15 papers
MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving
Ziying Song, Shengkai Zhang, Lei Yang +6
Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex interactions. However, existing…
PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout
Haozhuang Chi, Jingsong Liang, Ziying Song +4
Local pedestrian-vehicle forecasting spans heterogeneous physical scales: pedestrians combine root locomotion with articulated motion, whereas vehicles are rigid bodies described b…
Risk-Aware Decision-Making for Autonomous Overtaking: A World Model-Based Mixture-of-Experts Framework
Yongzhi Liu, Sunan Zhang, Jinchang Xu +4
Autonomous highway overtaking demands foresighted decision-making to handle complex interactions, stochastic traffic evolution, and temporal risk accumulation. However, standard sa…
Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning
Sheng Zhao, Bolin Zhao, Xiaodong Wu +1
In Formula 1, drivers optimize racing lines within tire grip limits to minimize lap times; however, in rally racing, drivers intentionally break traction to drift on loose surfaces…
Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
Haozhuang Chi, Daosheng Qiu, Hao Su +4
Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driving world models forecast the external environment…
UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception
Wenzhuo Liu, Qiannan Guo, Zhen Wang +9
Advanced Driver Assistance Systems (ADAS) need to understand human driver behavior while perceiving their navigation context, but jointly learning these heterogeneous tasks would c…
SparScene: Efficient Traffic Scene Representation via Sparse Graph Learning for Large-Scale Trajectory Generation
Xiaoyu Mo, Jintian Ge, Zifan Wang +2
Multi-agent trajectory generation is a core problem for autonomous driving and intelligent transportation systems. However, efficiently modeling the dynamic interactions between nu…
DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
Ke Guo, Haochen Liu, Xiaojun Wu +1
Realistic traffic simulation is critical for the development of autonomous driving systems and urban mobility planning, yet existing imitation learning approaches often fail to mod…
Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities
Zirui Li, Yunlong Lin, Guodong Du +5
Artificial intelligence underpins most smart city services, yet deep neural network (DNN) that forecasts vehicle motion still struggle with catastrophic forgetting, the loss of ear…
Self-supervised Pretraining for Integrated Prediction and Planning of Automated Vehicles
Yangang Ren, Guojian Zhan, Chen Lv +3
Predicting the future of surrounding agents and accordingly planning a safe, goal-directed trajectory are crucial for automated vehicles. Current methods typically rely on imitatio…
Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang +7
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, exist…
iPad: Iterative Proposal-centric End-to-End Autonomous Driving
Ke Guo, Haochen Liu, Xiaojun Wu +2
End-to-end (E2E) autonomous driving systems offer a promising alternative to traditional modular pipelines by reducing information loss and error accumulation, with significant pot…
Crafting Physical Adversarial Examples by Combining Differentiable and Physically Based Renders
Yuqiu Liu, Huanqian Yan, Xiaopei Zhu +4
Recently we have witnessed progress in hiding road vehicles against object detectors through adversarial camouflage in the digital world. The extension of this technique to the phy…
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
Wenru Liu, Yongkang Song, Chengzhen Meng +4
We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we form…
Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving
Haochen Liu, Li Chen, Yu Qiao +2
Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent sce…