Physics-informed residual reinforcement learning via expert prior knowledge for safe and efficient autonomous merging

This publication has 37 references indexed in Scilit:

• Data-Driven Vehicle Modeling for Path Tracking Based on the Combination of a Neural Network and Kinematics Model
  Automotive Innovation, 2025
• Fear-constrained personalized and anthropomorphic reinforcement learning for autonomous car-following
  Knowledge-Based Systems, 2025
• A dual-layer path planning approach for ramp merging with integrated risk management
  Expert Systems with Applications, 2025
• Autonomous vehicle extreme control for emergency collision avoidance via Reachability-Guided reinforcement learning
  Advanced Engineering Informatics, 2024
• Recent progress, challenges and future prospects of applied deep reinforcement learning : A practical perspective in path planning
  Neurocomputing, 2024
• Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles
  Automotive Innovation, 2024
• A deep reinforcement learning-based approach for autonomous lane-changing velocity control in mixed flow of vehicle group level
  Expert Systems with Applications, 2023
• A cooperative control method for safer on-ramp merging process in heterogeneous traffic flow
  Accident Analysis & Prevention, 2023
• Deep Reinforcement Learning Based Decision-Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments
  Automotive Innovation, 2023
• On-Ramp Merging for Highway Autonomous Driving: An Application of a New Safety Indicator in Deep Reinforcement Learning
  Automotive Innovation, 2023