RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems
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Abstract (arXiv)
The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety impact evaluations, whereby the observational data from both an ADS and a representative benchmark fleet are compared to quantify safety performance. In January 2024, a working group of experts across academia, insurance, and industry came together in Washington, DC to discuss the current and future challenges in performing such evaluations. A subset of this working group then met, virtually, on multiple occasions to produce this paper. This paper presents the RAVE (Retrospective Automated Vehicle Evaluation) checklist, a set of fifteen recommendations for performing and evaluating retrospective ADS performance comparisons. The recommendations are centered around the concepts of (1) quality and validity, (2) transparency, and (3) interpretation. Over time, it is anticipated there will be a large and varied body of work evaluating the observed performance of these ADS fleets. Establishing and promoting good scientific practices benefits the work of stakeholders, many of whom may not be subject matter experts. This working group's intentions are to: i) strengthen individual research studies and ii) make the at-large community more informed on how to evaluate this collective body of work.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Aug 2024.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Predicting NCAP Safety Ratings: An Analysis of Vehicle Characteristics and ADAS Features Using Machine Learningcited by 0
- 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Drivingcited by 0
- 2026Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shiftcited by 0
- 2026When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systemscited by 0
- 2026When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systems12 Sep 2026
- 2026Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift16 Jun 2026
- 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving18 Mar 2026
- 2025Predicting NCAP Safety Ratings: An Analysis of Vehicle Characteristics and ADAS Features Using Machine Learning2 Sep 2025