|Table of Contents|

Dynamic obstacle avoidance control of intelligent vehicle on large curvature roads considering trajectory prediction(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年04期
Page:
161-174
Research Field:
机械与汽车工程
Publishing date:
2024-07-10

Info

Title:
Dynamic obstacle avoidance control of intelligent vehicle on large curvature roads considering trajectory prediction
Author(s):
SHI Pei-long1;  WANG Cai-rui1; 2;  MA Qiang1;  LIU Rui1;  ZHAO Xuan1
(1. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China; 2. BYD Auto Shanghai, Shanghai 201611, China)
Keywords:
automotive engineering;  trajectory prediction;  model predictive control;  tracking control;  obstacle avoidance trajectory planning;  large curvature road condition
PACS:
U461.5
DOI:
10.19721/j.cnki.1671-8879.2024.04.015
Abstract:
In order to improve the dynamic obstacle avoidance safety of intelligent vehicles on high curvature roads, a trajectory prediction method based on real-time trajectory update long short term memory(U-LSTM)neural network and a dynamic obstacle avoidance control method for intelligent vehicles based on fuzzy control replanning logic strategy was proposed. A trajectory prediction model for U-LSTM neural network was established by extracting large curvature road parameters from the MATLABtoolbox, utilizing the current time-domain real trajectory point information, and training an updated iterative method. Considering the nonlinear relationship between intelligent vehicle tracking, avoidance, and surrounding information in high curvature road scenes, a replanning obstacle avoidance logic strategy based on fuzzy control is proposed. Dynamic programming and quadratic programming algorithms, as well as S-T graph method, was used to optimize the trajectory tracking and speed control of local paths. By establishing tracking error models and speed tracking models, vehicle lateral and longitudinal control was achieved. An MPC path tracking and speed tracking controller for vehicle lateral and longitudinal control was designed, and a joint simulation model was constructed to verify the rationality and effectiveness of trajectory prediction and control methods. The results show that the intelligent vehicle dynamic obstacle avoidance control method proposed in the article can accurately predict the vehicle's trajectory on high curvature roads, and the real-time trajectory U-LSTM neural network can effectively improve prediction accuracy. The replanning obstacle avoidance logic strategy can effectively avoid dynamic obstacles and meet the accuracy of vehicle longitudinal and lateral tracking control, ensuring the driving stability of the vehicle.7 tabs, 20 figs, 25 refs.

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Last Update: 2024-07-10