Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modeling

2021/09/15 by Jiamin Yu, Yu, Jiamin
Engineering · #60J20 #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Risk Management (q-fin.RM) #Traffic Prediction and Management Techniques #Traffic and Road Safety

paper · pdf · doi:10.48550/arxiv.2109.07211

openalex publication_date 2021/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

It has been for a long time to use big data of autonomous vehicles for perception, prediction, planning, and control of driving. Naturally, it is increasingly questioned why not using this big data for risk management and actuarial modeling. This article examines the emerging technical difficulties, new ideas, and methods of risk modeling under autonomous driving scenarios. Compared with the traditional risk model, the novel model is more consistent with the real road traffic and driving safety performance. More importantly, it provides technical feasibility for realizing risk assessment and car insurance pricing under a computer simulation environment.

Related

• Quantitative Risk Indices for Autonomous Vehicle Training Systems
• Quantifying the Individual Differences of Driver' Risk Perception with Just Four Interpretable Parameters
• A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness
• Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps
• Watch out for the risky actors: Assessing risk in dynamic environments for safe driving