Skip to main content
SpringerLink
Log in

Methods to Detect and Reduce Driver Stress: A Review

  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver’s personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver’s attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver’s awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdic, I., Fridman, L., McDuff, D., Marchi, E., Reimer, B. and Schuller, B. (2016). Driver frustration detection from audio and video in the wild. KI 2016: Advances in Artificial Intelligence: 39th Annual German Conf. AI, Klagenfurt, Austria.

  • Active Park Assist (2017). Ford. https://owner.ford.com/how-tos/vehicle-features/convenience-and-comfort/active-park-assist.html

  • Alvarsson, J. J., Wiens, S. and Nilsson, M. E. (2010). Stress recovery during exposure to nature sound and environmental noise. Int. J. Environmental Research and Public Health 7, 3, 1036–1046.

    Article  Google Scholar 

  • Bakker, J., Pechenizkiy, M. and Sidorova, N. (2011). What’s your current stress level? Detection of stress patterns from GSR sensor data. Proc. IEEE 11th Int. Conf. Data Mining Workshops, Vancouver, Canada.

  • Baltaci, S. and Gokcay, D. (2016). Stress detection in human—computer interaction: Fusion of pupil dilation and facial temperature features. Int. J. Human—Computer Interaction 32, 12, 956–966.

    Article  Google Scholar 

  • Barniv, Y., Aguilar, M. and Hasanbelliu, E. (2005). Using EMG to anticipate head motion for virtual-environment applications. IEEE Trans. Biomedical Engineering 52, 6, 1078–1093.

    Article  Google Scholar 

  • Blanchard, E. B., Hickling, E. J., Taylor, A. E., Loos, W. R., Forneris, C. A. and Jaccard, J. (1996). Who develops PTSD from motor vehicle accidents?. Behaviour Research and Therapy 34, 1, 1–10.

    Article  Google Scholar 

  • Boril, H., Sadjadi, S. O., Kleinschmidt, T. and Hansen, J. H. (2010). Analysis and detection of cognitive load and frustration in drivers’ speech. Proc. Eleventh Annual Conf. Int. Speech Communication Association, Chiba, Japan.

  • Brookhuis, K. A., De Waard, D. and Janssen, W. H. (2001). Behavioural impacts of advanced driver assistance systems — An overview. European J. Transport and Infrastructure Research 1, 3, 245–253.

    Google Scholar 

  • Brookhuis, K. A. and De Waard, D. (2002). On the assessment of (mental) workload and other subjective qualifications. Ergonomics 45, 14, 1026–1030.

    Article  Google Scholar 

  • Costin, R., Rotariu, C. and Pasarica, A. (2012). Mental stress detection using heart rate variability and morphologic variability of EeG signals. Proc Int. Conf. and Exposition on Electrical and Power Engineering, Iasi, Romania.

  • Coughlin, J. F., Reimer, B. and Mehler, B. (2009). Driver Wellness, Safety & the Development of an Awarecar. AgeLab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

  • De Waard, D. (1996). The Measurement of Drivers’ Mental Workload. Groningen University. Traffic Research Center. Groningen, the Netherlands.

    Google Scholar 

  • English, A. (2009). Toyota’s £30 Million Driving Simulator Review. The Telegraph. http://www.telegraph.co.uk/motoring/road-safety/6598418/Toyotas-30-million-driving-simulator-review.html

  • Engström, I. (2008). Young Drivers and Their Passengers Crash Risk and Group Processes. Ph. D. Dissertation. Linköping University. Linköping, Sweden.

    Google Scholar 

  • Fernández, A., Usamentiaga, R., Carús, J. L. and Casado, R. (2016). Driver distraction using visual-based sensors and algorithms. Sensors 16, 11, 1805.

    Article  Google Scholar 

  • Fernandez, R. and Picard, R. W. (2003). Modeling drivers’ speech under stress. Speech Communication 40, 1-2, 145–159.

    Article  MATH  Google Scholar 

  • Ford Europe (2011). Ford Focus- Lane Keeping Aid. https://www.youtube.com/watch?v=l_mUyQmxJQY

  • Funke, G., Matthews, G., Warm, J. S. and Emo, A. K. (2007). Vehicle automation: A remedy for driver stress?. Ergonomics 50, 8, 1302–1323.

