In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions. Evaluating vision-language models (VLMs) therefore requires testing whether they connect past spatial observations to future consequences. Yet existing benchma...
Lie-Qi Liu, Rui Gao, Jia-Chen Gu et al.· 0 citations
A novel PIDL model is proposed called DoubleGAN, which encodes the stochastic physics into the PIDL structure and captures the uncertain from vehicle trajectory data and introduces a stochastic CF model to inform the generator with prior physics information.
This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.
Yongjie Fu, Mehmet Kerem Turkcan, Mahshid Ghasemi et al.· IEEE transactions on intelli...· 4 citations
This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack, delving into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making.
Yu-Ping Wang, Shuo Xing, C. Cui et al.· ACM Computing Surveys· 60 citations· ⚡2
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