TY - GEN
T1 - BigRoad
T2 - 15th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2017
AU - Liu, Luyang
AU - Li, Hongyu
AU - Liu, Jian
AU - Karatas, Cagdas
AU - Wang, Yan
AU - Gruteser, Marco
AU - Chen, Yingying
AU - Martin, Richard P.
N1 - Funding Information:
We sincerely thank our shepherd Dr. Souvik Sen and the reviewers for their insightful comments. This material is based in part upon work supported by the National Science Foundation under Grant Nos. CNS-1329939, CNS-1409767, CNS-1409811, and CNS-1566455.
Publisher Copyright:
© 2017 ACM.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Advanced driver assistance systems and, in particular automated driving offers an unprecedented opportunity to transform the safety, efficiency, and comfort of road travel. Developing such safety technologies requires an understanding of not just common highway and city traffic situations but also a plethora of widely different unusual events (e.g., object on the road way and pedestrian crossing highway, etc.). While each such event may be rare, in aggregate they represent a significant risk that technology must address to develop truly dependable automated driving and traffic safety technologies. By developing technology to scale road data acquisition to a large number of vehicles, this paper introduces a low-cost yet reliable solution, BigRoad, that can derive internal driver inputs (i.e., steering wheel angles, driving speed and acceleration) and external perceptions of road environments (i.e., road conditions and front-view video) using a smartphone and an IMU mounted in a vehicle. We evaluate the accuracy of collected internal and external data using over 140 real-driving trips collected in a 3-month time period. Results show that BigRoad can accurately estimate the steering wheel angle with 0:69ffi median error, and derive the vehicle speed with 0:65 km/h deviation. The system is also able to determine binary road conditions with 95% accuracy by capturing a small number of brakes. We further validate the usability of BigRoad by pushing the collected video feed and steering wheel angle to a deep neural network steering wheel angle predictor, showing the potential of massive data acquisition for training self-driving system using BigRoad.
AB - Advanced driver assistance systems and, in particular automated driving offers an unprecedented opportunity to transform the safety, efficiency, and comfort of road travel. Developing such safety technologies requires an understanding of not just common highway and city traffic situations but also a plethora of widely different unusual events (e.g., object on the road way and pedestrian crossing highway, etc.). While each such event may be rare, in aggregate they represent a significant risk that technology must address to develop truly dependable automated driving and traffic safety technologies. By developing technology to scale road data acquisition to a large number of vehicles, this paper introduces a low-cost yet reliable solution, BigRoad, that can derive internal driver inputs (i.e., steering wheel angles, driving speed and acceleration) and external perceptions of road environments (i.e., road conditions and front-view video) using a smartphone and an IMU mounted in a vehicle. We evaluate the accuracy of collected internal and external data using over 140 real-driving trips collected in a 3-month time period. Results show that BigRoad can accurately estimate the steering wheel angle with 0:69ffi median error, and derive the vehicle speed with 0:65 km/h deviation. The system is also able to determine binary road conditions with 95% accuracy by capturing a small number of brakes. We further validate the usability of BigRoad by pushing the collected video feed and steering wheel angle to a deep neural network steering wheel angle predictor, showing the potential of massive data acquisition for training self-driving system using BigRoad.
KW - IMU
KW - Road data acquisition
KW - Self-driving
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=85025627302&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025627302&partnerID=8YFLogxK
U2 - 10.1145/3081333.3081344
DO - 10.1145/3081333.3081344
M3 - Conference contribution
AN - SCOPUS:85025627302
T3 - MobiSys 2017 - Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services
SP - 371
EP - 384
BT - MobiSys 2017 - Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
Y2 - 19 June 2017 through 23 June 2017
ER -