Work

A Novel Lane Change Decision-Making Model of Autonomous Vehicle Based on Support Vector Machine

Year: 2019

Type: article

Abstract: Autonomous driving is a crucial issue of the automobile industry, and research on lane change is its significant part. Previous works on the autonomous vehicle lane change mainly focused on lane change path planning and path tracking, but autonomous vehicle lane change decision making is rarely mentioned. Therefore, this paper establishes an autonomous lane change decision-making model based on benefit, safety, and tolerance by analyzing the factors of the autonomous vehicle lane change. Then, because of the multi-parameter and non-linearity of the autonomous lane change decision-making process, a support vector machine (SVM) algorithm with the Bayesian parameters optimization is adopted to solve this problem. Finally, we compare a lane change model based on rules with the proposed SVM model in the test set, and results illustrate that the SVM model performs better than the rule-based lane change model. Moreover, the real car experiment is carried out to verify the effectiveness of the decision model.

Source: IEEE Access

DOI: 10.1109/access.2019.2900416

Authors: Yonggang Liu, Xiao Wang, Liang Li, Shuo Cheng, Zheng Chen

Institutions: Chongqing University, State Key Laboratory of Mechanical Transmission, Tsinghua University, Kunming University of Science and Technology

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,750