Stochastic Modeling of Road Hazards on Intersections and their Effect on Safety of Autonomous Vehicles

TL;DR

This paper extends a probabilistic safety assessment model for autonomous vehicles to explicitly account for various operating conditions, enhancing the understanding of safety risks at intersections.

Contribution

It introduces an extension to previous models that explicitly incorporates multiple operating conditions within the safety assessment framework.

Findings

The extended model can quantify safety risks across different operating conditions.

It improves the accuracy of safety likelihood estimates for AVs at intersections.

The approach facilitates better safety assurance for diverse driving scenarios.

Abstract

Autonomous vehicles (AV) look set to become common on our roads within the next few years. However, to achieve the final breakthrough, not only functional progress is required, but also satisfactory safety assurance must be provided. Among those, a question demanding special attention is the need to assess and quantify the overall safety of an AV. Such an assessment must consider on the one hand the imperfections of the AV functionality and on the other hand its interaction with the environment. In a previous paper we presented a model-based approach to AV safety assessment in which we use a probabilistic model to describe road hazards together with the impact on AV safety of imperfect behavior of AV functions, such as safety monitors and perception systems. With this model, we are able to quantify the likelihood of the occurrence of a fatal accident, for a single operating condition. In this paper, we extend the approach and show how the model can deal explicitly with a set of different operating conditions defined in a given ODD.