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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Research Article

Motion Planning for Autonomous Vehicles Based on Dynamic Bio-inspired Neural Network Model

Author(s): Jia Yan Song, Shengchao Su* and Chen Yang

Volume 17, Issue 2, 2023

Published on: 27 May, 2022

Article ID: e010422203001 Pages: 14

DOI: 10.2174/1872212116666220401153227

Price: $65

Abstract

Background: Nowadays, with the increase of the number of cars, traffic accidents occur frequently, improving the active safety of cars, and traffic safety has become an inevitable requirement of the development of intelligent transportation.

Objective: Motion planning for autonomous driving is a challenging problem due to the uncertainty of the unknown complex environment. Conventional methods may fail in some cases on account of poor real-time performance and instability. The objective is to propose a motion planning method to plan a reasonable path autonomously for an autonomous vehicle in unknown environments and ensure obstacle avoidance.

Methods: The solution is to use the improved Glasius Bio-inspired Neural Network (GBNN) to plan a feasible route for the autonomous vehicle, which adaptively changes the direction of movement according to the changes of neuron values. Neuron connection weight in the neural network is reasonably improved to increase the update rate of activity value, so that this model can more adapt to the changes of dynamic environment. Meanwhile, the planned trajectory is fitted to meet vehicle kinematics model.

Results: Unstructured and structured roads are tested in static and dynamic environments respectively. During the test, static obstacles and surrounding vehicles’ intentions are all generated randomly. In this way, the generated route can be optimal and adapt to the stochastic as well.

Conclusion: Simulation results in unstructed road and structed road show the effectiveness of the proposed algorithm in autonomous motion planning. In addition, the performance of path length, smoothness and turning angle has been improved.

Keywords: Autonomous driving, motion planning, glasius bio-inspired neural network (GBNN), cohen stability, collision avoidance, bezier curve.

Graphical Abstract

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