mlsanityData to 14 Oct 2026

Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning

arXiv 1809.02926 ↗First posted 9 Sep 2018cs.LG cs.AI cs.ROcited by 28

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be interactive, since the distribution over all possible trajectories of the predicted vehicle depends not only on historical information, but also on future plans of other vehicles that interact with it. To achieve such interaction-aware predictions, we propose a probabilistic prediction approach based on hierarchical inverse reinforcement learning (IRL). First, we explicitly consider the hierarchical trajectory-generation process of human drivers involving both discrete and continuous driving decisions. Based on this, the distribution over all future trajectories of the predicted vehicle is formulated as a mixture of distributions partitioned by the discrete decisions. Then we apply IRL hierarchically to learn the distributions from real human demonstrations. A case study for the ramp-merging driving scenario is provided. The quantitative results show that the proposed approach can accurately predict both the discrete driving decisions such as yield or pass as well as the continuous trajectories.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 2 of its 27 references that are in the map. First posted on arXiv 9 Sep 2018.

Builds on

2 of 27 references.

References · 2

  1. 2018Probabilistic Prediction of Vehicle Semantic Intention and Motioncited by 31
  2. 2018Courteous Autonomous Carscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 2; 2021: 8; 2022: 7; 2023: 3; 2024: 2; 2025: 4; 2026: 2.

28 papers.

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  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  2. 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learningcited by 46
  3. 2021Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Trackingcited by 35
  4. 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Drivingcited by 34
  5. 2022The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Modelscited by 31
  6. 2021Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligencecited by 27
  7. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  8. 2022Transferable and Adaptable Driving Behavior Predictioncited by 20
  9. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  10. 2022Driver Digital Twin for Online Prediction of Personalized Lane Change Behaviorcited by 13
  11. 2021On complementing end-to-end human behavior predictors with planningcited by 10
  12. 2022Leveraging Smooth Attention Prior for Multi-Agent Trajectory Predictioncited by 9
16 more
  1. 2021Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Drivingcited by 6
  2. 2021Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Datacited by 6
  3. 2025Direct Post-Training Preference Alignment for Multi-Agent Motion Generation Models Using Implicit Feedback from Pre-training Demonstrationscited by 6
  4. 2021End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centeringcited by 5
  5. 2022Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Gamescited by 5
  6. 2023Value Approximation for Two-Player General-Sum Differential Games with State Constraintscited by 3
  7. 2024OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequencescited by 3
  8. 2022Preemptive Motion Planning for Human-to-Robot Indirect Placement Handoverscited by 2
  9. 2021Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Datacited by 1
  10. 2024Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environmentscited by 1
  11. 2022Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processescited by 0
  12. 2023Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learningcited by 0
  13. 2025Generalizable Trajectory Prediction via Inverse Reinforcement Learning with Mamba-Graph Architecturecited by 0
  14. 2025ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capturecited by 0
  15. 2026Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Studycited by 0
  16. 2026Q-based Variational Inverse Reinforcement Learningcited by 0

Similar, not cited

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  1. 2022Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
  2. 2021Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning
  3. 2020Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
  4. 2021A hierarchical behavior prediction framework at signalized intersections
  5. 2018Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
  6. 2025Conditional Prediction by Simulation for Automated Driving
  7. 2022Driving in Real Life with Inverse Reinforcement Learning
  8. 2022DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving