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Real-World Data Inspired Interactive Connected Traffic Scenario Generation

Autor: You, Junwei, Li, Pei, Cheng, Yang, Wu, Keshu, Gan, Rui, Parker, Steven T., Ran, Bin

Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments becomes i

Externí odkaz: http://arxiv.org/abs/2409.17429

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Goal-based Neural Physics Vehicle Trajectory Prediction Model

Autor: Gan, Rui, Shi, Haotian, Li, Pei, Wu, Keshu, An, Bocheng, Li, Linheng, Ma, Junyi, Ma, Chengyuan, Ran, Bin

Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have

Externí odkaz: http://arxiv.org/abs/2409.15182

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V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

Autor: You, Junwei, Shi, Haotian, Jiang, Zhuoyu, Huang, Zilin, Gan, Rui, Wu, Keshu, Cheng, Xi, Li, Xiaopeng, Ran, Bin

Advancements in autonomous driving have increasingly focused on end-to-end (E2E) systems that manage the full spectrum of driving tasks, from environmental perception to vehicle navigation and control. This paper introduces V2X-VLM, an innovative E2E

Externí odkaz: http://arxiv.org/abs/2408.09251

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