Abstract
Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy-efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decides whether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of the driving system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
emWave is a software of the company HeartMath: http://www.heartmath.com/.
References
Barkenbus JN (2009) Eco-driving: an overlooked climate change initiative. Energy Policy 38(2):762769
Brookhuis KA, de Waard D (2010) Monitoring drivers mental workload in driving simulators using physiological measures. Accid Anal Prev 42(3):898–903
Cho HJ (2008) Eco driving system. URL http://kia-buzz.com/eco-driving-system/, Last visit: 07.10.2014
Fiat (2010) Eco-driving uncovered: the benefits and challenges of eco-driving, based on the first study using real journey data
German Statistical Office (2014) Verkehr- Verkehrsunfälle 2013
Haworth N, Symmons M (2001) Driving to reduce fuel consumption and improve road safety. In: Proceedings road safety research, policing and education conference, vol 1, pp 7
Healey J, Picard R (2000) Smartcar: detecting driver stress. In: Proceedings of the 15th international conference on pattern recognition, vol 4, pp 218–221
Helms H, Lambrecht U, Hanusch J (2010) Energieeffizienz im Verkehr. Energieeffizienz 1:309–329
Kumar M, Weippert M, Vilbrandt R, Kreuzfeld S, Stoll R (2007) Fuzzy evaluation of heart rate signals for mental stress assessment. IEEE Trans Fuzzy Syst 15(5):791–808
Lotan T, Toledo T (2006) An in-vehicle data recorder for evaluation of driving behavior and safety. Transportation Research Board of the National Academies, Paper No. 061607, pp 1–14
New Zealand Transport Agency (2007) Your safe driving policy: helping you to manage work-related road safety and keep your employees and vehicles safe on the roads
Salahuddin L, Kim D (2006) Detection of acute stress by heart rate variability using a prototype mobile ECG sensor. In: Proceedings of the 2006 international conference on hybrid information technology, vol 2, pp 453–459
UNECE—United Nations Economic Commission for Europe (2004) Aggressive driving behaviour (background paper). URL http://www.unece.org/trans/roadsafe/rs4aggr.html, Last visit: 07.02.2015
van Mierlo J, Maggetto G, van de Burgwal E, Gense R (2004) Driving style and traffic measures—influence on vehicle emissions and fuel consumption. Proc Inst Mech Eng Part D J Automobile Eng 218:43–50
Xiaoqiu F, Jinzhang J, Guoqiang Z (2011) Impact of driving behavior on the traffic safety of highway intersection. In: Third international conference on measuring technology and mechatronics, vol 2, pp 370–373
Yay E, Madrid NM (2013) Seedrive—an adaptive and rule based driving system. In: The 9th international conference on intelligent environments, Athens, Greece, pp 262–265
Yay E, Madrid NM, RamÃrez JAO (2014) Using an improved rule match algorithm in an expert system to detect broken driving rules for an energy-efficiency and safety relevant driving system. Procedia Comput Sci KES 35(1):127–136
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Yay, E., Madrid, N.M. (2016). Stress-Aware Generation of Recommendations in a Driving System to Increase User Acceptance. In: Conti, M., MartÃnez Madrid, N., Seepold, R., Orcioni, S. (eds) Mobile Networks for Biometric Data Analysis. Lecture Notes in Electrical Engineering, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-319-39700-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-39700-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39698-9
Online ISBN: 978-3-319-39700-9
eBook Packages: EngineeringEngineering (R0)