粒子群最適化による自動運転車の効率的で相互作用を考慮した軌道計画
Efficient and Interaction-Aware Trajectory Planning for Autonomous Vehicles with Particle Swarm Optimization
粒子群最適化とニューラルネットワーク予測を組み合わせ、車両動力学を考慮した滑らかな車線変更軌道をリアルタイムで生成する手法を提案し、シミュレーションで有効性を検証した。
著者: Lin Song, David Isele, Naira Hovakimyan, Sangjae Bae
分類: cs.RO
原文アブストラクト
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. We conduct comparative analyses with two baseline methods for lane changing, involving analytic solutions and heuristic techniques in numerical simulations. The simulation results validate the efficacy and effectiveness of our proposed approach.
