Risk Estimation for Automated Driving

2026-01-21

AI Summary

This work proposes a general and precise risk assessment framework for autonomous driving that overcomes the limitations of existing approaches, which often rely on empirical modeling or strong approximations and struggle to jointly account for uncertainty in traffic participant state estimation and the severity of potential collisions. The framework uniquely integrates high-fidelity Gaussian-based collision probability estimation with a configurable collision severity function, enabling tailored severity modeling for different collision types—such as head-on or side impacts. By avoiding traditional simplifying assumptions, the method achieves efficient, scalable, and unified risk quantification suitable for real-time motion planning. Open-source implementation demonstrates its effectiveness and practicality in real-world autonomous driving systems.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

Safety is a central requirement for automated vehicles. As such, the assessment of risk in automated driving is key in supporting both motion planning technologies and safety evaluation. In automated driving, risk is characterized by two aspects. The first aspect is the uncertainty on the state estimates of other road participants by an automated vehicle. The second aspect is the severity of a collision event with said traffic participants. Here, the uncertainty aspect typically causes the risk to be non-zero for near-collision events. This makes risk particularly useful for automated vehicle motion planning. Namely, constraining or minimizing risk naturally navigates the automated vehicle around traffic participants while keeping a safety distance based on the level of uncertainty and the potential severity of the impending collision. Existing approaches to calculate the risk either resort to empirical modeling or severe approximations, and, hence, lack generalizability and accuracy. In this paper, we combine recent advances in collision probability estimation with the concept of collision severity to develop a general method for accurate risk estimation. The proposed method allows us to assign individual severity functions for different collision constellations, such as, e.g., frontal or side collisions. Furthermore, we show that the proposed approach is computationally efficient, which is beneficial, e.g., in real-time motion planning applications. The programming code for an exemplary implementation of Gaussian uncertainties is also provided.

Problem

• risk estimation
• automated driving
• collision probability
• collision severity
• motion planning

Innovation

• risk estimation
• collision probability
• collision severity
• automated driving
• motion planning