Vehicle sensor data-based analysis on the driving style differences between operating indoor simulator and on-road instrumented vehicle

Abstract

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus–response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models.

Keywords:

• Driving style
• instrumented vehicle
• nonnegative matrix factorization
• simulator
• VSD

Acknowledgment

The authors would like to thank Dr Simon Box, Dr Rich C. Mcllroy (University of Southampton), Dr Mark Brackstone, Mrs Karen Ghali (University of Southampton), Prof Jianping Wu (Tsinghua University), Dr Ming Xu (Tsinghua University), and Terigele (Dalian Medical University) for their valuable insights on the research and kind support in the preparation of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research reported in this paper is supported by National Natural Science Foundation of China (Grant nos. 71621001 and 71471014).