Energy-Efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles: A Simulation Study
This paper proposes a novel energy-efficient trajectory planning strategy for autonomous electric vehicles (EVs) called Energy-efficient Hybrid Model Predictive Planner (EHMPP), which optimizes energy recovery during deceleration and improves overall energy efficiency by considering optimal acceleration and speed profiles.
A Framework for Planning Time-Efficient Overtaking Trajectories in Autonomous Vehicles Using Spatio-temporal Topology and Reachable Set Analysis
This paper proposes a novel framework for planning time-efficient and safe overtaking trajectories for autonomous vehicles by combining spatio-temporal topological search with reachable set analysis to improve upon the limitations of traditional hierarchical planning methods.
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.
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.
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.
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.
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