Abstract

Background: To address the problem of high energy consumption of self-driving electric vehicles when following a planned trajectory, constraints are added to path planning and speed planning respectively.

Methods: Given the limitations of the existing path planning algorithms in terms of search efficiency and path length, this study introduces an innovative and improved strategy in the horizontal dimension. Based on the cost function of the distance between sampling points, this strategy aims to improve the search efficiency of the dynamic planning algorithm and reduce the search path length. Furthermore, the smoothness of the path is optimized to suit the actual driving conditions by applying a quadratic programming algorithm. An energy consumption model for pure electric vehicles is established in the vertical dimension, effectively constraining energy use during speed dynamic planning to reduce consumption while driving. Finally, the smoothness of speed planning is improved using a quadratic programming algorithm.

Results: The results of simulation experiments show that compared with traditional methods, the proposed algorithm achieves a substantial improvement in path length reduction of 5.8%, average curvature reduction of 31.6%, and average energy consumption reduction of 2.04% in static and dynamic obstacle avoidance environments.

Conclusion: The results show that the improved dynamic planning algorithm proposed in this study is significantly optimized in terms of mean path length, mean curvature, and energy consumption. Moreover, the proposed algorithm can meet the requirements of energy efficiency of vehicle driving.

Keywords: autonomous driving, trajectory planning, trajectory optimization, dynamic programming algorithm, discrete optimization, energy optimization.