Brake data-based location tracking in usage-based automotive insurance programs

Ankur Sarker, Chenxi Qiu, Haiying Shen, Hua Uehara, Kevin Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Many usage-based automotive insurance programs do not directly use any GPS-based location tracking devices. Instead, CAN-bus data, such as brake signal data, can be collected by these programs to evaluate drivers' driving habits and vehicle usage policies. In this paper, we demonstrate that by using a temporal sequence of applied brake signals collected from a vehicle, attackers can still possibly infer the vehicle's route over the period, even though brake signal data does not reveal any specific location information. Our route inference is basically composed of three steps: At first, we categorize brake signal subsequences into four different driving maneuvers (i.e., stopping from a certain speed, reducing speed to adjust with the traffic flow, and taking left and right turns). Second, we estimate the number of intersections traversed by the vehicle using the applied brake signals and their corresponding maneuvers. Particularly, we also estimate the overall speed profile based on the magnitude and interval of different brake signals. From the estimated speed profile, we infer the distances, traveling time, and traffic signs corresponding to the candidate edges. Finally, we design a graph-based route-selection algorithm to find a list of (paths) routes from the regional map using the predicted driving maneuvers and the speed profile. We use a score function based on three factors (i.e., distance, traveling time, and traffic signs) to identify a candidate edge. We evaluate our approach using over 450km of transportation data, which has been collected from 24 individuals. The experimental results demonstrate that, by resorting to our solution, 89% of the original drivers' trajectory can be successfully recovered from their brake data regardless of driver and vehicle models.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages229-240
Number of pages12
ISBN (Electronic)9781728154978
DOIs
StatePublished - Apr 2020
Event19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 - Sydney, Australia
Duration: Apr 21 2020Apr 24 2020

Publication series

NameProceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020

Conference

Conference19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
Country/TerritoryAustralia
CitySydney
Period4/21/204/24/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Information Systems and Management

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