mlsanityData to 7 Oct 2026

Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning

arXiv 2312.05784 ↗First posted 10 Dec 2023cs.AI cs.ROcited by 3

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

Abstract (arXiv)

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to the dynamic behavior of traffic participants and weather conditions. A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between traffic participants with contextual information and provides a decision for safe maneuver for AV. A spatio-temporal graph models the interactions between traffic participants for predicting the future trajectories of those participants. The predicted trajectories are utilized to generate a future occupancy map around the AV with uncertainties embedded to anticipate the evolving non-stationary driving environments. Then the contextual information and future occupancy maps are input to the policy network of the GP3Net framework and trained using Proximal Policy Optimization (PPO) algorithm. The proposed GP3Net performance is evaluated on standard CARLA benchmarking scenarios with domain shifts of traffic patterns (urban, highway, and mixed). The results show that the GP3Net outperforms previous state-of-the-art imitation learning-based planning models for different towns. Further, in unseen new weather conditions, GP3Net completes the desired route with fewer traffic infractions. Finally, the results emphasize the advantage of including the prediction module to enhance safety measures in non-stationary environments.

Key facts. Cited by 3 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 17 of its 29 references that are in the map. First posted on arXiv 10 Dec 2023.

Builds on

17 of 29 references.

References · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  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. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  5. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  6. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  7. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  8. 2018TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agentscited by 111
  9. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
  10. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 96
  11. 2022COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehiclescited by 95
  12. 2018Deep Imitative Models for Flexible Inference, Planning, and Controlcited by 75
5 more
  1. 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 30
  2. 2020Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusioncited by 23
  3. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 20
  4. 2019Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environmentscited by 9
  5. 2020DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networkscited by 9

Built on by

Cited by year

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

3 papers.

  1. 2025Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptationcited by 2
  2. 2024Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environmentcited by 1
  3. 2025Learning Safe Autonomous Driving Policies Using Predictive Safety Representationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
  2. 2021Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
  3. 2024Self-Driving Car Racing: Application of Deep Reinforcement Learning
  4. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
  5. 2025Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
  6. 2023Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
  7. 2023Deep Occupancy-Predictive Representations for Autonomous Driving
  8. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment