Advanced driver-assistance system

Advanced driver-assistance systems (ADAS) are technologies that assist drivers with the safe operation of a vehicle. Through a human-machine interface, ADAS increase car and road safety. ADAS use automated technology, such as sensors and cameras, to detect nearby obstacles or driver errors, and respond accordingly. ADAS can enable various levels of autonomous driving.
As most road crashes occur due to human error, ADAS are developed to automate, adapt, and enhance vehicle technology for safety and better driving. ADAS are proven to reduce road fatalities by minimizing human error. Safety features are designed to avoid crashes and collisions by offering technologies that alert the driver to problems, implementing safeguards, and taking control of the vehicle if necessary. ADAS may provide adaptive cruise control, assist in avoiding collisions, alert drivers to possible obstacles, warn of lane departure, assist in lane centering, incorporate satellite navigation, provide traffic warnings, provide navigational assistance through smartphones, automate lighting, or provide other features. According to the national crash database in the US, Forward Collision Prevention systems have the potential to reduce crashes by 29%. Similarly, Lane Keeping Assistance is shown to offer a reduction potential of 19%, while Blind Zone Detection could decrease crash incidents by 9%.
According to a 2021 research report from Canalys, approximately 33 percent of new vehicles sold in the United States, Europe, Japan, and China had ADAS. The firm also predicted that fifty percent of all automobiles on the road by the year 2030 would be ADAS-enabled.
Some groups advocate standardization of the name, such as Forward Collision Warning and Automatic Emergency Braking rather than Forward Collision Alert or Smart City Brake Support.
Such standardization is promoted by AAA, Consumer Reports, J.D. Power, National Safety Council, PAVE, and SAE International.
ADAS were first being used in the 1970s with the adoption of the anti-lock braking system. Early ADAS include electronic stability control, anti-lock brakes, blind spot information systems, lane departure warning, adaptive cruise control, and traction control. These systems can be affected by mechanical alignment adjustments or damage from a collision. This has led many manufacturers to require automatic resets for these systems after a mechanical alignment is performed.
The reliance on data that describes the outside environment of the vehicle, compared to internal data, differentiates ADAS from driver-assistance systems (DAS). ADAS rely on inputs from multiple data sources, including automotive imaging, LiDAR, radar, image processing, computer vision, and in-car networking. Additional inputs are possible from other sources separate from the primary vehicle platform, including other vehicles (vehicle-to-vehicle or V2V communication) and infrastructure (vehicle-to-infrastructure or V2I communication). Modern cars have ADAS integrated into their electronics; manufacturers can add these new features during the design process or after production via over-the-air (OTA) updates.
ADAS are considered real-time systems since they react quickly to multiple inputs and prioritize the incoming information to prevent crashes. The systems use preemptive priority scheduling to organize which task needs to be done first. The incorrect assignment of these priorities is what can cause more harm than good.
ADAS are categorized into different levels based on the amount of automation, and the scale provided by The Society of Automotive Engineers (SAE). ADAS can be divided into six levels. In level 0, ADAS cannot control the car and can only provide information for the driver to interpret on their own. Some ADAS that are considered level 0 are: parking sensors, surround-view, traffic sign recognition, lane departure warning, night vision, blind spot information system, rear-cross traffic alert, and forward-collision warning. Level 1 and 2 are very similar in that they both have the driver do most of the decision making. The difference is level 1 can take control over one functionality and level 2 can take control over multiple to aid the driver. ADAS that are considered level 1 are: adaptive cruise control, emergency brake assist, automatic emergency brake assist, lane-keeping, and lane centering. ADAS that are considered level 2 are: highway assist, autonomous obstacle avoidance, and autonomous parking. From level 3 to 5, the amount of control the vehicle has increases; level 5 being where the vehicle is fully autonomous. Some of these systems have not yet been fully embedded in commercial vehicles. For instance, highway chauffeur is a Level 3 system, and automated valet parking is a level 4 system, both of which are not in full commercial use in 2019. The levels can be roughly understood as Level 0 - no automation; Level 1 - hands on/shared control; Level 2 - hands off; Level 3 - eyes off; Level 4 - mind off, and Level 5 - steering wheel optional.
This list is not a comprehensive list of all of the ADAS. Instead, it provides information on critical examples of ADAS that have progressed and become more commonly available since 2015.
Ford and General Motors provide "hands-off, eyes-on" systems such as Blue Cruise and Super Cruise in North America. These systems allow drivers to take their hands off the steering wheel while the system is engaged. However, drivers must keep their eyes on the road and be ready to take immediate action at all times.
In Europe, in Q2 2018, 3% of sold passenger cars had level 2 autonomy driving features. In Europe, in Q2 2019, 325,000 passenger cars are sold with level 2 autonomy driving features, that is 8% of all new cars sold.
According to a 2021 research report from Canalys, approximately 33 percent of new vehicles sold in the United States, Europe, Japan, and China had ADAS features. The firm also predicted that fifty percent of all automobiles on the road by the year 2030 would be ADAS-enabled.
Major car brands with Level 2 features include Audi, BMW, Mercedes-Benz, Tesla, Volvo, Tata, Citroën, Ford, Hyundai, Kia, Mazda, Nissan, Peugeot, Mahindra and Subaru. Full Level 2 features are included with Full Self-Driving from Tesla, Pilot Assist from Volvo, OpenPilot from Comma.ai and ProPILOT Assist from Nissan.
Level 3 features are included in Drive Pilot from Mercedes-Benz.
On June 29, 2021, the National Highway Traffic Safety Administration (NHTSA), the branch of the United States Department of Transportation responsible for federal motor vehicle regulations, issued Standing General Order 2021-01 (SGO 2021-01), which required manufacturers of ADAS (Levels 1 or 2) and Automated Driving Systems (ADS) (Levels 3 through 5) to promptly report crashes that occurred when driver-assistance or automation systems were in use. SGO 2021-01 subsequently was amended on August 5, 2021. Under the amended SGO 2021-01, a crash involving ADS or Level 2 ADAS is reportable to the NHTSA if it meets the following criteria:
A severe crash is one that results in one or more of the following:
The incident report to the NHTSA must be made according to the following schedule:
SGO 2021-01 is in effect for three years, starting on June 29, 2021. After gathering data for almost a year (July 1, 2021 through May 15, 2022), the NHTSA released the initial set of data in June 2022 and stated they plan to update the data on a monthly basis. The data are subject to several caveats and limitations; for instance, manufacturers are not required to report the number of vehicles that have been built and equipped with ADS/ADAS, the number of vehicles operating with ADS/ADAS, or the total distance traveled with ADS/ADAS active, which would be helpful to normalize the incident report data.
According to the initial data covering July 2021 to May 15, 2022, ADS (Levels 3–5) from 25 different manufacturers were involved in 130 crashes, led by Waymo LLC (62), Transdev Alternative Services (34), Cruise LLC (23), General Motors (16), and Argo AI (10); because multiple manufacturers can report the same crash, the sum exceeds the total number of reportable incidents. Of the 130 crashes, 108 had no associated injuries reported; there was only one serious injury associated with the remaining crashes. The most commonly-reported damage location was the rear of the ADS-equipped vehicle.
Similarly, ADAS (Level 2) from 12 different manufacturers were involved in 367 crashes over the same period; 392 crashes were reported in total, but 25 either occurred before July 2021 or had no associated date. Reported incidents were led by Tesla (273), Honda (90), and Subaru (10). Of the 392 crashes, 98 included injury reporting; of the 98, 46 had no injuries reported, 5 resulted in serious injuries and 6 resulted in fatalities. The most commonly-reported damage location was the front of the ADAS-equipped vehicle.
According to PACTS, lack of full standardization might make the system have difficulty being understandable by the driver who might believe that the car behaves like another car while it does not.
ADAS might have many limitations, for instance a pre-collision system might have 12 pages to explain 23 exceptions where ADAS may operate when not needed and 30 exceptions where ADAS may not operate when a collision is likely.
Names for ADAS features are not standardized. For instance, adaptive cruise control is called Adaptive Cruise Control by Fiat, Ford, GM, VW, Volvo and Peugeot, but Intelligent Cruise Control by Nissan, Active Cruise Control by Citroen and BMW, and DISTRONIC by Mercedes. To help with standardization, SAE International has endorsed a series of recommendations for generic ADAS terminology for car manufacturers, that it created with Consumer Reports, the American Automobile Association, J.D. Power, and the National Safety Council.
Buttons and dashboard symbols change from car to car due to lack of standardization.
ADAS behavior might change from car to car, for instance ACC speed might be temporarily overridden in most cars, while some switch to standby after one minute.