    Article  Google Scholar 

  • Gao, H., Yüce, A. and Thiran, J. P. (2014). Detecting emotional stress from facial expressions for driving safety. Proc. IEEE Int. Conf. Image Processing (ICIP), Paris, France.

  • Hart, S. and Staveland, L. (1988). Development of a multidimensional workload rating scale: Results of empirical and theoretical research. Human Mental Workload, 52, 139–183.

    Article  Google Scholar 

  • Hansen, H. L. (1996). Analysis and compensation of speech under stress and noise for environment robustness in speech recognition. Speech Communication 20, 1-2, 151–173.

    Article  Google Scholar 

  • Healey, J., Seger, J. and Picard, R. (1999). Quantifying driver stress: Developing a system for collecting and processing bio-metric signals in natural situations. Biomedical Sciences Instrumentation, 35, 193–198.

    Google Scholar 

  • Healy, J. A. and Picard, R. W. (2005). Detecting stress during real-world driving using physiological sensors. IEEE Trans. Intelligent Transportation Systems 6, 2, 156–166.

    Article  Google Scholar 

  • Healey, J. A. (2009). Affect detection in the real world: Recording and processing physiological signals. Proc. 3rd Int. Conf. Affective Computing and Intelligent Interaction and Workshops, Amsterdam, the Netherlands.

  • Hennessy, D. A. and Wiesenthal, D. L. (1999). Traffic congestion, driver stress, and driver aggression. Aggressive Behavior: Official J. Int. Society for Research on Aggression 25, 6, 409–423.

    Article  Google Scholar 

  • Horvath, F. (1978). An experimental comparison of the psychological stress evaluator and the galvanic skin response in detection of deception. J. Applied Psychology 63, 3, 338–344.

    Article  Google Scholar 

  • Howard, B. (2017). What is lane departure warning, and how does it work?. Extreme Tech. http://www.extremetech.com/extreme/165320-what-is-lanedeparture-warning-and-e.how-does-it-work

  • Hu, J. B., Li, A. and Wang, W. L. (2011). Analysis on driver’s driving workload in different weather conditions. J. Beijing University of Technology 37, 4.

  • Hill, J. D. and Boyle, L. N. (2007). Driver stress as influenced by driving maneuvers and roadway conditions. Transportation Research Part F: Traffic Psychology and Behaviour 10, 3, 177–186.

    Article  Google Scholar 

  • Jadhav, P. and Jadhav, U. (2015). Lane departure warning system. Int. J. Computer Applications, 975, 8887.

    Google Scholar 

  • Jimenez, M. (2013). Physiological Sensor: Electronic Theses and Dissertations, Paper 689.

  • Kompier, M. A. and Di Martino, V. (1995). Review of bus drivers’ occupational stress and stress prevention. Stress Medicine 11, 1, 253–262.

    Article  Google Scholar 

  • Kontogiannis, T. (2006). Patterns of driver stress and coping strategies in a Greek sample and their relationship to aberrant behaviors and traffic accidents. Accident Analysis & Prevention 38, 5, 913–924.

    Article  Google Scholar 

  • Koolhaas, J. M., Bartolomucci, A., Buwalda, B. D., De Boer, S. F., Flugge, G., Korte, S. M., Meerlo, P., Murison, R., Olivier, B., Palanza, P. and Richter-Levin, G. (2011). Stress revisited: A critical evaluation of the stress concept. Neuroscience & Biobehavioral Reviews 35, 5, 1291–1301.

    Article  Google Scholar 

  • Kurniawan, H., Maslov, A. V. and Pechenizkiy, M. (2013). Stress detection from speech and galvanic skin response signals. Proc. 26th IEEE Int. Symp. Computer-Based Medical Systems, Porto, Portugal.

  • Lanatà, A., Valenza, G., Greco, A., Gentili, C., Bartolozzi, R., Bucchi, F., Frendo, F. and Scilingo, E. P. (2015). How the autonomic nervous system and driving style change with incremental stressing conditions during simulated driving. IEEE Trans. Intelligent Transportation Systems 16, 3, 1505–1517.

