A Quantitative Method to Determine What Collisions Are Reasonably Foreseeable and Preventable

TL;DR

This paper introduces a quantitative method to determine which collisions are reasonably foreseeable and preventable for Automated Driving Systems, aiding regulatory approval and safety assessments.

Contribution

It proposes a novel, quantifiable approach considering the Operational Design Domain to evaluate residual collision risks in automated vehicles.

Findings

Method effectively estimates foreseeable and preventable collisions.

Supports setting safety requirements for ADS development.

Enhances justification for design and testing decisions.

Abstract

The development of Automated Driving Systems (ADSs) has made significant progress in the last years. To enable the deployment of Automated Vehicles (AVs) equipped with such ADSs, regulations concerning the approval of these systems need to be established. In 2021, the World Forum for Harmonization of Vehicle Regulations has approved a new United Nations regulation concerning the approval of Automated Lane Keeping Systems (ALKSs). An important aspect of this regulation is that "the activated system shall not cause any collisions that are reasonably foreseeable and preventable." The phrasing of "reasonably foreseeable and preventable" might be subjected to different interpretations and, therefore, this might result in disagreements among AV developers and the authorities that are requested to approve AVs. The objective of this work is to propose a method for quantifying what is "reasonably foreseeable and preventable". The proposed method considers the Operational Design Domain (ODD) of the system and can be applied to any ODD. Having a quantitative method for determining what is reasonably foreseeable and preventable provides developers, authorities, and the users of ADSs a better understanding of the residual risks to be expected when deploying these systems in real traffic. Using our proposed method, we can estimate what collisions are reasonably foreseeable and preventable. This will help in setting requirements regarding the safety of ADSs and can lead to stronger justification for design decisions and test coverage for developing ADSs.