Prediction of Road Slope Ahead of Vehicles Based on Data Fusion and Data Mining Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Sun, Meng, author.

Contributor:

Chen, Lingtao

Liu, Li

Tan, Gangfeng

Tian, Zhongpeng

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Heavy commercial vehicle drivers may frequently shift gears when they are running on long and downhill roads in mountainous area. In order to improve driving safety and fuel economy, it is necessary to predict the slope of the road ahead in real time and correct the driver's shift strategy in time. At present, the road slope estimation is mainly based on the real-time estimation of the road slope at the current position of the vehicle based on the vehicle driving information obtained by the sensors, but the road slope of the road section that the vehicle is about to reach has not been predicted. In this paper, based on the road slope information of the road section that the driver has driven through, combined with Geographic Information System (GIS) information and road design standards, the slope of the road section ahead is predicted. GIS information and road design standards are used to predict the overall situation of a section of road, and the slope of the first part of the section is assumed to be known. Based on these, data fusion and data mining methods are used to predict the slope of the road ahead in advance. By predicting the slope of the road ahead in advance, drivers can get advance information about the road ahead and adjust or maintain current driving strategies to improve driving safety and fuel economy

Notes:

Vendor supplied data

Publisher Number:

2021-01-0910

Access Restriction:

Restricted for use by site license