Autonomous driving hazard scenario extraction and safety assessment based on crash reports and carla simulation

Xin Gu; Xinsheng Yin; Yameng Li; Xi Jin; Yunxuan Li

doi:10.1117/12.3024201

14 February 2024 Autonomous driving hazard scenario extraction and safety assessment based on crash reports and carla simulation

Xin Gu, Xinsheng Yin, Yameng Li, Xi Jin, Yunxuan Li

Author Affiliations +

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130180N (2024) https://doi.org/10.1117/12.3024201
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

To solve the traffic safety problems that arise during "mixed traffic" between autonomous and manual vehicles, this paper presents a harzard scenario simulation and safety assessment method based on autonomous driving crash reports. 197 crash reports of autonomous vehicles are selected from California Department of Motor Vehicles and used for autonomous driving hazard scenario extraction. Combining 37 pre-collision scenarios published by the National Highway Traffic Safety Administration, typical traffic safety scenarios are identified. A random forest model is used to identify key elements of typical autonomous vehicle accident scenarios. The CARLA microscopic traffic simulation platform is used to simulate two typical "mixed traffic" safety scenarios: Lead Vehicle Stopped and Following Vehicle Making a Maneuver. Three indicators - relative velocity, relative distance, and time-to-collision - are selected for constructing a safety evaluation suitable for autonomous vehicles. The research results show that in the Lead Vehicle Stopped scenario, when the following vehicle is 15 meters away from the front vehicle, the risk of collision is extremely high when the initial speed of the following vehicle is greater than 13.1 m/s. In the Following Vehicle Making a Maneuver scenario, when the initial speed of the following vehicle is greater than 12.5 m/s, the risk of collision with the front vehicle is also extremely high. The results can provide support for the safety evaluation of autonomous driving and the improvement of autonomous driving system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Gu, Xinsheng Yin, Yameng Li, Xi Jin, and Yunxuan Li "Autonomous driving hazard scenario extraction and safety assessment based on crash reports and carla simulation", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130180N (14 February 2024); https://doi.org/10.1117/12.3024201

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KEYWORDS

Autonomous vehicles

Safety

Autonomous driving

Unmanned vehicles

Lead

Roads

Random forests

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