# Software Reliability Growth Models Predict Autonomous Vehicle Disengagement Events

arXiv: https://arxiv.org/abs/1812.08901 · HTML: https://mlsanity.com/paper/1812.08901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Statistical Perspectives on Reliability of Artificial Intelligence Systems](https://mlsanity.com/paper/2111.05391/) (2021)
- [Reliability Analysis of Artificial Intelligence Systems Using Recurrent Events Data from Autonomous Vehicles](https://mlsanity.com/paper/2102.01740/) (2021)

## Abstract (arXiv)

The acceptance of autonomous vehicles is dependent on the rigorous assessment of their safety. Furthermore, the commercial viability of AV programs depends on the ability to estimate the time and resources required to achieve desired safety levels. Naive approaches to estimating the reliability and safety levels of autonomous vehicles under development are will require infeasible amounts of testing of a static vehicle configuration. To permit both the estimation of current safety, and make predictions about the reliability of future systems, I propose the use of a standard tool for modelling the reliability of evolving software systems, software reliability growth models (SRGMs). Publicly available data from Californian public-road testing of two autonomous vehicle systems is modelled using two of the best-known SRGMs. The ability of the models to accurately estimate current reliability, as well as for current testing data to predict reliability in the future after additional testing, is evaluated. One of the models, the Musa-Okumoto model, appears to be a good estimator and a reasonable predictor.