# Planning Reliability Assurance Tests for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2312.00186 · HTML: https://mlsanity.com/paper/2312.00186/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing](https://mlsanity.com/paper/1908.06540/) (2019)
- [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)
- [Assurance for sample size determination in reliability demonstration testing](https://mlsanity.com/paper/1905.08659/) (2019)
- [Short-term Maintenance Planning of Autonomous Trucks for Minimizing Economic Risk](https://mlsanity.com/paper/2106.01871/) (2021)

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

- [On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2505.02274/) (2025)
- [Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability](https://mlsanity.com/paper/2502.12386/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) technology has become increasingly prevalent and transforms our everyday life. One important application of AI technology is the development of autonomous vehicles (AV). However, the reliability of an AV needs to be carefully demonstrated via an assurance test so that the product can be used with confidence in the field. To plan for an assurance test, one needs to determine how many AVs need to be tested for how many miles and the standard for passing the test. Existing research has made great efforts in developing reliability demonstration tests in the other fields of applications for product development and assessment. However, statistical methods have not been utilized in AV test planning. This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data. We explore the relationship between multiple criteria of interest in the context of planning AV reliability assurance tests. Specifically, we develop two test planning strategies based on homogeneous and non-homogeneous Poisson processes while balancing multiple objectives with the Pareto front approach. We also offer recommendations for practical use. The disengagement events data from the California Department of Motor Vehicles AV testing program is used to illustrate the proposed assurance test planning methods.