehicle fleet by 2025, up from about 8% in 2019. In 2024, NHTSA finalized a mandate requiring AEB on all new light vehicles by September 2029, further driving implementations. In contrast, Asian manufacturers, particularly in Japan and China, emphasize vehicle-to-everything (V2X) communication for collision avoidance, integrating it into systems like Toyota's Safety Sense and Honda Sensing to enable real-time data sharing with infrastructure and other road users, reducing intersection and pedestrian risks in high-density environments.

Assessment and Economics

Safety Ratings and Programs

The program evaluates collision avoidance systems as part of its Safety Assist category, which contributes up to 35% of the overall 5-star rating, with specific scores for Autonomous Emergency Braking (AEB) systems targeting vehicles, pedestrians, and cyclists. In its 2023 protocols, Euro NCAP enhanced requirements for AEB performance in complex urban scenarios, including nighttime pedestrian detection and cyclist avoidance, mandating advanced capabilities for top ratings. That year, 14 out of 17 tested models achieved 5 stars, reflecting widespread adoption of effective AEB technologies among European manufacturers. In , the (IIHS) awards Top Safety Pick and Top Safety Pick+ designations, requiring "superior" or "advanced" ratings in front crash prevention tests that assess AEB effectiveness against vehicles and s. Updates in the 2020s, including 2023 and 2024 criteria, intensified focus on partial automation features like pedestrian AEB in low-light conditions and scenarios, reducing qualifiers to 48 models in 2023 due to stricter standards. Complementing this, the (NHTSA) integrates crash avoidance into its 5-Star Safety Ratings, with 2024 updates expanding evaluations of partial automation systems such as AEB and lane-keeping assistance, including requirements for Monroney label disclosures of these ratings starting in 2027. Regional programs like Latin NCAP and mirror these approaches with 5-star systems that score AEB and pedestrian detection in their Safety Assist categories. Latin NCAP's protocols emphasize AEB for urban and highway scenarios, awarding points for collision mitigation effectiveness, as seen in models achieving up to 90% in this area. NCAP's 2021-2025 protocol allocates up to 21 points for AEB variants, including city and inter-urban tests, with pedestrian AEB integration planned based on ongoing studies, enabling recent models to secure 5-star overall ratings through strong avoidance performance. Global harmonization efforts, coordinated through organizations like and the United Nations Economic Commission for Europe (UNECE), aim to align AEB testing protocols across programs to facilitate consistent evaluations and encourage . These initiatives focus on standardizing metrics for partial automation and vulnerable road user detection to support worldwide safety improvements. Safety ratings from these programs correlate strongly with vehicle sales, as higher-rated models capture over 90% of the market in regions like , influencing consumer preferences and prompting manufacturers to prioritize collision avoidance enhancements. For instance, low-rated models post-2022 have driven updates, with subsequent assessments showing improved AEB scores leading to elevated star ratings in programs like and IIHS.

Availability in Vehicles

Collision avoidance systems, particularly automatic emergency braking (AEB) and forward collision warning (FCW), have become increasingly standard in light vehicles worldwide. By 2025, approximately 90% of new passenger cars and light trucks under 10,000 pounds gross vehicle weight rating in the United States are equipped with AEB as a standard feature, driven by voluntary commitments from automakers and impending federal mandates set for full compliance by September 2029. In , the General Safety Regulation (GSR) has made advanced emergency braking systems mandatory for all new light vehicles since July 2022, achieving near-universal availability. Notable examples include the , which has offered a full suite of collision avoidance features—including AEB, FCW, and pedestrian detection—as standard equipment since the 2018 model year. For heavy and commercial vehicles, such as trucks and buses, adoption has accelerated due to regulatory pressures. In the , the GSR requires AEB and other collision mitigation systems on all newly registered heavy goods vehicles over 3.5 tons since July 2024, ensuring comprehensive coverage for new models. , while a final rule for mandatory AEB on Class 7 and 8 trucks was proposed in 2023 under the , with the final rule issued in 2025, many manufacturers have preemptively integrated these systems, and requirements are phased in starting in 2027 for Class 7 and 8 trucks, with full compliance by 2028 for vehicles over 10,000 pounds. The , a leading Class 8 model, has featured the Assurance suite—including ABA6 advanced braking and collision avoidance—as an available or standard option since the 2018 , with enhanced sensor integration in 2025 variants. Trends in availability highlight a shift toward , particularly in premium and (EV) segments. While basic AEB remains optional in some entry-level (ICE) models, advanced packages combining lane-keeping assist and pedestrian detection are standard on over 80% of mid-range and higher trims across major brands by 2025. EVs demonstrate higher integration rates, with models like the Tesla lineup and series offering comprehensive collision avoidance suites as standard since their 2020 introductions, compared to ICE counterparts where such features often require additional packages costing $1,000–$3,000. This disparity stems from EVs' native sensor architectures, which facilitate over-the-air updates and bundled ADAS deployment. Globally, availability is highest in developed markets like and , where regulations mandate AEB on nearly all new vehicles since 2022 and 2021, respectively, achieving penetration rates exceeding 95% for light vehicles by 2025. In contrast, developing markets such as those in and lag behind, with estimates indicating around 50% adoption in new light vehicles by 2025 due to cost barriers and slower regulatory implementation.

Cost and Market Adoption

Collision avoidance systems, as a key subset of advanced driver assistance systems (ADAS), involve significant component costs that influence their integration into vehicles. Individual sensors, such as cameras and radars essential for detection, typically range from $850 to $2,050 per unit, while more advanced units for enhanced perception can exceed $500 per unit in automotive-grade applications. Full system add-ons, including forward collision warning and automatic emergency braking, are priced between $1,000 and $3,000 for aftermarket installations in 2025, though integration during manufacturing can increase vehicle costs by $1,500 or more. These expenses stem from the complexity of and calibration requirements. The global ADAS market, encompassing collision avoidance technologies, reached approximately $38.91 billion in 2025, reflecting robust growth driven by increasing vehicle production and demands. rates in new have surged from around 20% in 2015 to over 70% by 2025 for basic features like emergency braking, with many advanced functions now exceeding 50% penetration in major markets. This expansion is supported by 359.8 million ADAS-equipped units shipped globally in 2025. However, barriers persist, particularly the high upfront costs of , which remain a deterrent for widespread integration in budget models despite overall sensor price declines. Insurance incentives are playing a pivotal role in overcoming these barriers, with many providers offering premium discounts of 10% or more for vehicles equipped with collision avoidance systems due to their proven reduction in claim frequency. Projections indicate further cost reductions through , as sensor manufacturing volumes rise and competition intensifies, potentially halving prices by 2030. The return on (ROI) for fleets and consumers is evident in lowered insurance costs and fewer accidents, with ADAS linked to 1% annual decreases in overall auto insurance loss trends, translating to 10-20% savings on premiums over time for equipped vehicles. Regulatory frameworks briefly referenced in global standards further accelerate this uptake by mandating certain features.

