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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

ADAS Reliability against Weather Conditions: Quantification of Performance Robustness

Advanced Driving Assistance System (ADAS) technologies provide an additional safety layer besides human drivers. Continual evaluation of the safety of the dynamic driving task enables ADAS to initiate a corrective (e.g., automated braking) and/or a preventative (e.g., audio-visual alerts) action if and when an unsafe roadway event is detected. To provide situational awareness, these safety systems principally rely on the vehicle mounted sensors whose performance can be greatly affected by weather events such as strong sunlight, atmospheric precipitation (rain, snowfall, fog), etc. Correspondingly, this study was conducted to characterize the performance of ADAS features in different weather conditions. Automated emergency braking (AEB) was selected as a representative ADAS feature. Two vehicles under test (VUT) were equipped with perception sensors such as LiDAR, RGB camera, infrared camera, radar, inertial measurement unit, GNSS, etc. Relevance and prominent use of these sensors in pre-production and developmental driving automation systems are widely reported in the literature. In addition, the data available through the OBD-II port of the VUTs was also recorded with temporal correspondence with the external sensors. Although weather related tests involving automotive systems have been traditionally performed in weather chambers, adoption of these test protocols for ADAS testing can be challenging. Because testing of ADAS must be performed dynamically, a runway of several hundred meters is necessary, and typical weather chambers cannot accommodate this requirement. Alternatively, this study utilized naturally occurring weather events to record AEB performance. For the purpose of this study, AEB tests performed under optimal weather conditions (sunny and bright) constituted the baseline performance. The same tests were performed in a number of different weather and roadway conditions; e.g., day/night, snow covered asphalt, persistent snowfall, overcast, rainfall etc. A number of metrics resulting from the test data analysis were used to quantify AEB performance in adverse weather conditions. These include distance of the test target when AEB system detected an imminent collision in different weather conditions, distance of the test target when AEB initiated an automated braking action in different road surface conditions (dry/wet asphalt vs snow covered asphalt), and whether AEB was successful in stopping a collision from happening in the test scenarios. These metrics helped to identify the failure modes of AEB in adverse weather conditions. It should be noted that quantification of ADAS performance robustness against adverse weather conditions is closely related to quantification of operational design domain (ODD), which is an emerging topic in driving automation systems literature. Nonetheless, observations and inferences made from this study will be used to design more comprehensive and elaborate test protocols for ADAS that are expected to improve in system capacity and ODD in near future.

Language

  • English

Media Info

  • Media Type: Web
  • Features: Figures; Photos; References; Tables;
  • Pagination: 13p
  • Monograph Title: 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV): Enhanced and Equitable Vehicle Safety for All: Toward the Next 50 Years

Subject/Index Terms

Filing Info

  • Accession Number: 01893903
  • Record Type: Publication
  • Report/Paper Numbers: 23-0306
  • Files: TRIS, ATRI, USDOT
  • Created Date: Sep 22 2023 8:52AM