Enhancing Autonomous Vehicle Safety in Cold Climates by Using a Road Weather Model: Safely Avoiding Unnecessary Operational Design Domain Exits

This study investigates the use of a road weather model (RWM) as a virtual sensing technique to assist autonomous vehicles (AVs) in driving safely, even in challenging winter weather conditions. In particular, we investigate how the AVs can remain within their operational design domain (ODD) for a greater duration and minimize unnecessary exits. As the road surface temperature (RST) is one of the most critical variables for driving safety in winter weather, we explore the use of the vehicle’s air temperature (AT) sensor as an indicator of RST. Data from both Road Weather Information System (RWIS) stations and vehicles measuring AT and road conditions were used. Results showed that using only the AT sensor as an indicator of RST could result in a high number of false warnings, but the accuracy improved significantly with the use of an RWM to model the RST. ROC-curve analysis resulted in an AUC value of 0.917 with the AT sensor and 0.985 with the RWM, while the true positive rate increased from 67% to 94%. The study also highlights the limitations of relying on dashboard cameras to detect slippery driving conditions, as it may not be accurate enough to distinguish between, for example, wet and icy road conditions. As winter maintenance often prevents slippery roads, the vehicles often measured wet or moist roads, despite RST < 0°C. Our calculations indicate that the vehicle should be able to detect 93% of slippery occasions but the rate of false warnings will be as high as 73%, if using a dashboard camera along with the AT sensor. There are clear benefits of using a RWM to improve road safety and reduce the risk of accidents due to slippery conditions, allowing AVs to safely extend their time within their ODD. The findings of this study provide valuable insights for the development of AVs and their response to slippery road conditions.