Safety validation for connected autonomous vehicles using large-scale testing tracks in high-fidelity simulation environment

Abstract

Public concern over the implementation of Connect Autonomous Vehicles (CAVs) remains a significant issue, and safety validation for CAVs remains a critical challenge due to the limitations of existing testing methods. While real-world testing is crucial, it can be expensive, time-consuming, and potentially impractical for evaluating the operation of CAV fleets. This paper presents a comprehensive co-simulation framework integrating the fully compiled CARLA with traffic microsimulation to establish a large-scale (20 × 20 km²) testing environment for systematic CAV safety validation. The framework encompasses three key components: 1) a high-fidelity testing environment featuring diverse road geometries and dynamic conditions including weather variations and realistic traffic flows; 2) an intelligent CAV function developed through deep reinforcement learning and enhanced with utility-based connectivity strategies; 3) A sophisticated safety measurement metric that utilizes surrogate safety assessments, integrating a multi-type Bayesian hierarchical model to comprehensively evaluate risk factors and incident probabilities. The case study assessed CAV penetration rates ranging from 0 % to 100 %, identifying an optimal safety performance at a 70 % penetration rate, which resulted in an 86.05 % reduction in accident rates compared to conventional driving scenarios. This optimal safety level was effectively achieved in rural and suburban areas, where the average conflict probability was 0.4. However, in transition zones that connect high-, medium-, and low-density areas, significant traffic conflicts persisted even at this optimal penetration rate, with a conflict probability exceeding 0.7. Key results highlight critical safety patterns under optimal conditions, revealing that roundabouts and signalized intersections account for over 70 % of conflicts involving CAVs. This work advances CAV safety validation by providing a more realistic, large-scale testing environment that compensates for real-world testing limitations and allows for comprehensive safety evaluations across diverse scenarios.