Accurate identification of vehicle-group-level traffic risk is important for intelligent transportation safety management. Existing risk-prediction studies have mainly focused on individual vehicles, pairwise interactions, or aggregated surrogate safety indicators, while the role of group-level structure-behavior coupl...
Jing Gan, Yao Wu, Da-Peng Zhang et al.· Accident Analysis and Preven...· 0 citations
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they...
Jia-Xi Liu, Hang Zhou, Hang-Yu Li et al.· 0 citations
Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing valuable support for end-to-end autonomous driving. However, existing cooperative driving methods mainly exploit roadside information to enhance the representation of th...
Jun-Wei You, Wei-Zhe Tang, Can Wang et al.· 0 citations
To address the limitations of existing models for mixed networks comprising expressways and arterial regions, this study develops a unified modeling and control framework. First, within such a mixed network, the trip length characteristics of urban trips are analyzed, revealing marked differences in trip lengths betwee...
Yunran Di, Weihua Zhang, Heng Ding et al.· IEEE Transactions on Automat...· 0 citations
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