    Article  Google Scholar 

  • Lazarus, R. S. and Folkman, S. (1984). Stress, Appraisal, and Coping. Springer. New York, USA.

    Google Scholar 

  • Lee, D. S., Chong, T. W. and Lee, B. G. (2017). Stress events detection of driver by wearable glove system. IEEE Sensors J. 17, 1, 194–204.

    MathSciNet  Google Scholar 

  • Lockhart, R. A. (1972). Interrelations between amplitude, latency, rise time, and the Edelberg recovery measure of the galvanic skin response. Psychophysiology 9, 4, 437–442.

    Article  Google Scholar 

  • Loft, S., Sanderson, P., Neal, A. and Mooi, J. M. (2007). Modeling and prediciting mental workload in en-route air traffic control: Critical review and broader implications. Human Factor: J. Human Factors and Ergonomics Society 49, 3, 376–399.

    Article  Google Scholar 

  • Ma, R. and Kaber, D. B. (2005). Situation awareness and workload in driving while using adaptive cruise control and a cell phone. Int. J. Industrial Ergonomics 35, 10, 939–953.

    Article  Google Scholar 

  • Mackay, C., Cox, T., Burrows, G. and Lazzerini, T. (1978). An inventory for the measurement of self-reported stress and arousal. British J. Social and Clinical Psychology 17, 3, 283–284.

    Article  Google Scholar 

  • Matthews, G., Desmond, P. A., Joyner, L., Carcary, B. and Gilliland, K. (1996). Validation of the driver stress inventory and driver coping questionnaire. Proc. Int. Conf. Traffic and Transport Psychology, Valencia, Spain.

  • Matthews, G., Joyner, L., Gilliland, K., Campbell, S., Falconer, S. and Huggins, J. (1999). Validation of a comprehensive stress state questionnaire: Towards a state big three. Proc. Personality Psychology in Europe, Ghent, Belgium.

  • Matthews, G. and Desmond, P. A. (2001). Stress and driving performance: Implications for design and training. Human Factors in Transportation. Stress, Workload and Fatigue. Lawrence Erlbaum Associates Publishers. Mahwah, New Jersey, USA.

    Google Scholar 

  • Mayou, R. and Bryant, B. (2003). Consequences of road traffic accidents for different types of road user. Injury 34, 3, 197–202.

    Article  Google Scholar 

  • Mcleod, S. (2017). Likert Scale. Simply Psychology. http://www.simplypsychology.org/likert-scale.html

  • Mehler, B., Reimer, B., Coughlin, J. and Dusek, J. (2009). Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Transportation Research Record: J. Transportation Research Board 2138, 1, 6–12.

    Article  Google Scholar 

  • Mehler, B., Reimer, B. and Coughlin, J. F. (2012). Sensitivity of physiological measures for detecting systematic variations in cognitive demand from a working memory task: An on-road study across three age groups. Human Factors 54, 3, 396–412.

    Article  Google Scholar 

  • Mehler, B., Kidd, D., Reimer, B., Reagan, I., Dobres, J. and McCartt, A. (2016). Multimodal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems. Ergonomics 59, 3, 344–367.

    Article  Google Scholar 

  • Meiring, G. A. M. and Myburgh, H. C. (2015). A review of intelligent driving style analysis systems and related artificial intelligent algorithms. Sensors 15, 12, 30653–30682.

    Article  Google Scholar 

  • Mizuno, N. and Hiep, N. M. (2013). An adaptive filtering technique for driver’s heart rate monitoring through vibration signal by seat-embedded piezoelectric sensors. IFAC Proc. Volumes 46, 11, 647–652.

    Article  Google Scholar 

  • Munla, N., Khalil, M., Shahin, A. and Mourad, A. (2015). Driver stress level detection using HRV analysis. Proc. Int. Conf. Advances in Biomedical Engineering (ICABME), Beirut, Lebanon.

  • Ohn-Bar, E., Tawari, A., Martin, S. and Trivedi, M. M. (2014). Predicting driver maneuvers by learning holistic features. IEEE Intelligent Vehicles Symp. Proc., Dearborn, Michigan, USA.

