mlsanityData to 7 Oct 2026

GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving

arXiv 2206.01488 ↗First posted 3 Jun 2022cs.RO cs.AI cs.LGcited by 3

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

Applying reinforcement learning to autonomous driving entails particular challenges, primarily due to dynamically changing traffic flows. To address such challenges, it is necessary to quickly determine response strategies to the changing intentions of surrounding vehicles. This paper proposes a new policy optimization method for safe driving using graph-based interaction-aware constraints. In this framework, the motion prediction and control modules are trained simultaneously while sharing a latent representation that contains a social context. To reflect social interactions, we illustrate the movements of agents in graph form and filter the features with the graph convolution networks. This helps preserve the spatiotemporal locality of adjacent nodes. Furthermore, we create feedback loops to combine these two modules effectively. As a result, this approach encourages the learned controller to be safe from dynamic risks and renders the motion prediction robust to abnormal movements. In the experiment, we set up a navigation scenario comprising various situations with CARLA, an urban driving simulator. The experiments show state-of-the-art performance on navigation strategy and motion prediction compared to the baselines.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 19 of its 35 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

19 of 35 references.

References · 19

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2017Constrained Policy Optimizationcited by 928
  5. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 659
  6. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  7. 2018Reward Constrained Policy Optimizationcited by 397
  8. 2015Risk-Constrained Reinforcement Learning with Percentile Risk Criteriacited by 320
  9. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
  10. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  11. 2021Multimodal Motion Prediction with Stacked Transformerscited by 207
  12. 2018Graph Convolutional Reinforcement Learningcited by 148
7 more
  1. 2019GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Drivingcited by 73
  2. 2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
  3. 2019Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Drivingcited by 52
  4. 2019Worst Cases Policy Gradientscited by 52
  5. 2018Deep Reinforcement Learning for Autonomous Drivingcited by 36
  6. 2018Intent-aware Multi-agent Reinforcement Learningcited by 12
  7. 2021Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehiclescited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowdscited by 6
  2. 2023iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learningcited by 5
  3. 2025Game-Theoretic Risk-Shaped Reinforcement Learning for Safe Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
  2. 2024Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation
  3. 2021Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
  4. 2023Robust Driving Policy Learning with Guided Meta Reinforcement Learning
  5. 2025Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
  6. 2022Cooperative Behavior Planning for Automated Driving using Graph Neural Networks
  7. 2021Learning Interactive Driving Policies via Data-driven Simulation
  8. 2026COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems