Driving Style Identification Strategy Based on DS Evidence Theory Jilin University

Format:

Book

Conference/Event

Author/Creator:

Wang, Changtai, author.

Contributor:

He, Rui

Zhu, Bing

Conference Name:

WCX SAE World Congress Experience (2023-04-18 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

Driving assistance system is regarded as an effective method to improve driving safety and comfort and is widely used in automobiles. However, due to the different driving styles of different drivers, their acceptance and comfort of driving assistance systems are also different, which greatly affects the driving experience. The key to solving the problem is to let the system understand the driving style and achieve humanization or personalization. This paper focuses on clustering and identification of different driving styles. In this paper, based on the driver's real vehicle experiment, a driving data acquisition platform was built, meanwhile driving conditions were set and drivers were recruited to collect driving information. In order to facilitate the identification of driving style, the correlation analysis of driving features is conducted and the principal component analysis method is used to reduce the dimension of driving features. According to the average acceleration, the headway and reciprocal of the time to collision during driving as clustering indicators, K-means algorithm is used to define driving style as aggressive, common and conservative. Different from previous studies, this paper introduces the idea of integrated learning, takes K-Nearest Neighbor, Back Propagation neural network and Support Vector Machines as the initial classifiers, and fuses the results of each classifier through DS evidence theory to obtain the recognition model of driving style, and tests the accuracy with the test set. The results show that the multi classifier fusion identification strategy proposed in this paper can clearly and accurately identify driving style, and its accuracy can reach 95.9%. At the same time, its accuracy is significantly improved compared with a single classifier, which adds a new idea to improve the accuracy of driving style identification

Notes:

Vendor supplied data

Publisher Number:

2023-01-0587

Access Restriction:

Restricted for use by site license