14 papers
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
Chengyue Wang, Bin Rao, Haicheng Liao +3
Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents. However, most existing approaches are purel…
TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction
Yanchen Guan, Chengyue Wang, Bin Rao +5
Traffic accident reconstruction is a forensic inverse problem that requires recovering physically consistent motion from sparse and heterogeneous evidence. Existing learning-based…
SwiftCache: Efficient LLM Serving for Multi-turn Conversations with Heterogeneous KV Cache Sharing
Jianmin Hu, Minxian Xu, Sa Wang +5
Multi-turn conversation is a fundamental scenario in LLM applications, widely used in chatbots and AI agents. As the conversation evolves, historical tokens accumulate continuously…
CausalDrive: Real-time Causal World Models for Autonomous Driving
Tianyi Yan, Huan Zheng, Dubing Chen +10
World models have emerged as a promising paradigm for scaling autonomous driving (AD) data, yet existing video generative models fall short as interactive simulators. Layout-condit…
E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving
Yihong Tang, Haicheng Liao, Tong Nie +7
End-to-end autonomous driving (AD) systems increasingly adopt vision-language-action (VLA) models, yet they typically ignore the passenger's emotional state, which is central to co…
Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
Haicheng Liao, Huanming Shen, Bonan Wang +8
Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) methods for autonomous vehicles (AVs) typi…