Avoidable collision controls under a connected and autonomous vehicles environment

Sangung Park
Seungjae Lee
Jooyoung Kim

 Because all travelers want an efficient and comfortable trip, many engineers and researchers related to automotive technologies have improved their technologies. Two important technologies, connected vehicle environment (CV environment) and autonomous vehicles(AV), are considered to satisfy two properties, which are called ADAS (Advanced Driver assistance system). CV communicates with other vehicles, vehicle management, and connection to other advanced technologies under radio communication environment and shows driver various information of road condition, accident warning, traffic congestion prediction, and nearby vehicle’s kinematic information. While, sensors of AV recognizes nearby information through innate various sensors, called sensor fusion and judge the next path.

However, these two technologies have their own weakness. CV shows too much information so drivers can’t embrace all information and have many difficulties of judging the driver’s behavior properly. Range of AV’s sensor is nearly the same as the range of vision so AV can’t perceive macroscopic traffic flows. To compensate the defect, automotive technologies tend to be the integrated technologies of connected environment and autonomous vehicles, called connected and autonomous vehicles(CAVs)

CAVs judge and drive themselves by using the vehicle and road information through the CAV environment and/or vehicle sensors. Among CAV technologies, this paper concentrate on two main technologies: Adaptive cruise control(ACC) and cooperative adaptive cruise control (CACC), which is related to collision controls. ACC and CACC keep the time headway between an ego vehicle and a following vehicle. Therefore, the complexity and diversity of the information obtained by CAVs have resulted in the combination of many sensitive components.

The combination of many sensitive components, which is related to reliability of technologies, causes CAVs malfunction. Malfunction of CAVs can normally affect vast harm of travelers so various safety standards are adopted to check the safety and minimize the malfunction. Many skeptics of CAVs raise a question about the reliability of CAVs. Therefore, CAV has to adopt its own safety standards. Automotive safety integrity level(ASIL) is a risk evaluation standard for only autonomous vehicles defined by the ISO 26262 – functional safety for road vehicles standard. ASIL is the combination of severity, exposure and controllability. This is a new adaptation of the safety integrity level (IEC 61508) for the automotive industry. ASIL is classified from A (Lowest safety) to D (Highest safety). ASIL D refers to the highest classification of initial hazard and to that standard’s most stringent level of safety measures to apply for avoiding an unreasonable residual risk.

This paper proposes theoretical methods to suggest proper time headway range by velocity to prevent collision controls. This paper formulates time-invariant headway and evaluates the proper headway range of CAV technologies using string stability theory. We simulate three available cases (No ADAS, ACC, and CACC cases) to investigate effects of the malfunction of CAV’s two main technologies (ACC and CACC). The contribution of this paper is to simulate proper headway range and time-invariant failure probability under CAV environment



ICTTP 2020

ICTTP, International Conference on Traffic and Transport Psychology, is held in Gothenburg, Sweden.



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