Simulation-based evaluation of the impact of perception sensor configuration on integrated safety of automated vehicle

Kurzfassung

Highly automated vehicles (AVs) (the SAE levels 4 - 5 [1]) rely on perception sensor information, including LiDARs, radars, and cameras. One of the most important factors to consider when equipping these sensors on AVs is the safety integrity level these sensors can provide for operation. The required level of safety in the area of safety-critical applications is determined from the target level of injury and fatality risk that should be assured [see e.g. 2]. This concept is being introduced in the field of automotive industries when developing the safety requirement of AVs [3]. In the automotive industry, safety analysis can be done by considering both active and passive safety (referred to as integrated safety). An active safety system targets to sense and prevent a possible collision, and a passive safety system targets to minimize the crash impact after collision. This study investigates the impact of perception sensor configurations on the safety integrity levels of automated vehicles by conducting simulations. At first, a Euro NCAP scenario is chosen as an example scenario for the simulation. Secondly, simulations are conducted using the Ansys driving simulator to simulate autonomous driving operations under different conditions. As inputs to the simulator, the sensor’s perception capability (maximum range and field of view), as well as initial velocity of ego and other vehicles and allowable deceleration, is chosen. As the output parameter, the impact speed in a collision is generated which has a direct relationship to the risk of fatality. The risk of fatality can be computed as a function of the resulting impact speed based on the published relationship between two parameters [4]. Lastly, a relationship between the sensor configuration and the risk of fatality (or safety integrity level) is analyzed using the obtained dataset. This study provides criteria for equipping various sensors in highly automated vehicles to ensure a high level of integrated safety. [1] Society of Automotive Engineers (SAE), “SAE-J3016: Taxonomy and Definitions for terms Related to Driving Automation Systems for On-Road Motor Vehicles”, Sep 2016. [2] ICAO, “ICAO AWOP/15 Report,” 15th meeting, Montreal 26 September-12 October 1994. [3] T. G. R. Reid, S. E. Houts, R. Cammarata, G. Mills, S. Agarwal, A. Vora, and G. Pandey, “Localization Requirements for Autonomous Vehicles,” SAE International Journal of CAV, vol. 2, no. 3, pp. 173-190, 2019. [4] Richards, DC. “Relationship between Speed and Risk of Fatal Injury: Pedestrians and Car Occupants,” Transport Research Laboratory, Department of Transport, London, UK: 2010.