TY - GEN
T1 - Predriveid
T2 - 2nd IEEE/ACM Symposium on Edge Computing, SEC 2017
AU - Kar, Gorkem
AU - Jain, Shubham
AU - Gruteser, Marco
AU - Chen, Jinzhu
AU - Bai, Fan
AU - Govindan, Ramesh
N1 - Funding Information: Œis material is based upon work supported by the National Science Foundation under Grant No CNS-1329939. Funding Information: This material is based upon work supported by the National Science Foundation under Grant No CNS-1329939. Publisher Copyright: © 2017 ACM.
PY - 2017/10/12
Y1 - 2017/10/12
N2 - .is paper explores the minimal dataset necessary at vehicular edge nodes, to effectively differentiate drivers using data from existing in-vehicle sensors. .is facilitates novel personalization, insurance, advertising, and security applications but can also help in understanding the privacy sensitivity of such data. Existing work on differentiating drivers largely relies on devices that drivers carry, or on the locations that drivers visit to distinguish drivers. Internally, however, the vehicle processes a much richer set of sensor information that is becoming increasingly available to external services. To explore how easily drivers can be distinguished from such data, we consider a system that interfaces to the vehicle bus and executes supervised or unsupervised driver differentiation techniques on this data. To facilitate this analysis and to evaluate the system, we collect in-vehicle data from 24 drivers on a controlled campus test route, as well as 480 trips over three weeks from five shared university mail vans. We also conduct studies between members of a family. The results show that driver differentiation does not require longer sequences of driving telemetry data but can be accomplished with 91% accuracy within 20s a.er the driver enters the vehicle, usually even before the vehicle starts moving.
AB - .is paper explores the minimal dataset necessary at vehicular edge nodes, to effectively differentiate drivers using data from existing in-vehicle sensors. .is facilitates novel personalization, insurance, advertising, and security applications but can also help in understanding the privacy sensitivity of such data. Existing work on differentiating drivers largely relies on devices that drivers carry, or on the locations that drivers visit to distinguish drivers. Internally, however, the vehicle processes a much richer set of sensor information that is becoming increasingly available to external services. To explore how easily drivers can be distinguished from such data, we consider a system that interfaces to the vehicle bus and executes supervised or unsupervised driver differentiation techniques on this data. To facilitate this analysis and to evaluate the system, we collect in-vehicle data from 24 drivers on a controlled campus test route, as well as 480 trips over three weeks from five shared university mail vans. We also conduct studies between members of a family. The results show that driver differentiation does not require longer sequences of driving telemetry data but can be accomplished with 91% accuracy within 20s a.er the driver enters the vehicle, usually even before the vehicle starts moving.
KW - Driving telemetry data
KW - On-board diagnostics
KW - Vehicular sensing
UR - http://www.scopus.com/inward/record.url?scp=85039865197&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039865197&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3132211.3134462
DO - https://doi.org/10.1145/3132211.3134462
M3 - Conference contribution
T3 - 2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017
BT - 2017 2nd ACM/IEEE Symposium on Edge Computing, SEC 2017
PB - Association for Computing Machinery, Inc
Y2 - 12 October 2017 through 14 October 2017
ER -