ABSTRACT
Detecting how a vehicle is steered and then alarming drivers in real time is of utmost importance to the vehicle and the driver's safety, since fatal accidents are often caused by dan- gerous steering. Existing solutions for detecting dangerous maneuvers are implemented either in only high-end vehicles or on smartphones as mobile applications. However, most of them rely on the use of cameras, the performance of which is seriously constrained by their high visibility requirement. Moreover, such an over/sole-reliance on the use of cameras can be a distraction to the driver.
To alleviate these problems, we develop a vehicle steering detection middleware called V-Sense which can run on commodity smartphones without additional sensors or infrastructure support. Instead of using cameras, the core of V-Sense/ senses a vehicle's steering by only utilizing non-vision sensors on the smartphone. We design and evaluate algorithms for detecting and differentiating various vehicle maneuvers, including lane-changes, turns, and driving on curvy roads. Since V-Sense does not rely on use of cameras, its detection of vehicle steering is not affected by the (in)visibility of road objects or other vehicles. We first detail the design, implementation and evaluation of V-Sense and then demonstrate its practicality with two prevalent use cases: camera-free steering detection and fine-grained lane guidance. Our extensive evaluation results show that V-Sense is accurate in determining and differentiating various steering maneuvers, and is thus useful for a wide range of safety-assistance applications without additional sensors or infrastructure.
- Augmented driving. https://itunes.apple.com/us/app/augmented-driving/id366841514?mt=8.Google Scholar
- Blacksensor on google play. https://play.google.com/store/apps/details?id=com.chahoo.bsdrive&hl=en.Google Scholar
- Drivea app. http://www.drivea.info/.Google Scholar
- Fatality analysis reporting system (fars) encyclopedia. http://www-fars.nhtsa.dot.gov/Main/index.aspx.Google Scholar
- Google map enable lane guidance. https://support.google.com/gmm/answer/3273406?hl=en.Google Scholar
- Honda, advanced driver-assistive system. http://world.honda.com/news/2014/4141024Honda-SENSING-Driver-Assistive-System/.Google Scholar
- Intersection design. http://www.deldot.gov/information/pubs_forms/manuals/road_design/pdf/revisions062811/07_Intersections.pdf?100411.Google Scholar
- ionroad app. http://www.ionroad.com/.Google Scholar
- Mobileye. http://www.mobileye.com/.Google Scholar
- Road edge and barrier detection with steer assist. https://www.youtube.com/watch?v=xrLVaWnJmMI.Google Scholar
- Steering patterns as drowsy indicator. http://www.sae.org/events/gim/presentations/2012/sgambati.pdf.Google Scholar
- Turning radius and intersection size. http://articles.latimes.com/2005/apr/20/autos/hy-wheel20.Google Scholar
- Volvo xc90. http://www.volvocars.com/us/cars/new-models/all-new-xc90.Google Scholar
- Traffic lane. http://en.wikipedia.org/wiki/Lane.Google Scholar
- Turning radius. http://en.wikipedia.org/wiki/Turning_radius.Google Scholar
- Lane change at intersection in california. http://articles.latimes.com/2005/apr/20/autos/hy-wheel20.Google Scholar
- M. Aly. Real time detection of lane markers in urban streets. In Intelligent Vehicles Symposium, 2008 IEEE, pages 7--12, June 2008.Google ScholarCross Ref
- C. Bo, X.-Y. Li, T. Jung, X. Mao, Y. Tao, and L. Yao. Smartloc: Push the limit of the inertial sensor based metropolitan localization using smartphone. In Proc. of ACM Mobicom, pages 195--198, 2013. Google ScholarDigital Library
- F. Caron, E. Duflos, D. Pomorski, and P. Vanheeghe. Gps/imu data fusion using multisensor kalman ltering: introduction of contextual aspects. Information Fusion, 7(2):221--230, 2006. Google ScholarDigital Library
- H. Dahlkamp, A. Kaehler, D. Stavens, S. Thrun, and G. Bradski. Self-supervised monocular road detection in desert terrain. In Proceedings of Robotics: Science and Systems, Philadelphia, USA, August 2006.Google ScholarCross Ref
- J. Dai, J. Teng, X. Bai, Z. Shen, and D. Xuan. Mobile phone based drunk driving detection. In International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pages 1--8, March 2010.Google ScholarCross Ref
- B. Friedland. Treatment of bias in recursive filtering. IEEE Transactions on Automatic Control, 14(4):359--367, Aug 1969.Google ScholarCross Ref
- J. A. Gubner. Probability and random processes for electrical and computer engineers. Cambridge University Press, 2006. Google ScholarDigital Library
- S. Hetrick. Examination of driver lane change behavior and the potential effectiveness of warning onset rules for lane change or "side" crash avoidance systems, 1997.Google Scholar
- D. Johnson and M. Trivedi. Driving style recognition using a smartphone as a sensor platform. In International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 1609--1615, Oct 2011.Google ScholarCross Ref
- T. Menard, J. Miller, M. Nowak, and D. Norris. Comparing the gps capabilities of the samsung galaxy s, motorola droid x, and the apple iphone for vehicle tracking using freesim mobile. In IEEE Intelligent Transportation Systems (ITSC), pages 985--990, 2011.Google ScholarCross Ref
- R. Ponziani. Turn signal usage rate results: A comprehensive field study of 12,000 observed turning vehicles. SAE International Paper, 2012-01-0261, 2012.Google Scholar
- Y. Wang, J. Yang, H. Liu, Y. Chen, M. Gruteser, and R. P. Martin. Sensing vehicle dynamics for determining driver phone use. In Proc. of ACM MobiSys. ACM, 2013. Google ScholarDigital Library
- C.-W. You, N. D. Lane, F. Chen, R. Wang, Z. Chen, T. J. Bao, M. Montes-de Oca, Y. Cheng, M. Lin, L. Torresani, and A. T. Campbell. Carsafe app: Alerting drowsy and distracted drivers using dual cameras on smartphones. In Proc. of ACM MobiSys. ACM, 2013. Google ScholarDigital Library
- P. Zhou, M. Li, and G. Shen. Use it free: Instantly knowing your phone attitude. In Proc. of ACM Mobicom. ACM, 2014. Google ScholarDigital Library
Index Terms
- Invisible Sensing of Vehicle Steering with Smartphones
Recommendations
Sensing vehicle dynamics for determining driver phone use
MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and servicesThis paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences ...
Vulnerable Road User Protection through Intuitive Visual Cue on Smartphones
CarSys '17: Proceedings of the 2nd ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and ServicesAs vehicle communication standards mature, protecting vulnerable road users (VRUs) using vehicle communication technology is looming as a promising and useful application. The current vehicle-to-pedestrian (V2P) communication as stipulated by the ...
Leveraging Acoustic Signals for Vehicle Steering Tracking with Smartphones
Given the increasing popularity, mobile devices are exploited to enhance active driving safety nowadays. Among all safety services provided for vehicles, tracking the rotation angle of steering wheel in real time can monitor the vehicles’ dynamics ...
Comments