Risk Estimation for Automated Driving

TL;DR

This paper introduces a novel, accurate, and computationally efficient method for risk estimation in automated driving, combining collision probability and severity to improve safety assessment and motion planning.

Contribution

It develops a general risk estimation approach that integrates collision probability with severity functions, enhancing accuracy and real-time applicability over existing methods.

Findings

The method accurately estimates collision risk considering uncertainty and severity.

It allows for different severity functions for various collision types.

The approach is computationally efficient for real-time use.

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.