Affective computing to help recognizing mistaken pedal-pressing during accidental braking

Affective computing has been used to improve computer usability and user interface, by considering user’s emotion. One aspect of affective computing is emotion recognition. There have been many researches regarding emotion recognition, yet there is still room for exploration in applying affective computing into a driver assistance system. On driving assistance, one aspect is about emergency braking. Several researches have been analyzing emergency braking and proposed approaches to detect them. A more focused but significant (especially for elderly and beginner driver) case is mistakenly pressing accelerator instead of brake pedal during emergency braking, which often leads to accidents. This paper investigates researches on affective computing, affective sensors, emergency braking, and mistaken pedal pressing. It is also investigating on a possible approach to realize the objective of improving the existing driving assistance system using affective computing, on the case of mistaken pedal-pressing during emergency braking. For preliminary experiment, driving simulator’s brake pedal is manipulated to act as accelerator pedal during emergency braking, while observing driver’s change of expression, and measuring time approximation.

[1]  Todd Rose,et al.  The End of Average , 2015 .

[2]  D.M. Reichardt.  Approaching driver models which integrate models of emotion and risk , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[3]  Christian Mühl,et al.  Review of the Use of Electroencephalography as an Evaluation Method for Human-Computer Interaction , 2013, PhyCS.

[4]  Timothy L. Brown,et al.  Speech-Based Interaction with In-Vehicle Computers: The Effect of Speech-Based E-Mail on Drivers' Attention to the Roadway , 2001, Hum. Factors.

[5]  Ivan Tanev,et al.  Application of genetic programming and genetic algorithm in evolving emotion recognition module , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[6]  Matthias Scheutz,et al.  What we can and cannot (yet) do with functional near infrared spectroscopy , 2014, Front. Neurosci..

[7]  Tieniu Tan,et al.  Affective Computing: A Review , 2005, ACII.

[8]  Kazuya Takeda,et al.  Analysis of Real-World Driver's Frustration , 2011, IEEE Transactions on Intelligent Transportation Systems.

[9]  Gangyi Ding,et al.  A New Study on the Driver's Emotion Model , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.

[10]  John D. Lee,et al.  Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines , 2007, IEEE Transactions on Intelligent Transportation Systems.

[11]  Tomokazu Suzuki.  Method for Detecting Operation Mistakes with Accelerator Pedal , 2018 .

[12]  Fang Chen,et al.  Detection of driver cognitive distraction: An SVM based real-time algorithm and its comparison study in typical driving scenarios , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[13]  Hiroshi Mouri,et al.  Proposal of advanced emergency braking system adapted to the road surface condition , 2018 .

[14]  Shinobu Tanaka,et al.  Hemodynamic Responses during Simulated Automobile Driving in a Monotonous Situation , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Luzheng Bi,et al.  EEG-Based Detection of Driver Emergency Braking Intention for Brain-Controlled Vehicles , 2018, IEEE Transactions on Intelligent Transportation Systems.

[16]  Ivan Tanev,et al.  Effects of Cruising Speed on Steering Oscillations of Car Induced by Modeled Cognitively Impaired Human Driver , 2017 .

[17]  Matthias Scheutz,et al.  Functional near-infrared spectroscopy in human-robot interaction , 2013, HRI 2013.

[18]  S J Westerman,et al.  Mobile (cellular) phone use and driving: a critical review of research methodology , 2001, Ergonomics.

[19]  Tanja Schultz,et al.  Classification of mental tasks in the prefrontal cortex using fNIRS , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Kazuhiko Takahashi,et al.  Brain activity recognition with a wearable fNIRS using neural networks , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).

[21]  Antonello Rizzi,et al.  A real time classifier for emotion and stress recognition in a vehicle driver , 2012, 2012 IEEE International Symposium on Industrial Electronics.

[22]  Yoko Hoshi,et al.  Towards the next generation of near-infrared spectroscopy , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[23]  Ivan Tanev,et al.  Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles , 2017, Algorithms.

[24]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[25]  D. Strayer,et al.  Cell phone-induced failures of visual attention during simulated driving. , 2003, Journal of experimental psychology. Applied.