Citation

Yang W, Zheng L, Li Y, Ren Y, Xiong Z. IEEE Trans. Intel. Transp. Syst. 2020; 21(6): 2350-2359.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2019.2918117

PMID

unavailable

Abstract

In the background of autonomous driving at level 3 to level 4, an automated vehicle should own smarter driving brain to face complicated transportation situations. In order to construct a safe automated driving brain under highway conditions, this paper focused on driving motion decision in order to generate the control target parameters in the time domain. The coordinate transformation was proposed to convert the complicated curving road to local straight coordinate or inverse, then a receding horizon programming based on mixed logic dynamic constraints was established to formulate a safe-guaranteed optimization model, where the objectives were assigned by driver's steering wheel and speed control, as well as the lateral lane tracking performance. Based on the motion optimization model, the links to the driver characteristics were analyzed, and the weight for each objective in optimization model was tuned by driver statistical features, in which the entropy weights, variance weights, and unique weights are compared. The simulation based on the simulating driving scenario was developed and the optimization results validated the safety and feasibility of motion decision and with the help of k-nearest neighbors (KNN) classifier, the clustering prediction results qualitatively revealed the proposed weights tuning methods for objectives in optimization model could better determine a human-like driving decision, furthermore, this paper gave a basis to compromise multi-objectives in driving decision.

Language: en