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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07758 |
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| _version_ | 1866916580843061248 |
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| author | Scanlon, John M. Teoh, Eric R. Kidd, David G. Kusano, Kristofer D. Bärgman, Jonas Chi-Johnston, Geoffrey Di Lillo, Luigi Favaro, Francesca Flannagan, Carol Liers, Henrik Lin, Bonnie Lindman, Magdalena McLaughlin, Shane Perez, Miguel Victor, Trent |
| author_facet | Scanlon, John M. Teoh, Eric R. Kidd, David G. Kusano, Kristofer D. Bärgman, Jonas Chi-Johnston, Geoffrey Di Lillo, Luigi Favaro, Francesca Flannagan, Carol Liers, Henrik Lin, Bonnie Lindman, Magdalena McLaughlin, Shane Perez, Miguel Victor, Trent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems Scanlon, John M. Teoh, Eric R. Kidd, David G. Kusano, Kristofer D. Bärgman, Jonas Chi-Johnston, Geoffrey Di Lillo, Luigi Favaro, Francesca Flannagan, Carol Liers, Henrik Lin, Bonnie Lindman, Magdalena McLaughlin, Shane Perez, Miguel Victor, Trent Robotics 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. |
| title | RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.07758 |