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Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles’ practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes – putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, etc. On the other hand, modern vehicle-to-vehicle (V2V) communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other [1]. In this paper, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers’ privacy. To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of\(97.3\% \), average detection delay of 1.2s, and 0 false alarm.
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Anomalous Behavior Detection in Trajectory Data of Older Drivers
Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hard-brakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.
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- PAR ID:
- 10487845
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)
- ISSN:
- 1949-4092
- ISBN:
- 979-8-3503-3111-0
- Page Range / eLocation ID:
- 146 to 151
- Format(s):
- Medium: X
- Location:
- Boca Raton, FL, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HONET59747.2023.10374878
