arXiv AI

Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving

arXiv:2508. 16947v2 Announce Type: replace-cross Abstract: Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inherent behavioral diversity of human driving.

arXiv Machine Learning

Jul 7

A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

arXiv:2503. 23650v2 Announce Type: replace Abstract: Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD).

By Zhuoren Li, Guizhe Jin, Ran Yu, Weiqi Zhang, Zhiwen Chen, Nan Li, Lu Xiong, Ilya Kolmanovsky, Dimitar Filev, Bo Leng, Jia Hu

agents reinforcement-learning robotics

arXiv AI

Jul 7

AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.

By Qi Liu, Yabei Li, Hongsong Wang, Heng Zhang, Lei He

llms agents robotics multimodal benchmarks safety

arXiv Machine Learning

Aug 12

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving

arXiv:2608. 10386v1 Announce Type: new Abstract: Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias.

By Jiazhuo Li, Linjiang Cao, Qi Liu, Xi Xiong

agents diffusion reinforcement-learning robotics safety

arXiv AI

Jun 16

HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization

arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.

By Youngjae Min, Jovin D'sa, Faizan M. Tariq, David Isele, Navid Azizan, Sangjae Bae

agents reinforcement-learning robotics

arXiv AI

Aug 3

RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving

arXiv:2602. 07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment.

By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy

agents diffusion reinforcement-learning robotics efficiency multimodal safety

arXiv Machine Learning

Jun 16

CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving

arXiv:2510. 12560v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models trained with imitation learning (IL) often generalize poorly, particularly in long-tail scenarios where expert demonstrations are sparse.

By Xiaoji Zheng, Ziyuan Yang, Yanhao Chen, Yuhang Peng, Yuanrong Tang, Gengyuan Liu, Bokui Chen, Jiangtao Gong

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 22

End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation

arXiv:2607. 18637v1 Announce Type: cross Abstract: Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions.

By Jingzheng Li, Yufei Ge, Zhijun Chen, Qianren Mao, Zizhe Wang, Binhang Qi, Bing Li, Keyu Chen, Baochang Zhang, Xianglong Liu, Philip S Yu

agents diffusion robotics safety

arXiv AI

Aug 11

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving

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

agents fine-tuning multimodal benchmarks

arXiv AI

Jun 11

ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

arXiv:2606. 11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems.

By Qichao Zhang, Xing Fang, Jiaqi Fang, Zhenwen Cai, Jie Ling, Qiankun Yu, Dongbin Zhao

agents robotics multimodal safety

arXiv AI

Jun 6

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

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

agents diffusion robotics fine-tuning multimodal benchmarks safety

arXiv AI

6d ago

Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

arXiv:2608. 12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments.

By Jean-Pierre Busch, Guido Linden, Jan Bergmann, Lutz Eckstein

safety

arXiv Machine Learning

Jul 20

Dichotomous Diffusion Policy Optimization

arXiv:2601. 00898v3 Announce Type: replace Abstract: Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference.

By Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan

agents diffusion reinforcement-learning robotics multimodal benchmarks

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