Social-Aware Trajectory Planning for Autonomous Vehicles in Complex Interaction Scenarios
The proposed S4TP framework integrates social-aware trajectory prediction and driving risk field modeling to enable safe, efficient, and socially appropriate trajectory planning for autonomous vehicles in complex traffic scenarios involving frequent interactions with human-driven vehicles.
Trajectory Planning for Autonomous Vehicles Using Iterative Reward Prediction and Uncertainty Propagation in Reinforcement Learning
A trajectory planning method for autonomous vehicles using reinforcement learning that includes iterative reward prediction to stabilize the learning process and uncertainty propagation to account for uncertainties in perception, prediction, and control.
Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles
A novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories for autonomous vehicles by incorporating road constraints and kinematic constraints.
A Semi-Decentralized Trajectory Planner for Connected and Autonomous Vehicles Based on Variational Equilibrium
This research paper proposes a novel semi-decentralized trajectory planning approach for connected and autonomous vehicles (CAVs) that leverages vehicle-to-everything (V2X) technology to improve computational efficiency and safety by achieving variational equilibrium (VE) in a game-theoretic framework.
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