MTBF Model for AVs -- From Perception Errors to Vehicle-Level Failures

TL;DR

This paper introduces a comprehensive MTBF model linking perception errors to vehicle failures in AVs, aiding safety certification by estimating collision rates based on perception quality and mission profiles.

Contribution

The paper presents a scalable, generic model connecting perception errors to vehicle-level failures, enabling safety assessment and perception quality requirements for AVs.

Findings

Derived perception quality requirements for target MTBFs.

Established a link between perception errors and collision rates.

Provided a scalable framework for safety certification.

Abstract

The development of Automated Vehicles (AVs) is progressing quickly and the first robotaxi services are being deployed worldwide. However, to receive authority certification for mass deployment, manufactures need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time Between Failure (MTBF) models for AVs. In this paper, we will introduce such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, we are able to derive requirements for the perception quality based on the desired vehicle-level MTBF or vice versa to obtain an MTBF value given a certain mission profile and perception quality.