RL for Autonomous Driving

RL: Reinforcement Learning

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10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

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Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
Jun 12, 2026Chufei Yan, Zhihao Cui, Yiyan Lv +3Reinforcement Learning Mixture of Experts
School of Physics, Northeast Normal University, Changchun, 130024, China. · 2Clean Energy Automotive Engineering Center, School of Automotive Studies, Tongji University, Shanghai, 201804, China. · 3Mengshi Automobile Technology Company, Dongfeng Motor Corporation, Wuhan, 430010, China.
From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving
Jun 12, 2026Yuewen Mei, Tong Nie, Jie Sun +3Adversarial Training Autonomous Driving
College of Transportation & Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, China · Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Human-like autonomy emerges from self-play and a pinch of human data
Jun 11, 2026Daphne Cornelisse, Julian Hunt, Zixu Zhang +4Self-Play RL Autonomous Driving
1NYU Tandon School of Engineering · 2NYU Courant · 3Princeton University +2
Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models
Jun 9, 2026Samuel Tetteh, Cody FlemingConstrained RL Robot Safety
Iowa State University Ames, Iowa, USA
Uncertainty-Aware Motion Planning for Autonomous Driving in Mixed Traffic Environment
Jun 8, 2026Ming Cheng, Hao Chen, Ziyi Yang +2Safe Motion Planning Motion Planning
Central South University · City University of Macau
DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving
Jun 7, 2026Qimao Chen, Fang Li, Yuechen Luo +11Autonomous Driving Datasets Autonomous Driving Benchmarks
1Tsinghua University · 2Xiaomi EV
Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach
Jun 5, 2026Qiang Le, Yaguang Yang, Isaac E. WeintraubConstrained Motion Planning Safe Motion Planning
Hampton University, Hampton, VA 23669, USA · Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA
An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving
Jun 2, 2026Ouided Braoui, Meriem Bouali, Nadir FarhiExplainable Artificial Intelligence Autonomous Driving
LITAN, ESTIN, RN 75, Amizour, 06300, Bejaia, Algeria · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallée, France
DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions
May 29, 2026Weicheng Zheng, Yixin Huang, Qiao Sun +2VLMs for Autonomous Driving Autonomous Driving
1Shanghai Qi Zhi Institute · 3Tongji University · 2IIIS Tsinghua University
ZAPS-DA: Zero-Phase Action Policy Smoothing with Decoupled Actor for Continuous Control in Reinforcement Learning
May 28, 2026Faiq ShamassRL Control Reinforcement Learning
San Diego, CA, USA
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
May 28, 2026Ahmed Abouelazm, Felix Klingebiel, Philip Schörner +1Reinforcement Learning Risk-Sensitive RL
FZI Research Center for Information Technology, Germany · Karlsruhe Institute of Technology, Germany
SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
May 27, 2026Kangyu Wu, Peng Cui, Guoxi Chen +1Autonomous Driving Traffic Accident Anticipation
School of Automation, Southeast University, Nanjing, China · Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China
DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions
May 20, 2026Weicheng Zheng, Yixin Huang, Qiao Sun +2VLMs for Autonomous Driving Efficient VLA Models
Shanghai Qi Zhi Institute · Tongji University · Tsinghua University
Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
May 20, 2026Yang Wu, Qiang Meng, Zhaojiang Liu +3End-to-End Autonomous Driving World Models
SJTU · FDU · NJU
Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
May 18, 2026Prakash Aryan, Kaushik Raghupathruni, Timo Kehrer +1Pedestrian Behavior Modeling Human Behavior Simulation
University of Bern, Bern, Switzerland. · AI4I, The Italian Institute of Artificial Intelligence, Turin, Italy.
Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions
May 17, 2026Qingwen Pu, Kun Xie, Yuan Zhu +1Human Behavior Simulation Autonomous Driving Safety Evaluation
Transportation Informatics Lab, Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, VA 23529, United States · Inner Mongolia Center for Transportation Research, Inner Mongolia University, Rm A357A, Transportation Building, Inner Mongolia University South Campus, 49 S Xilin Rd, Hohhot, Inner Mongolia, 010020, China · School of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, National and Local Joint Engineering Research Center of Integrated Transportation Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles
May 16, 2026Jianye Xu, Bassam AlrifaeeReward Shaping Control Barrier Functions
Department of Computer Science, RWTH Aachen University, Germany · Department of Aerospace Engineering, University of the Bundeswehr Munich, Germany
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
May 13, 2026Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9End-to-End Autonomous Driving Vision-Language-Action Models
Qualcomm AI Research · Qualcomm Technologies, Inc
Driving Intents Amplify Planning-Oriented Reinforcement Learning
May 12, 2026Hengtong Lu, Victor Shea-Jay Huang, Chengmin Yang +4RL for Autonomous Driving Goal-Conditioned RL
Li Auto · Tsinghua University · CUHK MMLab
MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
May 11, 2026Guangli Chen, Dianzhao Li, Wenjian Zhong +2Camera-LiDAR Fusion RL for Autonomous Driving
Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University, Dongguan, China · Chair of Applied Statistics, Technische Universität Dresden, Dresden, Germany · Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Dresden, Germany +1
Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
May 11, 2026Aron Distelzweig, Faris Janjoš, Andreas Look +7Autonomous Driving Benchmarks Self-Play RL
University of Freiburg · Bosch Center for Artificial Intelligence · Coburg University of Applied Sciences +1
REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
May 9, 2026Changze Li, Zhe Chen, Shaoyu Chen +7End-to-End Autonomous Driving Autonomous Parking
Horizon Robotics Technology Research and Development Co., Ltd.
ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving
May 6, 2026Huimin Wang, Yue Wang, Bihao Cui +7Diffusion Policy Autonomous Driving Planning
LiAuto
InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making
May 5, 2026Mustafa Sakhaia, Kaung Sithua, Min Khant Soe Okea +1Event-Based Vision End-to-End Autonomous Driving
aFaculty of Computer Science, Electronics and Telecommunications, AGH University of Krakow, Krakow, 30-059, Poland · bAcademic Computer Centre AGH, Krakow, 30-950, Poland
Dynamics Distillation for Efficient and Transferable Control Learning
May 2, 2026Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian +2Vehicle Dynamics Transfer Learning
University of Toronto · Vector Institute · NVIDIA +1
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment
Apr 30, 2026Ziang Guo, Chen Min, Xuefeng Zhang +5End-to-End Autonomous Driving RL for Autonomous Driving
Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia. · Research Center for Intelligent Computing Systems, SKLP, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. · SuZhou Automotive Research Institute, Tsinghua University, China. +1
Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA
Apr 30, 2026Feeza Khan Khanzada, Jaerock KwonDomain Generalization Cross-Domain Generalization
Department of Electrical and Computer Engineering, University of Michigan–Dearborn, Dearborn, MI 48128 USA
SpanVLA: Learning from Negative-Recovery Samples with Fast Action Bridging for Vision-Language-Action Model
Apr 21, 2026Zewei Zhou, Ruining Yang, Xuewei Qi +7VLMs for Autonomous Driving Autonomous Driving
Tony · University of California, Los Angeles, USA · Motional, USA +1
Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
Apr 17, 2026Shathushan Sivashangaran, Vihaan Dutta, Apoorva Khairnar +2OOD Generalization Autonomous Driving
Virginia Commonwealth University, Richmond, VA 23284, USA · University of Michigan, Ann Arbor, MI 48109, USA · Virginia Tech, Blacksburg, VA 24061, USA
RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
Apr 16, 2026Hao Gao, Shaoyu Chen, Yifan Zhu +4Diffusion-Based Planning RL for Diffusion Models
Huazhong University of Science & Technology · Horizon Robotics

