Title: Testing scenario generation and selection for autonomous vehicles using an integrated approach based on real-world accident data

Authors: Guozheng Song; Xiaopeng Li

Addresses: Sensor and Instrument Centre, Instrumentation Technology and Economy Institute, Beijing, China ' School of Management Science and Engineering, Nanjing University of Finance & Economics, Nanjing, Jiangsu, China

Abstract: The safety and reliability of Autonomous Vehicles (AVs) are a core concern, which should be validated before application. The critical testing scenarios extracted from historical accidents of AVs can help achieve the efficient safety and reliability testing of AVs. This paper presents an integrated approach that combines a data-driven method with a Bayesian Network (BN). The information including states, states' occurrence likelihoods and quantitative relationships of variables related to scenarios are learned from an AV accident database of California Department of Motor Vehicles (DMV), which is applied to establish a BN. Then, the scenarios are generated and assessed with the BN and a severity matrix. The testing scenarios are selected based on their weighted consequence severity and risk. In this way, this work achieved critical testing scenarios for the Automated Driving Systems (ADSs) and Perception Systems (PSs) of AVs based on the AV accident database.

Keywords: autonomous vehicle; Bayesian network; testing scenario generation and selection.

DOI: 10.1504/IJRS.2025.149317

International Journal of Reliability and Safety, 2025 Vol.19 No.4, pp.356 - 379

Received: 19 Jun 2024
Accepted: 09 Sep 2024
Published online: 24 Oct 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article