# Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing

## TL;DR

This paper explores how prior knowledge and software reliability models can improve safety assessments of autonomous vehicles, reducing the need for extensive road testing and providing better safety forecasts.

## Contribution

It introduces a new Conservative Bayesian Inference variant for AV safety assessment and applies SRGMs to real-world data to enhance forecast accuracy.

## Key findings

- Prior knowledge can significantly improve AV safety predictions.
- SRGMs, when calibrated, serve as effective tools for test planning.
- Recalibration enhances the reliability of safety forecasts.

## Abstract

There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe enough. From published quantitative safety and reliability assessments of AVs, we know that, given the goal of predicting very low rates of accidents, road testing alone requires infeasible numbers of miles to be driven. However, previous analyses do not consider any knowledge prior to road testing - knowledge which could bring substantial advantages if the AV design allows strong expectations of safety before road testing. We present the advantages of a new variant of Conservative Bayesian Inference (CBI), which uses prior knowledge while avoiding optimistic biases. We then study the trend of disengagements (take-overs by human drivers) by applying Software Reliability Growth Models (SRGMs) to data from Waymo's public road testing over 51 months, in view of the practice of software updates during this testing. Our approach is to not trust any specific SRGM, but to assess forecast accuracy and then improve forecasts. We show that, coupled with accuracy assessment and recalibration techniques, SRGMs could be a valuable test planning aid.