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.

翻译：本文提出一种新颖的数值方法，用于在自动驾驶场景中实现平滑换道轨迹。我们的轨迹生成方法采用粒子群优化技术，并结合神经网络预测进行轨迹精化。通过将多项式曲线拟合与粒子传播相结合，可以生成换道操作中平滑且符合动力学约束的轨迹，其中粒子传播能够考虑车辆动力学特性。所提出的规划算法具备实时计算能力，能够确定可行轨迹。我们通过数值模拟，与两种基线换道方法进行了对比分析：解析解方法与启发式技术。仿真结果验证了我们所提方法的有效性和高效性。