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Automated vehicles
Published in Lawrence A. Klein, ITS Sensors and Architectures for Traffic Management and Connected Vehicles, 2017
Adaptive cruise control (a Level 1 driver assistance function) operates like traditional cruise control, with one difference. When the sensors detect traffic slowing ahead, the vehicle also slows down. When traffic clears, the vehicle resumes the set speed. A further refinement of this concept is automatic emergency braking (AEB), which applies the brakes for the driver. The systems use on-vehicle sensors such as radar, cameras, or lidars to detect an imminent crash, warn the driver, and apply the brakes if the driver does not take sufficient action quickly enough. In March of 2016, NHTSA and the Insurance Institute for Highway Safety (IIHS) announced that 20 automakers entered into a voluntary agreement to make AEB standard by September 1, 2022 on more than 99% of the U.S. auto market. Trucks with gross weights between 8501 and 10,000 pounds will mostly be equipped with AEB by September 1, 2025, 3 years after the first agreement begins [29].
Prediction based advanced emergency braking for vulnerable road users
Published in Johannes Edelmann, Manfred Plöchl, Peter E. Pfeffer, Advanced Vehicle Control AVEC’16, 2017
T. Kim, K. Park, K. Yi, K. Min
Autonomous emergency braking (AEB) helps drivers to avoid or mitigate a collision based on the information about traffic situation. The USA Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) and the Insurance Institute for Highway Safety (IIHS) announced that 20 automakers have agreed to make AEB standard by September 1st, 2022. Therefore, AEB is predicted to be available to more consumers more quickly than would be possible through the regulatory process.
Effects on crash risk of automatic emergency braking systems for pedestrians and bicyclists
Published in Traffic Injury Prevention, 2023
Anders Kullgren, Khabat Amin, Claes Tingvall
The first AEB system was presented in 2003 and aimed at reducing the risk of rear-end crashes at low speed (Kodaka et al. 2003). Since then, many AEB systems aimed at reducing other collision types have been introduced, such as AEB for collisions at higher speed into other vehicles or stationary objects, crossings, pedestrians, bicyclists, and large animals and rear AEB for crashes during reversing, some of which have been shown to be effective in reducing crashes (Rizzi et al. 2014; Fildes et al. 2015; Cicchino 2017; Ydenius et al. 2017; Decker et al. 2021). A recent U.S. study has also investigated the effectiveness of the AEB with detection of pedestrians (Cicchino 2022). However, so far, studies of the effectiveness of AEB with the detection of bicyclists have not been presented.
Effectiveness of front crash prevention systems in reducing large truck real-world crash rates
Published in Traffic Injury Prevention, 2021
AEB systems are more common in the passenger vehicle fleet. Although no federal mandate exists for AEB, 20 automakers representing 99% of the United States automobile market have agreed to make AEB standard on virtually all new passenger vehicles by September 1, 2022 (Insurance Institute for Highway Safety 2016). AEB has existed in large trucks, and its availability in the fleet has been increasing as well, with greater market penetration for larger fleets (Belzowski and Herter 2015). Suites of crash avoidance technologies that include AEB (e.g., Bendix Wingman, Wabco OnGuard) have become default equipment on at least one truck model from Volvo, Peterbilt, Freightliner, and Mack, and on all models by International (Truck Safety Coalition 2017).
Quantifying Vision Zero: Crash avoidance in rural and motorway accident scenarios by combination of ACC, AEB, and LKS projected to German accident occurrence
Published in Traffic Injury Prevention, 2019
Lukas Stark, Michael Düring, Stefan Schoenawa, Jan Enno Maschke, Cuong Manh Do
AEB is an event-triggered system enabling the car for emergency braking maneuvers without driver interaction. Potential collision objects in front of the vehicle are detected by radar and described by lateral and longitudinal object distance, relative velocity, and object type. Time-to-collision (TTC) is applied as a criterion for intervention. Referring to Winner et al. (2015), the TTC calculation in Eq. (2) uses the sensor signal’s object distance dx and relative velocity vrel,x, both in the longitudinal direction.