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2000
Volume 19, Issue 1
  • ISSN: 2212-7976
  • E-ISSN: 1874-477X

Abstract

Background

In autonomous driving systems, the planning module serves as the link between environment perception and vehicle control, directly influencing the safety and efficiency of autonomous driving. Despite the existence of numerous patents and publications related to trajectory planning, there is still room for improvement in the economic efficiency of trajectory planning.

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.

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2024-12-12
2025-12-09

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Energy-saving Trajectory Planning Method for Electric Vehicles Based on Dynamic Programming Optimization
Recent Patents on Mechanical Engineering 19, 62 (2026); https://doi.org/10.2174/0122127976349832241025112134
/content/journals/meng/10.2174/0122127976349832241025112134
/content/journals/meng/10.2174/0122127976349832241025112134
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  • Article Type:     Research Article
Keyword(s): Autonomous driving; discrete optimization; dynamic programming algorithm; energy optimization; trajectory optimization; trajectory planning

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