Advanced driver assistance

Driverless cars aren't here yet. More vehicles are incorporating a degree of automation with such technologies as adaptive cruise control and lane centering, but the driver will continue to share driving responsibilities for the foreseeable future. In theory, fully automated driving could eliminate the vast majority of crashes, but that level of automation won't be parked in your driveway anytime soon.

Advanced crash avoidance features are becoming widespread. Many of today's vehicles have technologies that monitor driver input and the environment around the vehicle and warn the driver when they detect the possibility of a collision. They may automatically brake or steer the vehicle if the driver does not act to avoid the collision.

Some driver assistance technologies are reducing crashes. Front crash prevention systems have been shown to reduce front-to-rear crashes. Lane departure warning, blind spot detection and rear crash prevention also show real-world benefits.

• Real-world benefits of crash avoidance technologies: summary of IIHS/HLDI findings

Front crash prevention systems use various types of sensors, such as cameras, radar, or light detection and ranging (LIDAR), to detect when the vehicle is getting too close to one in front of it. The systems generally issue a warning and precharge the brakes to maximize their effect if the driver brakes. Most also brake the vehicle if the driver doesn't respond.

In some cases, especially on earlier models, automatic braking is activated without a preliminary warning.

Front crash prevention is becoming more universal and its capabilities more consistent across brands, thanks to a voluntary commitment by 20 automakers, representing 99 percent of U.S. light vehicle sales, to make the technology standard by 2022. The commitment, brokered by IIHS and the National Highway Traffic Safety Administration (NHTSA), calls for vehicles to have systems with both a forward collision warning component that meets NHTSA criteria and automatic braking that achieves certain minimum speed reductions in IIHS track tests.

Some front crash prevention systems can recognize pedestrians, cyclists and animals. These systems use advanced algorithms coupled with sensors and cameras to spot nonmotorists who are in or about to enter the vehicle's path.

Vehicles equipped with front crash prevention are much less likely to rear-end other vehicles than the same models without the technology (Fildes et al., 2015; Isaksson-Hellman & Lindman, 2016;  Cicchino, 2017). An Institute study found that systems with forward collision warning and automatic braking cut rear-end crashes in half, while forward collision warning alone reduces them by 27 percent. The autobrake systems also greatly reduce rear-end crashes involving injury.

HLDI conducted similar studies comparing insurance claim rates for vehicles equipped with front crash prevention with claim rates for the same models without the technology. Vehicles equipped with these systems consistently show lower rates of claims for damage to other vehicles and for injuries to people in other vehicles. (HLDI, 2018)

Even if a front crash prevention system doesn't avoid a crash altogether, it may still reduce the impact speed, thereby making a crash less severe.

To show why reducing speed is important, IIHS conducted two demonstration crash tests at different speeds. In each test, a 2013 Mercedes-Benz C-Class ran into the back of a stationary 2012 Chevrolet Malibu. The tests illustrated what happens in a 25 mph crash when the striking vehicle doesn't have autobrake, compared with what happens when the speed is reduced by 13 mph, the amount by which the C-Class's autobrake system reduced the impact speed in IIHS track testing. Damage in the higher speed crash test was about $28,000. The Malibu was a complete loss. Lowering the speed to 12 mph trimmed the damage to $5,700 (IIHS, 2013).

A similar speed reduction in a higher speed crash could significantly reduce injury risk, as well as vehicle damage (Kraft et al., 2009).

Front crash prevention systems with automatic braking have resulted in bigger reductions in rear-end crashes with injuries than in rear-end crashes of all severities, which suggests that these systems are preventing injuries in some rear-end crashes that aren't avoided (Cicchino, 2017).

One front crash prevention system that recognizes pedestrians appears to reduce crashes with pedestrians. Subaru's EyeSight system with pedestrian detection cut the rate of likely pedestrian-related insurance claims by 35 percent, compared with the same vehicles without the system, HLDI found (HLDI, 2018).

IIHS has rated front crash prevention systems since 2013 and began rating pedestrian detection systems in 2019.

