Trajectory Planning for Autonomous Vehicle Using Digital Map

Bajić, Jure; Herceg, Marijan; Resetar, Ivan; Velikic, Ivan

doi:10.1109/zinc.2019.8769382

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…A hierarchical planning trajectory planning algorithm for autonomous driving based on artificial potential field method and optimization is proposed. The main structure of the hierarchical planning algorithm is shown in Figure 1, where the global reference path, obstacle information, and vehicle position information are input to the path planning module and expressed on the Frenet coordinate system (Bajic et al, 2019). The path planner consists of three main parts: convex space solution, numerical optimization, and improved artificial potential field method.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

Qin

Liu

Guo

et al. 2023

Transactions of the Institute of Measurement and Control

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To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.

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Section: Introductionmentioning

confidence: 99%