mlsanityData to 14 Oct 2026

Generalizable Trajectory Prediction via Inverse Reinforcement Learning with Mamba-Graph Architecture

arXiv 2506.12474 ↗First posted 14 Jun 2025cs.LG cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Video, motion and generative Mamba models

also in 2 more
Abstract (arXiv)

Accurate driving behavior modeling is fundamental to safe and efficient trajectory prediction, yet remains challenging in complex traffic scenarios. This paper presents a novel Inverse Reinforcement Learning (IRL) framework that captures human-like decision-making by inferring diverse reward functions, enabling robust cross-scenario adaptability. The learned reward function is utilized to maximize the likelihood of output by integrating Mamba blocks for efficient long-sequence dependency modeling with graph attention networks to encode spatial interactions among traffic agents. Comprehensive evaluations on urban intersections and roundabouts demonstrate that the proposed method not only outperforms various popular approaches in terms of prediction accuracy but also achieves 2.3 times higher generalization performance to unseen scenarios compared to other baselines, achieving adaptability in Out-of-Distribution settings that is competitive with fine-tuning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 19 of its 32 references that are in the map. First posted on arXiv 14 Jun 2025.

Builds on

19 of 32 references.

References · 19

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2021How Attentive are Graph Attention Networks?cited by 835
  5. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  6. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  7. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  8. 2021Multimodal Motion Prediction with Stacked Transformerscited by 208
  9. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  10. 2018Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecturecited by 55
  11. 2024DeciMamba: Exploring the Length Extrapolation Potential of Mambacited by 49
  12. 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learningcited by 46
7 more
  1. 2018Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learningcited by 28
  2. 2024DGMamba: Domain Generalization via Generalized State Space Modelcited by 21
  3. 2019Social and Scene-Aware Trajectory Prediction in Crowded Spacescited by 19
  4. 2021Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Drivingcited by 19
  5. 2023MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEscited by 19
  6. 2019Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning Frameworkcited by 2
  7. 2024Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamicscited by 2

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  1. 2020Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
  2. 2025GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
  3. 2022Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
  4. 2025Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics
  5. 2019Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
  6. 2022Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search
  7. 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
  8. 2021Objective-aware Traffic Simulation via Inverse Reinforcement Learning