Omid Dehzangi
ABSTRACT
The objective of this paper is to introduce the design of the next generation driver monitoring platform to be facilitated in the semi-autonomous automotive system infrastructure. In the context of connected vehicles, this work extends current infrastructure to include real-time driver monitoring and feedback. Rather than leaving the driver out of the process, the goal is to obtain a vehicle where the degree of autonomy is continuously changed in real-time as a function of uncertainty ranges for driver biological state and behavior. The evolution and dissemination of mobile technology has created exceptional opportunities for highly detailed and personalized data collection in a far more granular and cost effective way. However, turning this potential into practice requires algorithms and methodologies to transform these raw data into actionable information. We have developed a robust driver monitoring platform consisting of automotive sensors (i.e. OBD-II) that capture the real-time information of the vehicle and driving behavior as well as a heterogeneous wearable body sensor network that collects the driver biometrics (e.g., electroencephalography (EEG) and electrocardiogram (ECG)). Accurate synchronization and storage of such multi-source heterogeneous data were also developed and validated. Finally, The task of characterizing driver distraction using EEG signals was investigated in two different road conditions as a proof of concept.
- Trimble, T. E., Bishop, R., Morgan, J. F., & Blanco, M. (2014, July). Human factors evaluation of level 2 and level 3 automated driving concepts: Past research, state of automation technology, and emerging system concepts. (Report No. DOT HS 812 043). Washington, DC: National Highway Traffic Safety Administration.Google Scholar
- Pompei, J.F., Taly, S., Buckley, S.J., Kemp, J. (2002) "An automobileintegrated system for assessing and reacting to driver cognitive load." in Proceedings of Convergence, Detroit, Michigan, USA, 411--416.Google Scholar
- B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden, "Cartel: a distributed mobile sensor computing system," in SenSys '06: Proceedings of the 4th international conference on Embedded networked sensor systems. New York, NY, USA: ACM, 2006, pp. 125--138. Google ScholarDigital Library
- Deffenbacher, J. L., Deffenbacher, D. M., Lynch, R. S., & Richards, T. L. (2003). Anger, aggression, and risky behavior: A comparison of high and low anger drivers. Behavior Research and Therapy, 41, 701--718.Google ScholarCross Ref
- E. Simonson, C. Baker, N. Burns, C. Keiper, O. H. Schmitt, and S. Stackhouse, "Cardiovascular stress (electrocardiographic changes) produced by driving an automobile," American Heart Journal, vol. 75, no. 1, pp. 125--135, 1968.Google Scholar
- Healey, J.A., Picard, R.W. (2005) "Detecting stress during real-world driving tasks using physiological sensors," IEEE Transactions on Intelligent Transportation Systems, (6): 156--166. Google ScholarDigital Library
- Yang, G., Lin, Y., Bhattacharya, P. (2005) "A driver fatigue recognition model using fusion of multiple features." IEEE International Conference on Systems, Man and Cybernetics, (2):1777--1784.Google Scholar
- Underwood, G., Chapman, G.P., Wright, S., Crundall, D. (1999) "Anger while driving." Transportation Research Part F: Traffic Psychology and Behaviour, (2): 55--68.Google Scholar
- Tan, H., Zhang, Y.U.J. (2006) "Detecting eye blink states by tracking iris and eyelids," Pattern Recognition Letters, (27): 667--675. Google ScholarDigital Library
- Bittner, R., Smrcka, P., Pavelka, M., Vysok, P., Pousek, L. (2001) "Fatigue indicators of drowsy drivers based on analysis of physiological signals," International Symposium on Medical Data Analysis, 62--68. Google ScholarDigital Library
- Vitaljacket R. (2010) "a wearable ambulatory ecg medical device". Biodevices {Online}. Available: http://www.vitaljacket.comGoogle Scholar
- Park, J., Xu, L., Sridhar, V., Chi, M., Cauwenberghs, G., (2011) "Wireless dry EEG for drowsiness detection," Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE , 3298--3301.Google Scholar
- Sun, Y., Yu, X., (2014). An Innovative Nonintrusive Driver Assistance System for Vital Signal Monitoring. IEEE J Biomed Health Inform. 18(6):1932--9.Google ScholarCross Ref