Jun 12, 2026, cs.ROJ/K move · Enter open · S save

Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency

Chufei Yan, Zhihao Cui, Yiyan Lv, Taojie Chen+2

School of Physics, Northeast Normal University, Changchun, 130024, China. · 2Clean Energy Automotive Engineering Center, School of Automotive Studies, Tongji University, Shanghai, 201804, China. · 3Mengshi Automobile Technology Company, Dongfeng Motor Corporation, Wuhan, 430010, China.

Reinforcement Learning Mixture of Experts Autonomous Driving RL for Autonomous Driving Safe RL

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Deep reinforcement learning (DRL) offers a compelling route to decision-making for advanced autonomous vehicles (AVs), yet its trial-and-error nature makes it difficult to guarantee safety during training and to achieve both safety and efficiency at deployment. We propose a unified safe reinforcement learning (SRL) framework that integrates safe distance (SD), reward machines (RM), and mixture-of-experts (MoE), termed MoE-RM-SRL. For deployment, SD and RM jointly shape a rule-aware reward that encodes highway traffic regulations and stage-wise objectives, enabling safe and reliable behavior without sacrificing efficiency. For training, we introduce a sparsely gated MoE layer comprising up to 11 deep Q-networks (DQNs); an SD-based gating rule activates a minimal set of experts for lane-keeping and lane-changing, mitigating the instability, discontinuities, and impulsive transients commonly induced by switching between heterogeneous controllers (e.g., MPC/rule-based modules and learned policies). We implement the proposed architecture in CARLA and integrate it with a 6-DoF driver-in-the-loop virtual-reality (DiL-VR) platform. Experiments in stochastic two-lane traffic show that MoE-RM-SRL substantially improves safety and efficiency over state-of-the-art baselines, and the framework naturally extends to multi-lane driving as well as on-ramp merging and exiting scenarios.

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