Johannes Paefgen
Florian Michahelles
ABSTRACT
We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.
- E. Fleisch and F. Thiesse, "On the management implications of ubiquitous computing: an IS perspective," in Proceedings of the 15th European Conference on Information Systems, St. Gallen, Switzerland, 2007, pp. 1929--40.Google Scholar
- V. Coroama, "The smart tachograph - individual accounting of traffic costs and its implications," in Proceedings of the 4th International Conference on Pervasive Computing, Dublin, Ireland: Lecture Notes in Computer Science, Vol. 3968, 2006, pp. 135--152. Google ScholarDigital Library
- J. Paefgen, F. Michahelles, and T. Staake, "GPS trajectory feature extraction for driver risk profiling," International Workshop on Trajectory Data Mining and Analysis, UbiComp, China, 2011. Google ScholarDigital Library
- I. J. Wouters and J. M. Bos, "Traffic accident reduction by monitoring driver behaviour with in-car data recorders.," Accident Analysis & Prevention, vol. 32, no. 5, pp. 643--50, Sep. 2000.Google ScholarCross Ref
- C. Troncoso, G. Danezis, and E. Kosta, "Pripayd: privacy friendly pay-as-you-drive insurance," ACM workshop on Privacy, 2007. Google ScholarDigital Library
- T. Toledo, O. Musicant, and T. Lotan, "In-vehicle data recorders for monitoring and feedback on drivers' behavior," Transportation Research Part C: Emerging Technologies, vol. 16, no. 3, pp. 320--331, Jun. 2008.Google ScholarCross Ref
- A. Pérez, M. I. Garcia, M. Nieto, J. L. Pedraza, S. Rodríguez, and J. Zamorano, "Argos: An Advanced In-Vehicle Data Recorder on a Massively Sensorized Vehicle for Car Driver Behavior Experimentation," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 463--473, Jun. 2010. Google ScholarDigital Library
- O. Musicant, H. Bar-gera, and E. Schechtman, "Electronic records of undesirable driving events," Transportation Research Part F: Psychology and Behaviour, vol. 13, no. 2, pp. 71--79, 2010.Google ScholarCross Ref
- R. Bhoraskar, N. Vankadhara, B. Raman, and P. Kulkarni, "Wolverine: Traffic and road condition estimation using smartphone sensors," 2012 Fourth International Conference on Communication Systems and Networks, pp. 1--6, Jan. 2012.Google Scholar
- M. Li, J. Dai, S. Sahu, and M. Naphade, "Trip analyzer through smartphone apps," Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in GIS '11, p. 537, 2011. Google ScholarDigital Library
- D. a. Johnson and M. M. Trivedi, "Driving style recognition using a smartphone as a sensor platform," 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1609--1615, Oct. 2011.Google Scholar
- A. K. Shaout and A. E. Bodenmiller, "A Mobile Application for Monitoring Inefficient and Unsafe Driving Behaviour," 1996.Google Scholar
- M. Ruta, F. Scioscia, F. Gramegna, E. D. Sciascio, and P. Bari, "A Mobile Knowledge-based System for On-Board Diagnostics and Car Driving Assistance," no. c, pp. 91--96, 2010.Google Scholar
- R. C. Hibbeler, Engineering Mechanics: Dynamics, 12th ed. Upper Saddle River, NJ: Prentice Hall, 2009.Google Scholar
- K. Li, M. Lu, F. Lu, Q. Lv, and L. Shang, "Personalized Driving Behavior Monitoring and Analysis for Emerging Hybrid Vehicles," Pervasive 2012. Google ScholarDigital Library
- A. Field, "Discovering Statistics using SPSS", London: Sage Publications, pp. 559--571, 2009. Google ScholarDigital Library
- C. J. Goodwin, "Research in Psychology: Methods and Design", New York: Wiley, p. 132., 2009.Google Scholar
- H. Eren, S. Makinist, E. Akin, und A. Yilmaz, "Estimating driving behavior by a smartphone", in 2012 IEEE Intelligent Vehicles Symposium (IV), 2012, p. 234--239.Google ScholarCross Ref
Index Terms
- Driving behavior analysis with smartphones: insights from a controlled field study
Recommendations
A smartphone based technique to monitor driving behavior using DTW and crowdsensing
Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Todays fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving ...
Design of Test System on Nighttime Driving Behavior and ECG Characteristics of Long-distance Bus Drivers (I) - Test Design
ICIE '10: Proceedings of the 2010 WASE International Conference on Information Engineering - Volume 03For the safety issues of night long-distance bus drivers, a test system that real-time continuously acquires vehicles driving parameter, traffic environmental images as well as ECG physiological state of drivers under night-time driving conditions is ...
Investigating the effects of an advance warning in-vehicle system on behavior and attention in controlled driving
AutomotiveUI '11: Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsAdvance warning systems constitute a class of in-vehicle systems that could influence and improve driving in the future, e.g. by warning of accidents or slippery roads. However, the effects of advance warning systems are still poorly understood and ...
Comments