Nowadays, the increasing car accidents ask for the better driver behavior analysis and risk assessment for travel safety, auto insurance pricing and smart city applications. Traditional approaches largely use GPS data to assess drivers. However, it is difficult to fine-grained assess the time-varying driving behaviors. In this paper, we employ the increasingly popular On-Board Diagnostic (OBD) equipment, which measures semantic-rich vehicle information, to extract detailed trajectory and behavior data for analysis. We propose PBE system, which consists of Trajectory Profiling Model (PM), Driver Behavior Model (BM) and Risk Evaluation Model (EM). PM profiles trajectories for reminding drivers of danger in real-time. The labeled trajectories can be utilized to boost the training of BM and EM for driver risk assessment when data is incomplete. BM evaluates the driving risk using fine-grained driving behaviors on a trajectory level. Its output incorporated with the time-varying pattern, is combined with the driver-level demographic information for the final driver risk assessment in EM. Meanwhile, the whole PBE system also considers the real-world cost-sensitive application scenarios. Extensive experiments on the real-world dataset demonstrate that the performance of PBE in risk assessment outperforms the traditional systems by at least 21%.
|Title of host publication||Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings|
|Editors||Ulf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio|
|Publisher||Springer-Verlag GmbH and Co. KG|
|Number of pages||17|
|Publication status||Published - 2019|
|Event||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland|
Duration: 10 Sep 2018 → 14 Sep 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018|
|Period||10/09/18 → 14/09/18|
Bibliographical noteThis research was supported by Shenzhen Peacock Talent Grant 827-000175, Guangdong Pre-national Project 2014GKXM054, the University of Macau Start-up Research Grant (SRG2015-00050-FST) and Research & Development Grant for Chair Professor (CPG2015-00017-FST), and Natural Science Foundation of China: 61572488 and 61673241.
- Driver behavior analysis
- On-board diagnostic (OBD)