5 papers
VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving
Tianjia Yang, Ke Li, Ruwen Qin +1
Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the vehicle can perceive and what it…
CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation
Ke Li, Kaidi Liang, Yuxin Ding +3
Evaluation of autonomous vehicle (AV) planners in safety-critical closed-loop simulation is essential for real-world deployment. However, generating controllable safety-critical sc…
HMPDM: A Diffusion Model for Driving Video Prediction with Historical Motion Priors
Ke Li, Tianjia Yang, Kaidi Liang +2
Video prediction is a useful function for autonomous driving, enabling intelligent vehicles to reliably anticipate how driving scenes will evolve and thereby supporting reasoning a…
CrashChat: A Multimodal Large Language Model for Multitask Traffic Crash Video Analysis
Kaidi Liang, Ke Li, Xianbiao Hu +1
Automating crash video analysis is essential to leverage the growing availability of driving video data for traffic safety research and accountability attribution in autonomous dri…
Multi-label Scene Classification for Autonomous Vehicles: Acquiring and Accumulating Knowledge from Diverse Datasets
Ke Li, Chenyu Zhang, Yuxin Ding +2
Driving scenes are inherently heterogeneous and dynamic. Multi-attribute scene identification, as a high-level visual perception capability, provides autonomous vehicles (AVs) with…