Explainable Motion Planning

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Overview

The field studies how robots and vehicles choose and revise trajectories, and how people can understand, verify, or guide those choices. Approaches include rule-based planners, causal and contrastive explanations, language-guided planning, and learned trajectory models paired with safety checks or interpretable predictions. Work is evaluated in closed-loop driving benchmarks such as nuPlan and CARLA, as well as robot simulations and real-world navigation tasks. A central challenge is balancing clear, useful explanations and robust safety with real-time performance, generalization to unfamiliar situations, and the risk that explanations add cognitive workload.

Latest papers

CardsList

17 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Learning to Explain While Planning: Rule-Aligned Diffusion Planning for Autonomous Driving

    Sep 30, 2026Jiaxi Ye, Chunji Lv, Guoren Wang +1Diffusion-Based PlanningAutonomous Driving Planning

    Beijing Institute of Technology

  2. Predict Before You Step: Auditable Occupancy Forecasting for Dynamic Obstacle Avoidance under Sparse Guidance

    Sep 22, 2026Yuhui Mao, Fen Liu, Shenghai Yuan +3Robot Navigation3D Occupancy Prediction

    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798

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

    Aug 12, 2026Jean-Pierre Busch, Guido Linden, Jan Bergmann +1Safe Motion PlanningMotion Planning

    Institute for Automotive Engineering (ika), RWTH Aachen University, Germany · ika · Chair of Automotive Technology (FTM), Technical University of Munich +1

  4. Parsimonious disturbance-aware minimum-time planning with parametric uncertainty

    Jul 14, 2026Martino Gulisano, Matteo Masoni, Marco GabicciniTrajectory OptimizationAutonomous Racing

    Dipartimento di Ingegneria Civile e Industriale, Universit`a di Pisa, Pisa, Italy.

  5. World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning

    Jul 12, 2026Tong Nie, Yuewen Mei, Junlin He +3Adversarial TrainingSelf-Play RL

    The Hong Kong Polytechnic University · Tongji University · McGill University +1

  6. BucketKD: A Safety-Aware Bucket-Based Knowledge Distillation Framework for End-to-End Motion Planning

    Jul 12, 2026Md Nahidul Islam, Mohd Hasan Ali, Dipankar Dasgupta +1Safe Motion PlanningMotion Planning

    Department of Computer Science, University of Memphis, Memphis, TN, United States · Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, United States

  7. Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning

    Jul 8, 2026Alessandro Canevaro, Hang Yu, Julian Schmidt +5Autonomous Driving BenchmarksDistribution Shift

    Mercedes-Benz AG, Research & Development, Sindelfingen, Germany · University of Tübingen, Tübingen, Germany · Karlsruhe Institute of Technology, ITIV, Karlsruhe, Germany

  8. Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner

    Jun 24, 2026Fenghe Guo, Runjie Shen, Chenyang Sun +4Multi-Robot Motion PlanningMobile Robotics

    Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China · State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201210, China · College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China

  9. Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency

    Jun 5, 2026Haoxuan Dong, Dongjun Li, Ziyou SongAdaptive Model RoutingMotion Planning

    Department of Mechanical Engineering National University of Singapore Singapore 117575, Singapore · Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI 48109, USA

  10. AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems

    May 26, 2026Seyedali Golestaneh, Zhuoyun Zhong, Donghyung Lee +1Robot Trajectory TrackingKinodynamic Motion Planning

    Department of Robotics Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA 01609, USA

  11. Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning

    May 24, 2026Hichem Cheriet, Badra Khellat Kihel, Samira Chouraqui +1Robot NavigationMotion Planning

  12. Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners

    Apr 15, 2026Nick Le Large, Marlon Steiner, Lingguang Wang +4Safe Motion PlanningMotion Planning

    Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany · FZI Research Center for Information Technology, Karlsruhe, Germany

  13. "Why This Avoidance Maneuver?" Contrastive Explanations in Human-Supervised Maritime Autonomous Navigation

    Apr 9, 2026Joel Jose, Andreas Madsen, Andreas Brandsæter +2Explainable Motion PlanningCounterfactual Explanations

    Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), Ålesund, Norway. · Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Ålesund, Norway. · Department of Science and Mathematics, Volda University College (HVO), Volda, Norway. +1

  14. RAD-LAD: Rule and Language Grounded Autonomous Driving in Real-Time

    Mar 30, 2026Anurag Ghosh, Srinivasa Narasimhan, Manmohan Chandraker +1Autonomous Driving PlanningLanguage Model-Based Control

    Carnegie Mellon University · NEC Labs America · UC San Diego

  15. A Causal Approach to Predicting and Improving Human Perceptions of Social Navigation Robots

    Mar 11, 2026Maximilian Diehl, Nathan Tsoi, Gustavo Chavez +2Human-Robot InteractionSocial Robot Navigation

    Faculty of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. · The University of Texas at Austin, Austin, Texas, USA. · Yale University, New Haven, Connecticut, USA.

  16. Towards Intelligible Human-Robot Interaction: An Active Inference Approach to Occluded Pedestrian Scenarios

    Feb 26, 2026Kai Chen, Yuyao Huang, Guang ChenAutonomous Driving PlanningExplainable Motion Planning

    Tongji University Shanghai, China

  17. Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation

    Feb 22, 2026Haojun Shi, Suyu Ye, Katherine M. Guerrerio +5Multi-Agent PlanningMulti-Robot Coordination

    Johns Hopkins University