Paper
15 January 2024 A trajectory planning method based on triggered planning framework for autonomous driving on unstructured roads in mining areas
Biao Xu, Lvfan Liu, Zeyu Yang, Yang Li, Manjiang Hu, Ming Gao
Author Affiliations +
Proceedings Volume 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023); 1298322 (2024) https://doi.org/10.1117/12.3017930
Event: Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 2023, Wuhan, China
Abstract
This article proposes a trajectory planning method for autonomous driving on unstructured roads in mining areas. The proposed method adopts a path and speed decoupling approach and a triggered planning framework based on different trigger events pre-designed, which has higher passability and stability when applied in some unstructured scenarios. At the path planning level, a variable lateral sampling range is utilized to accommodate the irregular boundaries of mining roads. The speed planning module initially determines the vehicle's speed planning tasks (cruising, following, stop) based on the output of path planning, which integrates decision-making into the planning process, eliminating the need for complicated state machine designs used in other methods. Additionally, the introduced triggered planning framework effectively minimizes the mutual influence between the decision planning module and other modules and improves the passability in complex scenarios. The simulation experiments demonstrate the capability of this method in accomplishing decisionmaking and planning tasks successfully on mining roads.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Biao Xu, Lvfan Liu, Zeyu Yang, Yang Li, Manjiang Hu, and Ming Gao "A trajectory planning method based on triggered planning framework for autonomous driving on unstructured roads in mining areas", Proc. SPIE 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 1298322 (15 January 2024); https://doi.org/10.1117/12.3017930
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KEYWORDS
Roads

Mining

Evolutionary algorithms

Autonomous driving

Decision making

Detection and tracking algorithms

Engineering

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