  • Or, C. K. and Duffy, V. G. (2007). Development of a facial skin temperature-based methodology for non-intrusive mental workload measurement. Occupational Ergonomics 7, 2, 83–94.

    Google Scholar 

  • Parsons, R., Tassinary, L. G., Ulrich, R. S., Hebl, M. R. and Grossman-Alexander, M. (1998). The view from the road: Implications for stress recovery and immunization. J. Environmental Psychology 18, 2, 113–140.

    Article  Google Scholar 

  • Pauzié, A. (2008a). Evaluating driver mental workload using the driving activity load index (DALI). Proc. European Conf. Human Interface Design for Intelligent Transport Systems, Berlin, Germany.

  • Pauzié, A. (2008b). A method to assess the driver mental workload: The driving activity load index (DALI). IET Intelligent Transport Systems 2, 4, 315–322.

    Article  Google Scholar 

  • Pauzié, A. (2009). Method to evaluate driver’s workload in real road context. Proc. Int. Conf. Digital Human Modeling, San Diego, California, USA.

  • Pedrotti, M., Mirzaei, M. A., Tedesco, A., Chardonnet, J. R., Mérienne, F., Benedetto, S. and Baccino, T. (2014). Automatic stress classification with pupil diameter analysis. Int. J. Human-Computer Interaction 30, 3, 220–236.

    Article  Google Scholar 

  • Pradhan, A. K., Hammel, K. R., DeRamus, R., Pollatsek, A., Noyce, D. A. and Fisher, D. L. (2005). Using eye movements to evaluate effects of driver age on risk perception in a driving simulator. Human Factors 47, 4, 840–852.

    Article  Google Scholar 

  • Regan, M. A. and Mitsopoulos, E. (2001). Understanding Passenger Influences on Driver Behaviour: Implications for Road Safety and Recommendations for Countermeasure Development (No. 180).

  • Reimer, B., Mehler, B. and Coughlin, J. F. (2010). An evaluation of driver reactions to new vehicle parking assist technologies developed to reduce driver stress. Cambridge: New England University Transportation Center, Massachusetts Institute of Technology.

  • Reimer, B. and Mehler, B. (2011). The impact of cognitive workload on physiological arousal in young adult drivers: A field study and simulation validation. Ergonomics 54, 10, 932–942.

    Article  Google Scholar 

  • Reimer, B., Mehler, B. and Coughlin, J. F. (2016). Reductions in self-reported stress and anticipatory heart rate with the use of a semi-automated parallel parking system. Applied ergonomics, 52, 120–127.

    Article  Google Scholar 

  • Rimini-Doering, M., Manstetten, D., Altmueller, T., Ladstaetter, U. and Mahler, M. (2001). Monitoring driver drowsiness and stress in a driving simulator. Proc. 1st Int. Driving Symp. Human Factors in Driver Assessment, Training and Vehicle Design, Iowa, USA.

  • Sahayadhas, A., Sundaraj, K. and Murugappan, M. (2012). Detecting driver drowsiness based on sensors: A review. Sensors 12, 12, 16937–16953.

    Article  Google Scholar 

  • Schießl, C. (2007). Stress and strain while driving. Proc. European Conf. Transport Research Institutes, 1–11.

  • Selye, H. (1976). Stress without distress. Psychopathology of Human Adaption, 137–146.

  • Sena, P., Fiorentino, A., D’Amore, M. and Fusco, B. M. (2014). Road scenario and driver stress level: An HRV study in both virtual and real environments. Proc. Transport Research Arena (TRA) 5th Conf.: Transport Solutions from Research to Deployment, Paris, France.

  • Son, J., Mehler, B., Lee, T., Park, Y., Coughlin, J. and Reimer, B. (2011). Impact of cognitive workload on physiological arousal and performance in younger and older drivers. Proc. 6th Int. Driving Symp. Human Factors in Driver Assessment, Training, and Vehicle Design, Iowa, USA.

  • Stanton, N. A. and Young, M. S. (2005). Driver behaviour with adaptive cruise control. Ergonomics 48, 10, 1294–1313.

