Personalized Automated Driving Decision Based on the Gaussian Mixture Model

doi:10.3901/JME.2022.16.280

Abstract

Abstract: Under the era of big data, a personalized driving decision system would further improve the safety and comfort of the intelligent vehicle with the help of the research on naturalistic driving data. An improving method for Bézier curve path is proposed to achieve the efficient path planning for intelligent vehicle, and then, the quadratic programming for speed and acceleration of ego vehicle is developed, and a speed predictive model for naturalistic driver is built based on the Gaussian process theory, which lay the foundation for collision avoidance in path planning. A personalized path assessing method is developed based on Gaussian mixture model and combining with path rationality, planning homogeneity and speed fluctuation objectives to filter the personalized and agreeable target path. With the consideration of complete control targets, an optimal controller with quadratic programming is designed, and the reference path and target control parameters for automated driving are obtained simultaneously. The naturalistic driving data could be effectively applied with the proposed methods and the driving performance such as safety, comfort, personality would be improved.

Key words: Bézier curve, path planning, Gaussian process, Gaussian mixture model, optimal control

CLC Number:

TG156

YANG Wei, ZHENG Ling, LI Yinong. Personalized Automated Driving Decision Based on the Gaussian Mixture Model[J]. Journal of Mechanical Engineering, 2022, 58(16): 280-289.

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