mlsanityData to 7 Oct 2026

Driving in Real Life with Inverse Reinforcement Learning

arXiv 2206.03004 ↗First posted 7 Jun 2022cs.RO cs.AI cs.LGcited by 18

On the map: End-to-end autonomous driving → Reinforcement learning for driving

also in 1 more
Abstract (arXiv)

In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals, filters these trajectories with a lightweight and interpretable safety filter, and then uses a learned model to score each remaining trajectory. The best trajectory is then tracked by the low-level controller of our self-driving vehicle. We train our trajectory scoring model on a 500+ hour real-world dataset of expert driving demonstrations in Las Vegas within the maximum entropy IRL framework. DriveIRL's benefits include: a simple design due to only learning the trajectory scoring function, relatively interpretable features, and strong real-world performance. We validated DriveIRL on the Las Vegas Strip and demonstrated fully autonomous driving in heavy traffic, including scenarios involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff zones. Our dataset will be made public to help further research in this area.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 24 of its 32 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

24 of 32 references.

References · 24

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2016End to End Learning for Self-Driving Carscited by 1,000
  7. 2017Focal Loss for Dense Object Detectioncited by 749
  8. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  9. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 485
  10. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  11. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  12. 2019Learning by Cheatingcited by 361
12 more
  1. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
  2. 2018Learning to Drive in a Daycited by 266
  3. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  4. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 251
  5. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  6. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
  7. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  8. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  9. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  10. 2021Learning to drive from a world on railscited by 114
  11. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policiescited by 68
  12. 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learningcited by 46

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 7; 2024: 4; 2025: 4; 2026: 1.

18 papers.

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  1. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  2. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  3. 2023CCIL: Context-conditioned imitation learning for urban drivingcited by 12
  4. 2023End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023cited by 11
  5. 2023A Bayesian Approach to Robust Inverse Reinforcement Learningcited by 9
  6. 2022Differentiable Constrained Imitation Learning for Robot Motion Planning and Controlcited by 7
  7. 2022Receding Horizon Planning with Rule Hierarchies for Autonomous Vehiclescited by 7
  8. 2023Autonomous Driving with Deep Reinforcement Learning in CARLA Simulationcited by 5
  9. 2024Blending Data-Driven Priors in Dynamic Gamescited by 5
  10. 2025LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Modelscited by 4
  11. 2024Automated Feature Selection for Inverse Reinforcement Learningcited by 1
  12. 2024End-To-End Training and Testing Gamification Framework to Learn Human Highway Drivingcited by 1
6 more
  1. 2024Hybrid Imitation-Learning Motion Planner for Urban Drivingcited by 1
  2. 2025Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Surveycited by 1
  3. 2025A Framework for Learning Scoring Rules in Autonomous Driving Planning Systemscited by 1
  4. 2025Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimalitycited by 1
  5. 2023NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Predictioncited by 0
  6. 2026Q-based Variational Inverse Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
  2. 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
  3. 2026Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study
  4. 2025GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
  5. 2022Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
  6. 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
  7. 2019Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
  8. 2023Learning Realistic Traffic Agents in Closed-loop