Efficient and Interaction-Aware Trajectory Planning for Autonomous Vehicles with Particle Swarm Optimization

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This paper introduces a novel numerical approach to achieving smooth lane-change trajectories in autonomous driving scenarios.

• Our trajectory generation approach leverages particle swarm optimization (PSO) techniques, incorporating Neural Network (NN) predictions for trajectory refinement.
• The generation of smooth and dynamically feasible trajectories for the lane change maneuver is facilitated by combining polynomial curve fitting with particle propagation, which can account for vehicle dynamics.
• The proposed planning algorithm is capable of determining feasible trajectories with real-time computation capability.