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Abstract
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OBJECTIVE: In order to analyze specific events while driving (such as a safety critical event [SCE] or secondary task), we were interested in studying whether driving behavior was unusual around the event. An indicator characterizing driving style (driving style indicator [DSI]) was estimated for each driver by using naturalistic data. The analysis of the gap in driving style could be calculated for a specific trip or even a time window and could help characterize events: a more risky situation when DSI was above average, increase in safety margins when under average.
METHODS: Lateral acceleration and longitudinal acceleration were used for DSI calculation. The first step consisted in filtering the signal acquired by the inertial measurement unit (60 Hz). The noise was filtered out with an eighth order, phase-compensated digital low-pass Butterworth filter with a cut-out frequency of 5 Hz and offsets were compensated for. The second step consisted in calculating the jerk of the acceleration in lateral and longitudinal directions for each trip. The third step summarized the distribution of these jerks for all trips of each driver. Finally, the DSI was defined as the standard deviation of this distribution. A driver was represented by lateral DSI and longitudinal DSI.
RESULTS: The indicator was used on French pilot data (10 drivers) and on UK data (30 drivers) from the UDRIVE project. To assess this indicator, tests on track were conducted by professional drivers simulating two opposite driving style. The first results were promising and discriminated a smooth from a rough driving style. Indeed, in the pilot data, the classification was in accordance with our expectations and confirmed by videos. The same kind of distribution was observed in the UK data and needs to be confirmed when the UDRIVE database is complete.
CONCLUSION: DSI is a new parameter that will be used to define clusters of drivers and study variation of driving parameters in each class during selected events (SCE, secondary task, etc.) in the UDRIVE project.
Peer-reviewed paper from the 61st Annual Scientific Conference of the Association for the Advancement of Automotive Medicine (AAAM), October 2017
Language: en |