Paper Info
Title
Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis.
Abstract
Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle's driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.
Year
DOI
Venue
2020
10.3390/s20185197
SENSORS
Keywords
DocType
Volume
in-vehicle sensor,controller area network,vehicular information,reverse engineering,vehicle state estimation
Journal
20
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Beomjun Kim101.35
Yunju Baek215623.54