PBE : driver behavior assessment beyond trajectory profiling

Bing HE, Xiaolin CHEN, Dian ZHANG*, Siyuan LIU, Dawei HAN, Lionel M. NI

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

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%.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings
EditorsUlf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
PublisherSpringer-Verlag GmbH and Co. KG
Pages507-523
Number of pages17
ISBN (Print)9783030109967
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland
Duration: 10 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11053 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
CountryIreland
CityDublin
Period10/09/1814/09/18

Fingerprint

Profiling
Driver
Trajectories
Trajectory
Risk Assessment
Evaluation Model
Risk assessment
Time-varying
Risk Evaluation
Model Evaluation
Accidents
Insurance
Model
Pricing
Diagnostics
Global positioning system
Safety
Costs
Railroad cars
Real-time

Bibliographical note

This 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.

Keywords

  • Driver behavior analysis
  • On-board diagnostic (OBD)

Cite this

HE, B., CHEN, X., ZHANG, D., LIU, S., HAN, D., & NI, L. M. (2019). PBE : driver behavior assessment beyond trajectory profiling. In U. Brefeld, A. Marascu, F. Pinelli, E. Curry, B. MacNamee, N. Hurley, E. Daly, ... M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings (pp. 507-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-030-10997-4_31
HE, Bing ; CHEN, Xiaolin ; ZHANG, Dian ; LIU, Siyuan ; HAN, Dawei ; NI, Lionel M. / PBE : driver behavior assessment beyond trajectory profiling. Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings. editor / Ulf Brefeld ; Alice Marascu ; Fabio Pinelli ; Edward Curry ; Brian MacNamee ; Neil Hurley ; Elizabeth Daly ; Michele Berlingerio. Springer-Verlag GmbH and Co. KG, 2019. pp. 507-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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HE, B, CHEN, X, ZHANG, D, LIU, S, HAN, D & NI, LM 2019, PBE : driver behavior assessment beyond trajectory profiling. in U Brefeld, A Marascu, F Pinelli, E Curry, B MacNamee, N Hurley, E Daly & M Berlingerio (eds), Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11053 LNAI, Springer-Verlag GmbH and Co. KG, pp. 507-523, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018, Dublin, Ireland, 10/09/18. https://doi.org/10.1007/978-3-030-10997-4_31

PBE : driver behavior assessment beyond trajectory profiling. / HE, Bing; CHEN, Xiaolin; ZHANG, Dian; LIU, Siyuan; HAN, Dawei; NI, Lionel M.

Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings. ed. / Ulf Brefeld; Alice Marascu; Fabio Pinelli; Edward Curry; Brian MacNamee; Neil Hurley; Elizabeth Daly; Michele Berlingerio. Springer-Verlag GmbH and Co. KG, 2019. p. 507-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI).

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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AB - 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%.

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HE B, CHEN X, ZHANG D, LIU S, HAN D, NI LM. PBE : driver behavior assessment beyond trajectory profiling. In Brefeld U, Marascu A, Pinelli F, Curry E, MacNamee B, Hurley N, Daly E, Berlingerio M, editors, Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Proceedings. Springer-Verlag GmbH and Co. KG. 2019. p. 507-523. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-10997-4_31