mlsanityData to 7 Oct 2026

Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions

arXiv 2402.04168 ↗First posted 6 Feb 2024cs.LG cs.CV cs.ROcited by 4

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Abstract (arXiv)

Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action space and unstructured reward designs which lack structure. In this work, we introduce Informed Reinforcement Learning, where a structured rulebook is integrated as a knowledge source. We learn trajectories and asses them with a situation-aware reward design, leading to a dynamic reward which allows the agent to learn situations which require controlled traffic rule exceptions. Our method is applicable to arbitrary RL models. We successfully demonstrate high completion rates of complex scenarios with recent model-based agents.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

21 of 43 references.

References · 21

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2023Mastering Diverse Domains through World Modelscited by 1,193
  3. 2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 158
  4. 2021Reward (Mis)design for Autonomous Drivingcited by 102
  5. 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 101
  6. 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 41
  7. 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 38
  8. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  9. 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 30
  10. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  11. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
  12. 2022One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Drivingcited by 16
9 more
  1. 2019Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarioscited by 12
  2. 2020Reinforcement Learning with Iterative Reasoning for Merging in Dense Trafficcited by 10
  3. 2018Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic Statescited by 8
  4. 2023Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learningcited by 7
  5. 2019Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Trafficcited by 5
  6. 2021Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spacescited by 5
  7. 2020Safe Trajectory Planning Using Reinforcement Learning for Self Drivingcited by 2
  8. 2022Quantification of Actual Road User Behavior on the Basis of Given Traffic Rulescited by 2
  9. 2016A Fuzzy Approach to Qualification in Design Exploration for Autonomous Robots and Systemscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learningcited by 2
  2. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Drivingcited by 1
  3. 2024An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Drivingcited by 0
  4. 2026Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Drivingcited by 0