TY - GEN
T1 - Brake data-based location tracking in usage-based automotive insurance programs
AU - Sarker, Ankur
AU - Qiu, Chenxi
AU - Shen, Haiying
AU - Uehara, Hua
AU - Zheng, Kevin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086895083&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086895083&partnerID=8YFLogxK
U2 - 10.1109/IPSN48710.2020.00-32
DO - 10.1109/IPSN48710.2020.00-32
M3 - Conference contribution
AN - SCOPUS:85086895083
T3 - Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
SP - 229
EP - 240
BT - Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
Y2 - 21 April 2020 through 24 April 2020
ER -