These systems use cameras to track the vehicle's position within the lane, alerting the driver if the vehicle is in danger of inadvertently straying across lane markings when the turn signal is not activated. Some systems use haptic warnings, such as steering wheel or seat vibration, while others use audible and/or visual warnings. Some systems cause the vehicle to actively resist moving out of the lane or help direct the vehicle back into the lane through light braking or minor steering adjustments.

Lane departure warning has not brought down insurance claim rates but has reduced rates of single-vehicle, sideswipe, and head-on crashes reported to the police (HLDI, 2018; Cicchino, 2018; Sternlund et al., 2017).

This feature uses sensors to monitor the side of the vehicle for vehicles approaching blind spots. In many systems, a visual alert appears on or near the side mirrors if a vehicle is detected. An audible alert may activate if the driver signals a turn and there is a vehicle in the blind spot. Some systems also may activate the brake or steering controls to keep the vehicle in its lane.

Blind spot detection has been shown to reduce lane-change crashes by 14 percent (Cicchino, 2018). HLDI research has also found that blind spot detection lowers rates of insurance claims covering damage to other vehicles (HLDI, 2018).

There are many different technologies designed to help drivers back up safely. Rearview cameras display what is behind the vehicle, projecting a much larger field than is visible in mirrors or even by looking directly out the back windshield. Effective May 2018, rearview cameras are required on new vehicles in order to reduce backover crashes, in which young children are frequently the victims (Office of the Federal Register, 2014).

Some camera systems, as well as systems that use radar or ultrasonic sensors, warn the driver if there are objects in the way when the vehicle is in reverse. Systems with rear automatic braking automatically apply the brakes to keep the vehicle from backing into or over an object. A rear cross-traffic alert system detects vehicles approaching from either side that may cross the path of a backing vehicle, warns the driver, and may automatically brake to prevent a collision.

These rear crash prevention systems, particularly those with rear automatic braking, are preventing backing crashes reported to the police (Cicchino, 2017; Cicchino, 2019; Cicchino, 2019).

Rear parking sensors, which warn drivers of objects behind them, have reduced insurance claims but have had inconsistent effects on backing crashes reported to the police (HLDI, 2018; Cicchino, 2017; Cicchino, 2019).

IIHS has rated rear crash prevention systems since 2018.

In driving, automation involves using radar, camera, and other sensors to perform parts or all of the driving task on a sustained basis instead of the driver. One example is adaptive cruise control, which continually adjusts the vehicle's speed to maintain a set minimum following distance. Features such as automatic braking, which acts as a back-up if the human driver fails to brake, or blind spot detection, which provides additional information to the driver, aren't considered automation under this definition.

Driving automation is not limited to vehicles that drive themselves without human interaction but includes technologies that vary in technical capability.

The National Highway Traffic Safety Administration adopted definitions of different levels of automation developed by SAE International (SAE International, 2018). The levels of driving automation range from none, or Level 0, to full driving automation, or Level 5. The levels are differentiated by whether a human is required to monitor the driving environment and whether, if things go wrong, the human is expected to take control or the automated system can bring the vehicle safely to a stop.

Levels of driving automation

In vehicles in which only some parts of the driving task, such as steering, controlling speed or following distance, or braking, are automated (Levels 1-2), drivers are still expected to be actively engaged and to continuously monitor the driving environment. Adaptive cruise control and lane centering are each examples of Level 1 systems. When combined, as in Tesla's Autopilot software, they are Level 2.

In contrast, drivers aren't expected to be actively engaged when using automated systems that monitor the environment in addition to performing some or all parts of the driving task (levels 3-5). However, some of these systems may rely on the driver to intervene if something goes wrong (Level 3) while others may stop the vehicle safely if the driver is unable to take over (levels 4 and 5).

So far, all of this technology is constrained to specific road and environmental conditions, so drivers will be expected to bridge the gap until full driving automation is developed that can perform the entire driving task without driver input under all conditions (Level 5).

