Research on Evaluation Method of Lane Departure Warning System Tongji Universtiy

Format:

Book

Conference/Event

Author/Creator:

Han, Dashuang, author.

Contributor:

Ma, Zhixiong

Yan, Yilin

Zhu, Xichan

Conference Name:

WCX SAE World Congress Experience (2020-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

Based on FOT data of a Chinese automobile company, this paper aims to study the practical role of lane departure warning system. The data of this automobile company collects a total of 32.29 hours of test data, including vehicle control, lane line and other relevant information, FOT data included both test groups and contrast groups. This paper designs research questions for the development purpose of LDW system: whether the LDW system can affect driver behavior or vehicle performance to improve road safety. To solve this problem, a hypothesis is proposed: due to the role of LDW system, in the test group and contrast group, the driving safety of the test group is higher than that of the benchmark group. According to the research hypothesis, three analysis indexes of the test are determined and defined: the number of road deviation, the time of road deviation and the maximum distance of road deviation, which are collectively referred to as safety and benefit indexes. Through data screening and processing, a total of 302 test conditions and 589 contrast conditions were extracted for the verification and analysis of LDW system. In the process of verification and analysis, the working threshold and corresponding driver performance of LDW system of test vehicle are firstly analyzed. Then, the analysis indexes are calculated to obtain the distribution of safety and benefit indexes of the benchmark group and the test group, and preliminarily verify the research hypothesis of LDW system, that is, LDW system has certain effect on the improvement of driving safety, but the influence is not significant. Support vector machine classifier training is carried out in two groups of test conditions, and the obtained classifier showed that the distribution difference between the two groups of test conditions was not obvious, which further verified the research hypothesis

Notes:

Vendor supplied data

Publisher Number:

2020-01-1032

Access Restriction:

Restricted for use by site license