mlsanityData to 7 Oct 2026

Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey

arXiv 2501.01886 ↗First posted 3 Jan 2025cs.RO cs.AI cs.SYcited by 1

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

Abstract (arXiv)

Autonomous vehicles (AVs) can significantly promote the advances in road transport mobility in terms of safety, reliability, and decarbonization. However, ensuring safety and efficiency in interactive during within dynamic and diverse environments is still a primary barrier to large-scale AV adoption. In recent years, deep reinforcement learning (DRL) has emerged as an advanced AI-based approach, enabling AVs to learn decision-making strategies adaptively from data and interactions. DRL strategies are better suited than traditional rule-based methods for handling complex, dynamic, and unpredictable driving environments due to their adaptivity. However, varying driving scenarios present distinct challenges, such as avoiding obstacles on highways and reaching specific exits at intersections, requiring different scenario-specific decision-making algorithms. Many DRL algorithms have been proposed in interactive decision-making. However, a rationale review of these DRL algorithms across various scenarios is lacking. Therefore, a comprehensive evaluation is essential to assess these algorithms from multiple perspectives, including those of vehicle users and vehicle manufacturers. This survey reviews the application of DRL algorithms in autonomous driving across typical scenarios, summarizing road features and recent advancements. The scenarios include highways, on-ramp merging, roundabouts, and unsignalized intersections. Furthermore, DRL-based algorithms are evaluated based on five rationale criteria: driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI). Each criterion of DDTUI is specifically analyzed in relation to the reviewed algorithms. Finally, the challenges for future DRL-based decision-making algorithms are summarized.

Key facts. Cited by 1 paper 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 42 of its 174 references that are in the map. First posted on arXiv 3 Jan 2025.

Builds on

42 of 174 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts15 Aug
  2. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving25 Mar

Other references · 40

  1. 2018Sanity Checks for Saliency Mapscited by 1,149
  2. 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 467
  3. 2023DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Modelcited by 455
  4. 2017Interpretation of Neural Networks is Fragilecited by 443
  5. 2021Bottleneck Transformers for Visual Recognitioncited by 364
  6. 2021ResMLP: Feedforward networks for image classification with data-efficient trainingcited by 338
  7. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  8. 2019Self-Driving Cars: A Surveycited by 147
  9. 2019Sanity Checks for Saliency Metricscited by 119
  10. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 96
  11. 2021Explanations in Autonomous Driving: A Surveycited by 80
  12. 2017Beyond Sparsity: Tree Regularization of Deep Models for Interpretabilitycited by 68
28 more
  1. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  2. 2021Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Trafficcited by 38
  3. 2022A Survey on Automated Driving System Testing: Landscapes and Trendscited by 36
  4. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  5. 2022OCTET: Object-aware Counterfactual Explanationscited by 28
  6. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
  7. 2019Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Drivingcited by 21
  8. 2019Cooperation-Aware Reinforcement Learning for Merging in Dense Trafficcited by 20
  9. 2019Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networkscited by 20
  10. 2017Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Mergecited by 17
  11. 2020Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersectionscited by 15
  12. 2022Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersectionscited by 15
  13. 2020Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theorycited by 8
  14. 2023SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Drivingcited by 8
  15. 2024A Rule-Based Behaviour Planner for Autonomous Drivingcited by 7
  16. 2020Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learningcited by 6
  17. 2020NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasetscited by 6
  18. 2024Enhanced Visual SLAM for Collision-free Driving with Lightweight Autonomous Carscited by 6
  19. 2023A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)cited by 5
  20. 2020Intelligent Roundabout Insertion using Deep Reinforcement Learningcited by 4
  21. 2020From Simulation to Real World Maneuver Execution using Deep Reinforcement Learningcited by 4
  22. 2022Coordination for Connected and Automated Vehicles at Non-signalized Intersections: A Value Decomposition-based Multiagent Deep Reinforcement Learning Approachcited by 4
  23. 2022Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Methodcited by 3
  24. 2024Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Drivingcited by 3
  25. 2018Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Trafficcited by 2
  26. 2022COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learningcited by 2
  27. 2023Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluationcited by 2
  28. 2021Spatio-Temporal Lattice Planning Using Optimal Motion Primitivescited by 1

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