10 papers
EvoAD: Self-Evolving Autonomous Driving via Latent World-Model Guided Post-hoc Reflection
Junjie Yang, Qiming Zhang, Yuyan Li +12
End-to-end autonomous driving systems have achieved rapid progress by learning unified policies that map sensor observations directly to driving trajectories. However, once deploye…
From Likelihood to Utility: Quality-Axis Diffusion for End-to-End Autonomous Driving
Junjie Yang, Ziyi Shi, Shuqi Shen +3
Diffusion models have become a dominant paradigm for end-to-end autonomous driving, yet existing planners organize trajectory generation along the Gaussian-noise axis, treating dri…
EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving
Junjie Yang, Qingwei Zeng, Youyou Li +5
World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictability, motivating a distinctio…
SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles
Bin Rao, Haicheng Liao, Chengyue Wang +3
Autonomous vehicles (AVs) rely on accurate trajectory prediction for safe navigation in diverse traffic environments, yet existing models struggle with long-tail scenarios-rare but…
Flow Matching for Offline Reinforcement Learning with Discrete Actions
Fairoz Nower Khan, Nabuat Zaman Nahim, Ruiquan Huang +2
Generative policies based on diffusion models and flow matching have shown strong promise for offline reinforcement learning (RL), but their applicability remains largely confined…
Coordinated Pandemic Control with Large Language Model Agents as Policymaking Assistants
Ziyi Shi, Xusen Guo, Hongliang Lu +7
Effective pandemic control requires timely and coordinated policymaking across administrative regions that are intrinsically interdependent. However, human-driven responses are oft…
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…
LILAC: Long-sequence Incremental Low-latency Arbitrary Motion Stylization via Streaming VAE-Diffusion with Causal Decoding
Peng Ren, Hai Yang
Generating long and stylized human motions in real time is critical for applications that demand continuous and responsive character control. Despite its importance, existing strea…
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning
Tengfei Lyu, Siyuan Feng, Hao Liu +1
Ride-hailing platforms face significant challenges in optimizing order dispatching and driver repositioning operations in dynamic urban environments. Traditional approaches based o…
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
Mingxing Peng, Yuting Xie, Xusen Guo +3
Ensuring the safety and robustness of autonomous driving systems necessitates a comprehensive evaluation in safety-critical scenarios. However, these safety-critical scenarios are…