Applications Beyond Automotive

Aviation Systems

In aviation, collision avoidance systems are essential for preventing mid-air collisions in three-dimensional airspace, operating independently of to enhance pilot and provide automated guidance. The primary system is the (TCAS), known internationally as the (ACAS), which interrogates nearby aircraft transponders to detect potential threats and issue advisories. TCAS II, the version mandated by the (FAA) since December 30, 1993, for all U.S.-registered commercial aircraft with more than 30 passenger seats, functions by calculating the closest point of approach based on relative positions, altitudes, and velocities. TCAS operates through two main alert levels: Traffic Advisories (TAs), which prompt pilots to visually acquire nearby traffic, and Resolution Advisories (RAs), which provide specific escape maneuvers to maintain safe separation. RAs are altitude-based, directing vertical maneuvers such as "Climb" or "Descend" to achieve a minimum vertical separation of 1,000 feet between aircraft, with coordinated advisories exchanged between equipped planes to avoid opposing actions. For instance, initial corrective RAs require a climb or descent rate of approximately 1,500 feet per minute (fpm), while strengthened "Increase Climb" or "Increase Descent" RAs demand rates up to 2,500 fpm to resolve imminent threats within seconds. These advisories prioritize mid-air collision prevention by focusing on threats within a protected vertical and horizontal range, typically up to 40 nautical miles horizontally and 10,000 feet vertically, though effectiveness diminishes against non-transponder-equipped aircraft. Advancements in aviation collision avoidance have integrated Automatic Dependent Surveillance-Broadcast (ADS-B) with TCAS following the FAA's 2020 mandate, which requires ADS-B Out for operations in most to broadcast precise position data. ADS-B In enhances TCAS by providing pilots with graphical displays of surrounding traffic, including ground tracks and relative velocities, on a single interface, thereby improving awareness beyond TCAS's transponder-limited surveillance. Ongoing development of ACAS X, the next-generation system, aims to accommodate higher traffic densities and unmanned aircraft systems (UAS) through variants like ACAS Xa for manned aircraft and ACAS Xu for UAS integration, with testing and certification progressing as of 2025 to support beyond-visual-line-of-sight operations. For unmanned aircraft systems (UAS), or drones, the FAA's Alliance for of UAS through Excellence (ASSURE) program has developed detect-and-avoid (DAA) standards tailored to beyond-visual-line-of-sight operations, using sensors like and electro-optical systems to replicate "see-and-avoid" functions and ensure safe integration with manned traffic. These DAA systems must meet performance criteria equivalent to human pilots, with ongoing certification efforts focusing on well-clear volumes and avoidance maneuvers. The effectiveness of TCAS has been demonstrated by a substantial reduction in near mid-air collisions (NMACs) among equipped aircraft, with FAA analyses indicating that TCAS II lowers the probability of critical NMACs by approximately 58% compared to pre-implementation levels in scenarios with Mode C transponder equipage. In , where TCAS-equipped commercial flights predominate, fatal mid-air collisions between such aircraft have approached zero since , attributed to timely RA compliance that resolves over 90% of potential conflicts without pilot intervention beyond following the advisory. However, challenges persist in (VFR) conditions, where aircraft often lack transponders, limiting TCAS surveillance and increasing reliance on pilot vigilance for collision avoidance.

Maritime Navigation Aids

Collision avoidance systems in maritime navigation rely on a combination of radar-based technologies and automated tracking to detect and respond to potential hazards at sea, distinguishing themselves from land-based systems through the emphasis on long-range detection in open waters and compliance with international waterway rules. Key technologies include Automatic Radar Plotting Aids (), which emerged in the 1960s but became mandatory under the International Convention for the Safety of Life at Sea (SOLAS) in the 1990s for ships over 10,000 , automating the tracking of up to 20-40 targets to assess collision risks by calculating relative motion vectors and closest points of approach. Complementing ARPA, the Automatic Identification System (AIS), mandated by SOLAS from 2002 for vessels over 300 on international voyages, enables real-time vessel tracking by broadcasting position, speed, course, and identity via VHF radio, enhancing for collision avoidance without replacing radar. These systems integrate with the International Regulations for Preventing Collisions at Sea (COLREGs), adopted by the (IMO) in 1972, by incorporating rule-based maneuvers such as stand-on and give-way logic to determine vessel responsibilities in crossing, head-on, or scenarios. In modern autopilots and integrated bridge systems, this integration allows for automatic obstacle avoidance, where algorithms evaluate COLREGs compliance before suggesting course alterations, such as a 30-degree turn to starboard for give-way vessels under Rule 15. Advancements in the 2020s have introduced (AI) for dynamic routing, enabling predictive path planning that adapts to real-time environmental data like currents and traffic density while adhering to COLREGs, as demonstrated in models that simulate multi-vessel interactions for safer trajectories. Paralleling this, the IMO's e-Navigation initiative, launched in 2006 and ongoing through strategic implementation plans, promotes harmonized digital systems for enhanced collision avoidance, including standardized data exchange for route optimization and risk assessment across global fleets. The effectiveness of these systems is evident in post-AIS implementation data from 2002 onward, with studies showing a risk reduction of approximately 55% in collision probabilities by 2008 due to improved tracking in high-traffic areas, independent of watchkeeping practices. However, limitations persist in congested ports, where high vessel density overwhelms tracking capacities, and in fog, where reduced visibility hampers radar and AIS reliability, often requiring human override and adherence to COLREGs Rule 19 for safe speeds and sound signals. Sensor fusion, combining ARPA and AIS data, briefly addresses these by providing a unified view but cannot fully mitigate environmental constraints.

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