Autonomous Driving Planning

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30 papers in the last four weeks, up 76% on the four weeks before. 0.3% of all new papers.

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Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response
Dec 3, 2025Aron Distelzweig, Yiwei Wang, Faris Janjoš +5Autonomous Driving Planning Multi-Agent Planning
University of Freiburg, Germany · Bosch Center for Artificial Intelligence, Germany · Technical University of Munich, Germany +2
GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving
Nov 14, 2025Fabian Schmidt, Markus Enzweiler, Abhinav ValadaVision-Language Models VLMs for Autonomous Driving
Esslingen University of Applied Sciences · University of Freiburg
HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving
Nov 10, 2025Zhongyu Xia, Zhiwei Lin, Yongtao Wang +1Autonomous Driving Perception End-to-End Autonomous Driving
Wangxuan Institute of Computer Technology, Peking University, Beijing, China. · University of California, Merced, USA.
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
Oct 28, 2025Zhenxin Li, Nadine Chang, Wenhao Yao +9Reinforcement Learning End-to-End Autonomous Driving
Institute of Trustworthy Embodied AI, Fudan University · *Work done during an internship at NVIDIA. · NVIDIA +3
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
Oct 6, 2025Xurui Song, Shuo Huai, JingJing Jiang +2VLMs for Autonomous Driving Reasoning for VLA Models
S-Lab, Nanyang Technological University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
May 27, 2025Zhennan Wang, Jianing Teng, Canqun Xiang +4End-to-End Autonomous Driving Autonomous Driving Perception
HAOMO.AI Technology Co., Ltd
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
Mar 31, 2025Zhuoren Li, Guizhe Jin, Ran Yu +8Autonomous Driving Planning RL for Autonomous Driving
College of Automotive and Energy Engineering, Tongji University, Shanghai 201804, China
Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving
Feb 10, 2025Lei Zheng, Rui Yang, Minzhe Zheng +3Safe Motion Planning Motion Planning
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · School of Engineering, Great Bay University, Dongguan 523000, China · Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Fading Expert Guidance: Bridging Model-Based and Learning-Based Control for Abortable Autonomous Overtaking
Aug 18, 2023Jinxiong Lu, Gokhan Alcan, Ville KyrkiAutonomous Driving Planning RL for Autonomous Driving
Intelligent Robotics Group, Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Data-Driven Risk Fields for Safer End-to-End Autonomous Driving
Date pendingYuanxin Tian, Zhiyuan Liu, Jinhao Li +9End-to-End Autonomous Driving Safe Motion Planning
Tsinghua University · Yinwang · KAIST

Dec 3, 2025, cs.ROJ/K move · Enter open · S save

Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response

Aron Distelzweig, Yiwei Wang, Faris Janjoš, Marcel Hallgarten+4

University of Freiburg, Germany · Bosch Center for Artificial Intelligence, Germany · Technical University of Munich, Germany+2

Autonomous Driving Planning Multi-Agent Planning

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Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense urban traffic, where lane changes and merges require anticipating and influencing other agents. Modern motion predictors offer highly accurate forecasts, yet their integration into planning is mostly rudimental: discarding unsafe plans. Similarly, end-to-end models offer a one-way integration that avoids the challenges of joint prediction and planning modeling under uncertainty. In contrast, game-theoretic formulations offer a principled alternative but have seen limited adoption in autonomous driving. We present Bayesian Iterative Best Response (BIBeR), a framework that unifies motion prediction and game-theoretic planning into a single interaction-aware process. BIBeR is the first to integrate a state-of-the-art predictor into an Iterative Best Response (IBR) loop, repeatedly refining the strategies of the ego vehicle and surrounding agents. This repeated best-response process approximates a Nash equilibrium, enabling bidirectional adaptation where the ego both reacts to and shapes the behavior of others. In addition, our proposed Bayesian confidence estimation quantifies prediction reliability and modulates update strength, more conservative under low confidence and more decisive under high confidence. BIBeR is compatible with modern predictors and planners, combining the transparency of structured planning with the flexibility of learned models. Experiments show that BIBeR achieves an 11% improvement over state-of-the-art planners on highly interactive interPlan lane-change scenarios, while also outperforming existing approaches on standard nuPlan benchmarks.

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