Abstract
Recent cars are equipped with a large number of sensors, electronic and communication devices that collect heterogeneous information about the vehicle, the environment and the driver. The use of the information coming from all these devices can highly contribute to the improvement of the vehicle safety as well as the driving experience. The last few years were marked by the development of a large number of in-vehicle intelligent systems that use driving behavior models to assist the driver ubiquitously. However, an important aspect to enhance driving experience is to make the provided assistance as close as possible to the behavior of the car owner, hence a need of personal models of drivers learned from their observed behavior. In this paper, the concept of intelligent and self-learning car is presented and examples of some car’s embedded systems are given. Also, the role of modeling driver behavior in the design of driving assistance systems is emphasized. Further-more, the importance of monitoring-based driving behavior model construction to enable a personalized assistance is brought out together with some potential applications of formal driving behavior models.
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References
Al-Sultan, S., Al-Bayatti, A.H., Zedan, H.: Context-aware driver behavior detection system in intelligent transportation systems. IEEE Trans. Veh. Technol. 62(9), 4264–4275 (2013)
Aliane, N., Fernández, J., Bemposta, S., Mata, M.: Traffic violation alert and management. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1716–1720. IEEE (2011)
Bouhoute, A., Berrada, I., El Kamili, M.: A formal driving behavior model for intelligent transportation systems. In: Networked Systems, pp. 298–312. Springer (2014)
Bouhoute, A., Oucheikh, R., Zahraoui, Y., Berrada, I.: A holistic approach for modeling and verification of human driver behavior. In: 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–7. IEEE (2015)
Elmahalawy, A.M.: A car monitoring system for self recording traffic violations. World 4, 5 (2014)
Enache, N.M., Mammar, S., Netto, M., Lusetti, B.: Driver steering assistance for lane-departure avoidance based on hybrid automata and composite lyapunov function. IEEE Trans. Intell. Transp. Syst. 11(1), 28–39 (2010)
Henzinger, T.A., Kopke, P.W.: Discrete-time control for rectangular hybrid automata. Theoret. Comput. Sci. 221(1), 369–392 (1999)
Johansson, B.: Road sign recognition from a moving vehicle. Technical report (2002)
Jung, C.R., Kelber, C.R.: A lane departure warning system based on a linear-parabolic lane model. In: Intelligent Vehicles Symposium, 2004 IEEE, pp. 891–895. IEEE (2004)
Li, W., Sadigh, D., Sastry, S.S., Seshia, S.A.: Synthesis for human-in-the-loop control systems. In: Tools and Algorithms for the Construction and Analysis of Systems, pp. 470–484. Springer (2014)
Lynch, N., Segala, R., Vaandrager, F.: Hybrid i/o automata. Inf. Comput. 185(1), 105–157 (2003)
Marzooqi, A.: Road safety system for monitoring fleet drivers. In: Safer Driving, Reducing Risks, Crashes and Casualties. Proceedings of the 68th Road Safety Congress Held Blackpool, 3–5 Mar 2003 (2003)
McCall, J.C., Trivedi, M.M.: Driver behavior and situation aware brake assistance for intelligent vehicles. Proc. IEEE 95(2), 374–387 (2007)
Enache, N.M., Netto, M., Mammar, S., Lusetti, B.: Driver steering assistance for lane departure avoidance. Control Eng. Pract. 17(6), 642–651 (2009)
Monika, Y.D.D., Avinash, N., Jung, H.G., Na, H.: Real time traffic sign recognition system as speed regulator in IAV. In: IICAI, pp. 1936–1951 (2009)
Nejati, O.: Smart recording of traffic violations via M-RFID. In: 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 1–4. IEEE (2011)
Ni, D.: Traffic Flow Theory: Characteristics, Experimental Methods, and Numerical Techniques (2015)
Oliver, N., Pentland, A.P.: Driver behavior recognition and prediction in a smartcar. In: AeroSense 2000, pp. 280–290. International Society for Optics and Photonics (2000)
Plavsic, M.: Analysis and modeling of driver behavior for assistance systems at road intersections. Ph.D. thesis, Lehrstuhl für Ergonomie der Technischen Universität München (2010)
Sadigh, D., Driggs-Campbell, K., Puggelli, A., Li, W., Shia, V., Bajcsy, R., Sangiovanni-Vincentelli, A.L., Sastry, S.S., Seshia, S.A.: Data-driven probabilistic modeling and verification of human driver behavior. In: Formal Verification and Modeling in Human-Machine Systems (2014)
Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: single-frame classification and system level performance. In: Intelligent Vehicles Symposium, 2004 IEEE, pp. 1–6. IEEE (2004)
Sunder, S.: Foundations for innovation in cyber-physical systems. In: Proceedings of the NIST CPS Workshop, Chicago, IL, USA, vol. 13 (2012)
Vallverdu, J.: Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics (2015)
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Bouhoute, A., Oucheikh, R., Berrada, I. (2017). Context-Aware Driving Assistance: An Approach for Monitoring-Based Modeling and Self-learning Cars. In: El-Azouzi, R., Menasche, D.S., Sabir, E., De Pellegrini, F., Benjillali, M. (eds) Advances in Ubiquitous Networking 2. UNet 2016. Lecture Notes in Electrical Engineering, vol 397. Springer, Singapore. https://doi.org/10.1007/978-981-10-1627-1_46
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DOI: https://doi.org/10.1007/978-981-10-1627-1_46
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