- De Santos A., Sánchez-Avila, C., Guerra-Casanova, J., Bailador-Del Pozo, G. (2011) Real-Time Stress Detection by Means of Physiological Signals. Recent Applications in Bioinformatics ISBN 978--953--307--488--7.Google Scholar
- Sun F., Kuo C., Cheng H., Buthpitiya S., Collins P., Griss M. (2010) Activity-Aware Mental Stress Detection Using Physiological Sensors. Proceedings of 2nd International Conference on Mobile Computing, Applications and Services (MobiCASE); Santa Clara, CA, USA.Google Scholar
- Lee B., Chung, W.Y. (2012) A Smartphone-Based Driver Safety Monitoring System Using Data Fusion. Sensors 2012, (12):17536--52.Google ScholarCross Ref
- Lecture Notes in Computer Science Volume 4565, 2007, pp 294--303Google Scholar
- Lin, C.T., Chen, S.A., Ko, L.W., Wang, U.K. (2011) EEG-based brain dynamics of driving distraction, The 2011 International Joint Conference on Neural Networks (IJCNN) 1497,1500.Google ScholarCross Ref
- Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., McKeown, M. J., Iragui, V., Sejnowski, T. J., (2000) Removing electroencephalographic artifacts by blind source separation, Psychophysiology, ( 37):163--78.Google Scholar
Index Terms
- Multi-Modal Biological Driver Monitoring via Ubiquitous Wearable Body Sensor Network
Recommendations
Driver State Monitoring System: A Review
BDIoT '19: Proceedings of the 4th International Conference on Big Data and Internet of ThingsTraffic crashes cause a lot of fatality and injuries every year. Robust and reliable driver monitoring system is an important step toward achieving higher vehicle autonomy level, as well as insuring the road safety and preventing road accidents. Driver ...
A Monitoring System to Understand Postal Motorcyclist's Driving Behavior
ITSC '15: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation SystemsIn Korea, motorcycle accidents constitute about 5% of total traffic accidents, however, the fatality rate approximates 12%, which is disproportionately higher per mile driven compared to other vehicular fatalities. Most of these fatalities are related ...
A novel active heads-up display for driver assistance
Special issue on human computingIn this paper, we introduce a novel laser-based wide-area heads-up windshield display which is capable of actively interfacing with a human as part of a driver assistance system. The dynamic active display (DAD) is a unique prototype interface that ...
Reviews
Lalit P Saxena
Self-driving cars, or driverless cars, are becoming the future of driving without manual intervention. Currently, the speed of around 25 kilometers per hour (km/h) has been achieved for self-driving cars; however, the research in this direction is progressing to achieve more than 100 km/h. Automatic braking, lane-keeping assistance, and collision warning and avoiding are a few examples of automated systems being installed in cars. The authors present an automatic driver monitoring system to monitor the uncertainty of a driver's biological state and behavior in real time. They developed "a robust driver monitoring platform that consists of automotive on-board diagnostics sensors to capture the real-time information of the vehicle and driving behavior." Further, it contains "a heterogeneous wearable body sensor network that collects the driver's biometrics using electroencephalography, electrocardiogram, electromyography, and Galvanic skin response synchronized to record speed, acceleration, steering, and fuel consumption." The system has many features: it is minimally intrusive, comprehensive, user-friendly, responsive in real time, ubiquitous, and available remotely. For the experiments, the authors employed four drivers on a route comprising a mixture of urban roads and highways in Dearborn, Michigan. The drivers were asked to drive twice, at 11 AM and 6 PM, when there was less traffic and in rush hour, respectively. The system monitored the drivers' behavior while driving and while conversing with passengers, and extracted the drivers' biological state information using data mining, machine learning, and statistical analysis. The authors generalize the results of less-complicated driving environments for automated driving scenarios. They provide insights into having a statistical measure of the biometrics for an easy and safe switching of "control between the driver and the automated vehicle when necessary." Online Computing Reviews Service
Access critical reviews of Computing literature here
Become a reviewer for Computing Reviews.
Comments