Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in
improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of
advanced functions such as driver safety monitoring systems and adaptive driving assistance systems.
In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognition system
by learning from non-intrusive, easily accessible vehicle related measurements and its validation using
real-world driving data. Compared to conventional approaches, this paper first highlights the necessities of adopting a personalized system by analysing feature distribution of individual driver’s data
and all drivers’ data via advanced data visualization and statistical analysis. If significant differences
are identified, a dedicated personalized model is learnt to predict the driver’s driving state. Spearman
distance is also drawn to evaluate the differences between individual driver’s data and all drivers’
data in a quantitative manner. In addition, five categories of classifiers are tested and compared to
identify a suitable one for classification, where random forest with Bayesian parameter optimization
outperforms others and therefore is adopted in this paper. A recently collected dataset from real-world
driving experiments is adopted to evaluate the proposed system. Comparative experimental results
indicate that the personalized learning system with road information significantly outperforms conventional approaches without considering personalized characteristics or road information, where the
overall accuracy increases from 81.3% to 91.6%. It is believed that the newly developed personalized
learning system can find a wide range of applications where diverse behaviours exist.
Funding
This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.
History
School
Architecture, Building and Civil Engineering
Published in
Transportation Research Part C: Emerging Technologies
Volume
105
Pages
241 - 261
Citation
YI, D. ... et al., 2019. A machine learning based personalized system for driving state recognition. Transportation Research Part C: Emerging Technologies, 105, August 2019, pp. 241-261.
This paper was accepted for publication in the journal Transportation Research Part C: Emerging Technologies and the definitive published version is available at https://doi.org/10.1016/j.trc.2019.05.042.