The AV industry is growing exponentially, and according to a report by Market Research Future, the market is expected to hit over $65 billion by 2027. AV insurance and rising competition are expected to fuel that growth. Auto insurance for ADAS has directly affected the global economy, and many questions have arisen within the general public. ADAS allow autonomous vehicles to enable self-driving features, but there are associated risks with ADAS. AV companies and manufacturers are recommended to have insurance in the following areas in order to avoid any serious litigations. Depending on the level, ranging from 0 to 5, each car manufacturer would find it in its best interest to find the right combination of different insurances to best match their products. Note that this list is not exhaustive and may be constantly updated with more types of insurances and risks in the years to come.
With the technology embedded in autonomous vehicles, these self-driving cars are able to distribute data if a car crash occurs. This, in turn, will invigorate the claims administration and their operations. Fraud reduction will also disable any fraudulent staging of car crashes by recording the car's monitoring of every minute on the road. ADAS are expected to streamline the insurance industry and its economic efficiency with capable technology to fight off fraudulent human behavior. In September 2016, the NHTSA published the Federal Automated Vehicles Policy, which describes the U.S. Department of Transportation's policies related to highly automated vehicles (HAV) which range from vehicles with ADAS features to autonomous vehicles.
In March 2014, the US Department of Transportation's National Highway Traffic Safety Administration (NHTSA) announced that it will require all new vehicles under 10,000 pounds (4,500 kg) to have rear view cameras by May 2018. The rule was required by Congress as part of the Cameron Gulbransen Kids Transportation Safety Act of 2007. The Act is named after two-year-old Cameron Gulbransen. Cameron's father backed up his SUV over him, when he did not see the toddler in the family's driveway
The advancement of autonomous driving is accompanied by ethical concerns. The earliest moral issue associated with autonomous driving can be dated back to as early as the age of the trolleys. The trolley problem is one of the most well-known ethical issues. Introduced by English philosopher Philippa Foot in 1967, the trolley problem asks that under a situation which the trolley's brake does not work, and there are five people ahead of the trolley, the driver may go straight, killing the five persons ahead, or turn to the side track killing the one pedestrian, what should the driver do? Before the development of autonomous vehicles, the trolley problem remains an ethical dilemma between utilitarianism and deontological ethics. However, as the advancement in ADAS proceeds, the trolley problem becomes an issue that needs to be addressed by the programming of self-driving cars. The crashes that autonomous vehicles might face could be very similar to those depicted in the trolley problem. Although ADAS make vehicles generally safer than only human-driven cars, crashes are unavoidable. This raises questions such as “whose lives should be prioritized in the event of an inevitable crash?” Or “What should be the universal principle for these ‘crash-algorithms’?”
Many researchers have been working on ways to address the ethical concerns associated with ADAS. For instance, the artificial intelligence approach allows computers to learn human ethics by feeding them data regarding human actions. Such a method is useful when the rules cannot be articulated because the computer can learn and identify the ethical elements on its own without precisely programming whether an action is ethical. However, there are limitations to this approach. For example, many human actions are done out of self-preservation instincts, which is realistic but not ethical; feeding such data to the computer cannot guarantee that the computer captures the ideal behavior. Furthermore, the data fed to an artificial intelligence must be carefully selected to avoid producing undesired outcomes.
Another notable method is a three-phase approach proposed by Noah J. Goodall. This approach first necessitates a system established with the agreement of car manufacturers, transportation engineers, lawyers, and ethicists, and should be set transparently. The second phase is letting artificial intelligence learn human ethics while being bound by the system established in phase one. Lastly, the system should provide constant feedback that is understandable by humans.
In October 2023, Consumer Reports rated 17 "active driving assistance systems". Their criteria were:
Their ratings were:
In March 2024, the American Insurance Institute for Highway Safety (IIHS) reported its first "partial automation safeguard ratings". Their criteria were:
The ratings were (no system received a "good" rating):
Intelligent transport systems (ITS) highly resemble ADAS, but experts believe that ITS goes beyond automatic traffic to include any enterprise that safely transports humans. ITS is where the transportation technology is integrated with a city's infrastructure. This would then lead to a “smart city”. These systems promote active safety by increasing the efficiency of roads, possibly by adding 22.5% capacity on average, not the actual count. ADAS have aided in this increase in active safety, according to a study in 2008. ITS systems use a wide system of communication technology, including wireless technology and traditional technology, to enhance productivity.
Driver control assistance systems (DCAS) is the name of a draft ADAS regulation. It would allow hands-free driving with a possible risk of lack of attentiveness. Such DCAS regulation would allow system such as Tesla FSD in Europe. The UNECE driver control assistance systems regulation plan that DCAS shall be designed to ensure that the driver performs the driving task, that the driver's hands must remain on the wheel and that the system shall monitor the driver's visual engagement.