DGCPath is a Distribution‑Aware Generative Contrastive framework designed for self‑supervised path representation learning. It combines a diffusion‑based view generator, a variational contrastive mechanism that aligns latent features at the distribution level, and a generative cross‑supervision module for view‑level consistency. Experiments on three real‑world trajectory datasets show that DGCPath surpasses state‑of‑the‑art baselines on two downstream tasks, indicating stronger generalization and representation effectiveness.
By Sean Bin Yang, Hao Miao, Zongyi Xu, Jilin Hu, Xiangmeng Wang, Hua Lu, Bin Yang, Christian S. Jensen
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
By Mingkai Jia, Jiaxin Guo, Zhijian Shu, Jiawei Xu, Mingxiao Li, Jintao Cheng, Ping Tan, Wei Yin
arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.
By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song
arXiv:2606. 09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback.
By Lena Krieger, Xuan Zhao, Zhuo Cao, Qin Wang, Hanno Scharr, Ira Assent
arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
By Juntong Peng, Juanwu Lu, Yupeng Zhou, Can Cui, Yaobin Chen, Ziran Wang
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class of models includes both Vision-Language-Action models and trajectory-generative planners.
arXiv:2608. 01039v1 Announce Type: cross Abstract: Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures.
By Liwei Deng, Haotian Meng, Yupu Zhang, Yan Zhao, Torben Bach Pedersen, Kai Zheng, Christian S. Jensen
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang
arXiv:2606.27504v2 Announce Type: replace
Abstract: World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only...
By Tianze Xia, Lijun Zhou, Kaixin Xiong, Jingfeng Yao, Zhenxin Zhu, Haiyang Sun, Bing Wang, Guang Chen, Wenyu Liu, Hangjun Ye, Xinggang Wang
The paper proposes a method for learning task-relevant representations in deep reinforcement learning by maximizing rollout total correlation, which captures the correlation among all learned representations and actions across entire trajectories. It introduces two complementary lower bounds—one generative and one discriminative—along with chunk‑wise mini‑batching to improve this objective, and also proposes an intrinsic reward derived from the learned representation to enhance exploration. Experiments on challenging image‑based simulated control tasks demonstrate improved sample efficiency and robustness to white noise and natural video backgrounds compared to leading baselines.
By Bang You, Huaping Liu, Jan Peters, Oleg Arenz
arXiv:2507. 00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns.
By Lihuan Li, Hao Xue, Shuang Ao, Yang Song, Flora Salim
arXiv:2607. 09741v1 Announce Type: cross Abstract: Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents.
By Chengyue Wang, Bin Rao, Haicheng Liao, Bonan Wang, Chengzhong Xu, Zhenning Li