The potential injury and fatality reductions associated with driving automation are huge. An in-depth study of police-reported crashes occurring during 2005-07 where at least one vehicle was towed from the scene concluded that a driver's error or physical state led to 94 percent of the crashes (Singh, 2015). If automation can eliminate all crashes involving driver-related factors, then thousands of lives could be saved each year — but that's a big "if."

Much of the automation available in current vehicles, such as adaptive cruise control and lane centering, typically works only on higher speed roadways where crashes are relatively infrequent. Even if all interstate miles were logged by vehicles driven entirely by automation that did not crash, the maximum overall benefit would be 17 percent fewer crash deaths and 9 percent fewer crash injuries, based on an Institute estimate using 2014 crash data (IIHS, 2016).

More advanced forms of automation that operate more broadly could potentially prevent far more crashes, but it is still too early to tell if these technologies will live up to expectations.

Regulatory frameworks for testing and deploying self-driving cars are being developed in the United States and other countries. In the U.S., the National Highway Traffic Safety Administration has issued guidance to help state lawmakers address testing and deployment of automated vehicle technology and to encourage a consistent legislative approach nationwide.

In 2011, Nevada became the first state to enact legislation specifically permitting research and testing of vehicles with partial and full autonomy on public roads. Since then, most states have passed legislation or issued executive orders addressing driving automation. Some of the laws only authorize a study, define key terms or authorize funding, while others permit testing on public roads or authorize full deployment. In some states, testing on public roads has been allowed without any specific legislation or regulatory action.

Autonomous vehicle laws by state, in detail

Vehicles that drive themselves without human involvement will first be available as part of taxi and ride-sharing services. In August 2016, the world's first taxi service featuring high levels of driving automation debuted in Singapore (Watts, 2016). In 2016, Uber began allowing select customers in Pittsburgh to hail a vehicle that drives itself with a human supervisor, marking the first time the public could ride in this type of vehicle in the United States (Hawkins, 2017). Other companies have or soon plan on having automated vehicles provide taxi services in select locations or as delivery vehicles.

Regardless of when vehicles equipped with high levels of automation become available for purchase, it will be decades before most vehicles on the road drive themselves. It takes a long time for new vehicle features to penetrate the vehicle fleet. For example, electronic stability control was introduced in the United States in 1995 model year vehicles, but it was not until nearly 20 years later in the 2012 model year that it became standard in over 95 percent of new vehicle models (HLDI, 2016). Since most people don't drive brand new vehicles, it won't be before the 2030s that 95 percent of the vehicles on the road have ESC.

More recent crash avoidance technologies like front crash prevention and lane departure warning are not expected to be in nearly all registered vehicles on the nation's roadways until after 2040 (HLDI, 2018). It will be even longer before most registered vehicles in the U.S. are equipped with Level 2 automation that is just now becoming available in vehicles today.

Appropriate driver responses and acceptance of crash avoidance technologies are critical to their success. If drivers don't trust the systems or find them annoying or not useful, they may disable them. Similarly, if drivers experience warnings but don't understand them, are overwhelmed by them, or don't take an appropriate corrective action, then the systems will be ineffective.

Interpreting warnings from multiple systems may be confusing or even distracting for some drivers. Many drivers involved in lane departure crashes are asleep or otherwise incapacitated, which can limit their ability to respond to lane departure warning and lane-keeping support systems (Cicchino & Zuby, 2017;  Wiachek et al., 2017).

Partial automation may require additional training. Drivers need to know when automation is available, how to use it and how to take control when automation is no longer available or if it fails. Experimental studies have shown that drivers can lose sight of what automated systems are doing, fail to notice when something goes wrong, and have trouble taking control again (Endsley & Kiris, 1995; Gold et al., 2016; German Insurers Accident Research (UDV), 2016; Haslbeck & Hoermann, 2016; Lee & See, 2004; Merat et al., 2014; Wickens et al., 2004; Zeeb et al., 2016; Ruscio et al., 2015).

