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Cited by 29Traffic control and managementTraffic and Road SafetyAutonomous Vehicle Technology and Safety

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

Open access
Xu, Zheng, Wang, Xiaomeng, Wang, Xuesong, Zheng, Nan·2025·Accident Analysis & Prevention
DOI 10.1016/j.aap.2025.108011
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Abstract

• Proposed a co-simulation framework for systematic safety evaluations of CAVs in large-scale networks. • Observed that safety benefits do not consistently correlate linearly with increasing CAV penetration rates. • Identified optimal stability between automated and human-driven vehicles at 70% CAV penetration. • Found that roundabouts and signalized intersections account for over 70% of conflicts involving CAVs. 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 km 2 ) 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.

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Crossref · 10.1016/j.aap.2025.108011

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Research works printed but not linked: 1

  1. Wiedemann, 1992, Microscopic traffic simulation: the simulation system MISSION, background and actual state, Project ICARUS (V1052) Final Report, 2, 1

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Authors
Xu, Z., Wang, X., Wang, X., & Zheng, N.
Affiliations
  • Monash University (AU)
  • Tongji University (CN)
Corresponding author
Xu, Zheng
ORCID
Venue
Accident Analysis & Prevention
Publisher
Elsevier BV
Published
2025-06
Volume
215
Pages
108011
ISSN
0001-4575 (ISSN-L)
DOI
10.1016/j.aap.2025.108011
OpenAlex
W4408558711
Semantic Scholar
26f715d43c3428ddb4061e9699b8987ce38f38ff
Cited by
29
References
54
Citations by year
2026: 17 · 2025: 12
FWCI
10.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Accident Analysis & Prevention (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
connected and autonomous vehicles, CAV penetration rate, co-simulation, CARLA, microscopic traffic simulation, deep reinforcement learning, Bayesian hierarchical model, intersection conflicts, roundabouts, signalized intersections
MeSH terms
Accidents, Traffic, Automation, Automobile Driving, Automobiles, Bayes Theorem, Computer Simulation, Humans, Safety

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