    Article  Google Scholar 

  • Stock, M. (2015). Intelligent Car Seat Detects Driver’s Stress Level: Reuters. http://www.reuters.com/article/uscar-technology/intelligent-car-seat-detects-driversstress-level-idUSKCN0RN11P20150923

  • Sun, F. T., Kuo, C., Cheng, H. T., Buthpitiya, S., Collins, P. and Griss, M. (2010). Activity-aware mental stress detection using physiological sensors. Proc. Int. Conf. Mobile Computing, Applications, and Services, Santa Clara, California, USA.

  • Takeda, K., Erdogan, H., Hansen, J. and Abut, H. eds. (2009). In-vehicle Corpus and Signal Processing for Driver Behavior. Springer-Verlag New York. New York, USA.

  • Tripplett, T. and Rosenbloom, S. (2015). American Driving Survey: Methodology and Year 1 Results. AAA Foundation, Washington.

  • Vavrinsky, E., Tvarozek, V., Gaspierik, P., Stopjakova, V., Donoval, M. and Teleka, P. (2012). Design of noninvasive setup for car driver biomonitoring. Procedia Chemistry, 6, 203–210.

    Article  Google Scholar 

  • Wang, Y., Mehler, B., Reimer, B., Lammers, V., D’Ambrosio, L. A. and Coughlin, J. F. (2010). The validity of driving simulation for assessing differences between in-vehicle informational interfaces: A comparison with field testing. Ergonomics 53, 3, 404–420.

    Article  Google Scholar 

  • Westerman, S. J. and Heigney, D. (2000). Individual differences in driver stress, error and violation. Personality and Individual Differences 29, 5, 981–998.

    Article  Google Scholar 

  • T. C. Harrison Ford (2017). What is Ford Active City Stop?. http://www.tch.co.uk/about/why-choose-ford/active-city-stop

  • The American Institute Stress (2017). What is Stress?. http://www.stress.org/what-is-stress

  • Wiesenthal, D. L., Hennessy, D. A. and Totten, B. (2000). The influence of music on driver stress. J. Applied Social Psychology, 30, 1709–1719.

    Article  Google Scholar 

  • Wijsman, J., Grundlehner, B., Liu, H., Hermens, H. and Penders, J. (2011). Towards mental stress detection using wearable physiological sensors. Proc. Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, Boston, Massachusetts, USA.

  • Yamaguchi, M. and Sakakima, J. (2007). Evaluation of driver stress in a motor-vehicle driving simulator using a biochemical marker. J. Int. Medical Research 35, 1, 91–100.

    Article  Google Scholar 

  • Yamakoshi, T., Yamakoshi, K., Tanaka, S., Nogawa, M., Park, S. B., Shibata, M., Sawada, Y., Rolfe, P. and Hirose, Y. (2008). Feasibility study on driver’s stress detection from differential skin temperature measurement. Proc. IEEE Engineering in Medicine and Biology Society Annual Conf., Vancouver, Canada.

  • Yin, B., Ruiz, N., Chen, F. and Khawaja, M. A. (2007). Automatic cognitive load detection from speech features. Proc. 19th Australasian Conf. Computer- Human Interaction: Entertaining User Interfaces, Adelaide, Australia.

Download references

Acknowledgement

This work was supported by Mid-Career Researher Program through an NRF grant funded by the Korean Government (MSIT) (No. NRF-2016R1A2B4015818), Korea.

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Pukyong National University, Busan, 48513, Korea

    Wan-Young Chung

  2. Department of Electronics Engineering, Keimyung University, Daegu, 42601, Korea

    Teak-Wei Chong

  3. School of Computer Science, Faculty of Science and Engineering, The University of Nottingham Ningbo China, Ningbo, 315100, China

    Boon-Giin Lee

Authors
  1. Wan-Young Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teak-Wei Chong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boon-Giin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Boon-Giin Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chung, WY., Chong, TW. & Lee, BG. Methods to Detect and Reduce Driver Stress: A Review. Int.J Automot. Technol. 20, 1051–1063 (2019). https://doi.org/10.1007/s12239-019-0099-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12239-019-0099-3

Key words

Search

Navigation