Safety benefit assessment of autonomous emergency braking and steering systems for the protection of cyclists and pedestrians based on a combination of computer simulation and real-world test results

TL;DR

This paper introduces a Bayesian inference framework combining simulation and real-world tests to evaluate the safety benefits of autonomous emergency braking and steering systems for protecting cyclists and pedestrians.

Contribution

It presents a novel Bayesian approach to quantify safety benefits by integrating simulation data with real-world test results for AEBSS systems.

Findings

Framework successfully combines simulation and real-world data.

Application to EU project PROSPECT demonstrates practical utility.

Potential for broad application in safety benefit assessments.

Abstract

Cyclists and pedestrians account for a significant share of fatalities and serious injuries in the road transport system. In order to protect them, advanced driver assistance systems are being developed and introduced to the market, including autonomous emergency braking and steering systems (AEBSS) that autonomously perform braking or an evasive manoeuvre by steering in case of a pending collision, in order to avoid the collision or mitigate its severity. This study proposes a new prospective framework for quantifying safety benefit of AEBSS for the protection of cyclists and pedestrians in terms of saved lives and reduction in the number of people suffering serious injuries. The core of the framework is a novel application of Bayesian inference in such a way that prior information from counterfactual simulation is updated with new observations from real-world testing of a prototype AEBSS. As an illustration of the method, the framework is applied for safety benefit assessment of the AEBSS developed in the European Union (EU) project PROSPECT. In this application of the framework, counterfactual simulation results based on the German In-Depth Accident Study Pre-Crash Matrix (GIDAS-PCM) data were combined with results from real-world tests on proving grounds. The proposed framework gives a systematic way for the combination of results from different sources and can be considered for understanding the real-world benefit of new AEBSS. Additionally, the Bayesian modelling approach used in this paper has a great potential to be used in a wide range of other research studies.