An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets
This addresses the challenge for automobile and mobility companies to safely and efficiently test autonomous vehicles on public streets, though it appears incremental as it builds on existing testing methods.
The study tackled the problem of testing pre-production autonomous vehicles on public roads, which is currently tedious, costly, and risky due to rare safety-critical cases and unlimited traffic scenarios. The proposed Accelerated Deployment framework achieved highly accurate estimation with much faster evaluation time and lower deployment risk.
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.