Wu, D.; Tu, S.Z.; Whalin, R.W.; Zhang, L. Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs. Vehicles2023, 5, 1275-1293.
Wu, D.; Tu, S.Z.; Whalin, R.W.; Zhang, L. Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs. Vehicles 2023, 5, 1275-1293.
Wu, D.; Tu, S.Z.; Whalin, R.W.; Zhang, L. Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs. Vehicles2023, 5, 1275-1293.
Wu, D.; Tu, S.Z.; Whalin, R.W.; Zhang, L. Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs. Vehicles 2023, 5, 1275-1293.
Abstract
By shifting the focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can contribute to a more comprehensive understanding of driving behavior. Combing DAD with a conflict identification (CIM) model can potentially enhance the effectiveness of Advanced Driver Assistance Systems (ADAS) in terms of crash evasion capabilities. This paper is part of our research titled Automatic Safety Diagnosis in Connected Vehicle Environment, which received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
