Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modeling

TL;DR

This paper explores innovative risk modeling techniques for autonomous driving that leverage big data, aiming to improve risk assessment and insurance pricing by aligning models more closely with real-world traffic safety performance.

Contribution

It introduces a novel risk modeling approach based on risk entropy, addressing technical challenges and enabling risk assessment and insurance pricing in simulated autonomous driving environments.

Findings

New risk model aligns better with real traffic safety data

Feasibility demonstrated for risk assessment in simulation

Potential for improved insurance pricing strategies

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.