The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey
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Abstract (arXiv)
Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV safety assessment. The simulation-based testing method is an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver, which is modeled by driver models as well. Therefore, driver models are essential for AV safety assessment from the current perspective. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models as applied to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV benchmarks is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, potential gaps in existing driver models are identified, which provide direction for future work. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2023.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
- 2025HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generationcited by 3
- 2026General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Mergingcited by 0
- 2026General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging11 Mar 2026
- 2025HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation19 Mar 2025
- 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review8 Feb 2024