Systems need to be turned on to be effective. Observations at dealerships of seven automakers in 2016 found that front crash prevention systems were activated in 93 percent of the vehicles observed that arrived for service, and nearly 100 percent of the blind spot detection and rear-cross traffic alert systems were turned on (Reagan at al., 2018). Activation of lane departure warning and lane keeping-support systems were much lower at 52 percent. Lane departure systems that warned by vibration were more likely to be activated than those that beeped, and lane keeping assistance systems were more likely to be turned on than warning-only systems.

Automated systems also can't be effective unless they are used. IIHS research shows that driver acceptance of lane centering and adaptive cruise control systems can vary considerably by vehicle. Drivers report that systems that accelerate, decelerate and steer smoothly improve the experience (Kidd & Reagan, 2018).

One concern is that drivers might rely on crash avoidance systems too much and feel freer to look away from the road or take other risks. However, in IIHS surveys of owners of vehicles with various technologies, many owners reported safer driving habits with the systems — for example, using turn signals more often with lane departure warning (Braitman et al., 2010; Eichelberger & McCartt, 2014; Cicchino & McCartt, 2015; Eichelberger & McCartt, 2016). Fewer owners reported potentially unsafe behavior, such as waiting for an alert before braking or allowing the vehicle to brake for them at least some of the time.

An Institute naturalistic study monitored teens who drove vehicles with and without a suite of prototype collision avoidance systems, including forward collision warning and lane departure warning. When teens drove with the systems, turn signal use increased and lane drifts decreased, but the teens also followed other vehicles at close distances more often (Jermakian et al., 2017).

In addition to driver challenges, the technology itself can have limitations. For example, lane departure warning systems use sensors to register lane markings or the road edge, which may be problematic on roads that aren't well marked or are covered with snow. Sensors may not function well in low light or inclement weather. Some systems only work at certain speeds.

Crash avoidance technologies can also have the unintended consequence of increasing some crash types with reducing others. The Institute's research on the effects of front crash prevention on police-reported crashes found that vehicles with forward collision warning and automatic braking had a 20 percent higher rate of being rear-ended than vehicles without the systems (Cicchino, 2017). However, vehicles with forward collision warning alone or low-speed autobrake had lower rates of being rear-ended than vehicles without.

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, collectively known as connected vehicle technology, are prototype safety systems in which vehicles and roadway infrastructure communicate over a wireless network.

With V2V communication, vehicles transmit information regarding their actions to other vehicles. For example, in a long chain of vehicles, if the lead vehicle suddenly brakes, this information will be transmitted to every other vehicle in the chain so that the other drivers are alerted. It also could be possible for the trailing vehicles to automatically begin braking when the lead vehicle's signal is received.

With V2I communication, cars receive and transmit information to roadway infrastructure. For example, highway systems could monitor vehicle location within a lane. If the vehicle is detected drifting out of a lane, the system could alert the vehicle. In urban environments, traffic signals can alert vehicles of an impending light change so drivers can prepare to stop.

A 2013 pilot study in Ann Arbor, Mich., tested the functionality and reliability of these connected vehicle technologies (Bezzina & Sayer, 2015). The results indicate connected vehicle technologies are technically feasible and would reduce property-damage and injury crashes. However, there are some barriers to wide adoption, including privacy and security concerns, as well as technical aspects and performance requirements of the systems (Harding et al., 2014).

The U.S. Department of Transportation is funding additional pilot deployment sites to refine system and operational requirements and inform a comprehensive deployment plan (U.S. Department of Transportation, 2016). In January 2017, NHTSA issued a notice of proposed rulemaking that will require passenger vehicles to have the technology by 2023 and is considering comments received from the public (Office of the Federal Register, 2017). IIHS supports the proposal (IIHS, 2017).

NHTSA estimates that two V2V functions, intersection movement assist and left turn assist, together could potentially prevent up to nearly 600,000 crashes and about 1,300 fatalities annually when fully deployed through the light vehicle fleet (Office of the Federal Register, 2017). These functions warn drivers when they are at risk of crashing with another vehicle when entering or turning left at an intersection.