# An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets

arXiv: https://arxiv.org/abs/1805.02114 · HTML: https://mlsanity.com/paper/1805.02114/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2018.

## Built on by (most cited first, 4 of 4)

- [Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems](https://mlsanity.com/paper/2111.02204/) (2021)
- [Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments](https://mlsanity.com/paper/2407.15839/) (2024)
- [Controllable risk scenario generation from human crash data for autonomous vehicle testing](https://mlsanity.com/paper/2512.07874/) (2025)
- [A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies](https://mlsanity.com/paper/2609.00718/) (2026)

## Abstract (arXiv)

Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads. However, due to rareness of the safety-critical cases and, effectively, unlimited number of possible traffic scenarios, these on-road testing efforts have been acknowledged as tedious, costly, and risky. In this study, we propose Accelerated De- ployment framework to safely and efficiently estimate the AVs performance on public streets. We showed that by appropriately addressing the gradual accuracy improvement and adaptively selecting meaningful and safe environment under which the AV is deployed, the proposed framework yield to highly accurate estimation with much faster evaluation time, and more importantly, lower deployment risk. Our findings provide an answer to the currently heated and active discussions on how to properly test AV performance on public roads so as to achieve safe, efficient, and statistically-reliable testing framework for AV technologies.