mlsanityData to 7 Oct 2026

LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty

arXiv 2209.11422 ↗First posted 23 Sep 2022cs.LG cs.ROcited by 9

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

Abstract (arXiv)

Uncertainty on human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision processes (POMDPs) offer a principled framework for planning under uncertainty, often leveraging Monte Carlo sampling to achieve online performance for complex tasks. However, sampling also raises safety concerns by potentially missing critical events. To address this, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), that learns to attend to critical human behaviors during planning. LEADER learns a neural network generator to provide attention over human behaviors in real-time situations. It integrates the attention into a belief-space planner, using importance sampling to bias reasoning towards critical events. To train the algorithm, we let the attention generator and the planner form a min-max game. By solving the min-max game, LEADER learns to perform risk-aware planning without human labeling.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 20 of its 35 references that are in the map. First posted on arXiv 23 Sep 2022.

Builds on

20 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Closing the Planning-Learning Loop with Application to Autonomous Driving11 Jan

Other references · 19

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  4. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 347
  5. 2016DESPOT: Online POMDP Planning with Regularizationcited by 105
  6. 2017Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learningcited by 86
  7. 2018Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulationcited by 76
  8. 2022Safe Reinforcement Learning by Imagining the Near Futurecited by 67
  9. 2019SUMMIT: A Simulator for Urban Driving in Massive Mixed Trafficcited by 39
  10. 2020Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learningcited by 19
  11. 2018HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertaintycited by 18
  12. 2020MAGIC: Learning Macro-Actions for Online POMDP Planningcited by 17
7 more
  1. 2020Re-understanding Finite-State Representations of Recurrent Policy Networkscited by 15
  2. 2021Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Resultscited by 14
  3. 2019LeTS-Drive: Driving in a Crowd by Learning from Tree Searchcited by 13
  4. 2016Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planningcited by 10
  5. 2019GAMMA: A General Agent Motion Model for Autonomous Drivingcited by 7
  6. 2019Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Versioncited by 6
  7. 2019A behavior driven approach for sampling rare event situations for autonomous vehiclescited by 2

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 3; 2025: 2.

9 papers.

  1. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  2. 2023Model-Based Runtime Monitoring with Interactive Imitation Learningcited by 37
  3. 2023Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulationcited by 10
  4. 2024Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approachcited by 7
  5. 2024Quantum Artificial Intelligence: A Brief Surveycited by 3
  6. 2025Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planningcited by 3
  7. 2024Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environmentscited by 1
  8. 2023The Planner Optimization Problem: Formulations and Frameworkscited by 0
  9. 2025HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers
  2. 2024Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving
  3. 2022Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving
  4. 2022Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning
  5. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
  6. 2025Estimating cognitive biases with attention-aware inverse planning
  7. 2021How To Not Drive: Learning Driving Constraints from Demonstration
  8. 2026Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving