This article proposes a motion planning approach for autonomous electric vehicles to generate an appropriate planned path according to the time-varying surrounding information. This approach utilizes the proposed novel sine resistance network to mesh the road with the aim of improving the planned path smoothness, which has the capability of generating a continuous-curvature planned path that contributes to tracking and reducing the jerkiness. Meanwhile, considering that the classical artificial potential field (APF) method is only suitable for the static scenarios, a bias oval APF is constructed to predict the change of relative distance between the ego vehicle and each obstacle by taking the speed information into account. The proposed planning approach can ensure that the planned path is collision-free in dynamic environments and the generated path is smooth simultaneously. Cosimulation results in CarSim and MATLAB/Simulink are provided to prove the advantage and feasibility of the proposed motion planning approach for autonomous electric vehicles.

中文翻译：

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动态环境下自主电动汽车基于正弦电阻网络的运动规划方法


本文提出了一种自动驾驶电动汽车的运动规划方法，根据时变的周围信息生成适当的规划路径。该方法利用所提出的新型正弦电阻网络对道路进行网格划分，目的是提高规划路径的平滑度，该方法能够生成有助于跟踪和减少抖动的连续曲率规划路径。同时，考虑到经典的人工势场（APF）方法仅适用于静态场景，因此构造了一个偏置椭圆形APF，通过考虑速度信息来预测自车与每个障碍物之间的相对距离的变化。所提出的规划方法可以确保规划路径在动态环境中无碰撞，同时生成的路径是平滑的。 CarSim 和 MATLAB/Simulink 中的协同仿真结果证明了所提出的自动驾驶电动汽车运动规划方法的优势和可行性。