The use of belief theory to assess driver’s vigilance

Human error has been implicated as a causative factor in 85% of drivers’ and operators’ crashes, and lack of vigilance has been identified as the single most important factor in incidents involving human error. Driver vigilance could decline with sleepiness, fatigue or monotony. In Queensland, inattention and fatigue respectively contribute to 27% and 5% of reported crashes. Vigilance decline is characterised by an increased or absence of response to critical events. The current technology to assess and prevent vigilance decline is based on the isolate use of a particular device such as eye tracker or steering wheel movements. The reliability of these devices is debatable as the value of the readings could be highly inaccurate, uncertain, partial, conflictual or unreliable. Furthermore, there has been very little research examining the use of multiple devices to diagnose vigilance decline. The aim of this paper is to use belief theory to assess driver’s vigilance. Belief theory is a formal tool suitable for representing the inaccuracy, uncertainty and asynchnocity of knowledge. Our approach consists of merging a set of measurements, related to the environment, driver, and vehicle, gathered from different Advanced Driver Assistance Systems (ADAS). This paper presents the theoretical basis leading to the development of an advanced in-vehicle system capable of assessing vigilance decline. The development of such a tool has a potential to be a major contributor to reducing death and injury rates due hypovigilance related driver’s errors.

[1]  AubertDidier,et al.  Cooperative Fusion for Multi-Obstacles Detection With Use of Stereovision and Laser Scanner , 2005 .

[2]  Andry Rakotonirainy,et al.  Design of context-aware systems for vehicles using complex system paradigms , 2005, CONTEXT Workshop on Safety and Context.

[3]  D. Gruyer,et al.  Heterogeneous multi-criteria combination with partial or full information , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[4]  G. Matthews,et al.  Task-induced fatigue states and simulated driving performance , 2002, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[5]  D. Gruyer,et al.  A new multi-lanes detection using multi-camera for robust vehicle location , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[6]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[7]  D. Aubert,et al.  A reliable road lane detector approach combining two vision-based algorithms , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[8]  Qiang Ji,et al.  Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance , 2002, Real Time Imaging.

[9]  Andry Rakotonirainy,et al.  Advancement in Advanced Driving Assistance Systems tools: Integrating vehicle dynamics, environmental perception and drivers' behaviours to assess vigilance , 2005 .

[10]  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).

[11]  Alexander Zelinsky,et al.  Vision In and Out of Vehicles: Integrated Driver and Road Scene Monitoring , 2002, ISER.

[12]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[13]  A Amditis,et al.  Advanced driver monitoring: the AWAKE project , 2001 .

[14]  Andry Rakotonirainy,et al.  An investigation into peripheral physiological markers that predict monotony , 2004 .

[15]  F Sagberg,et al.  Fatigue, sleepiness and reduced alertness as risk factors in driving , 2004 .

[16]  B. Mourllion,et al.  Multi-hypotheses tracking algorithm based on the belief theory , 2005, 2005 7th International Conference on Information Fusion.

[17]  Alexander Zelinsky,et al.  Vision In and Out of Vehicles: Integrated Driver and Road Scene Monitoring , 2004, Int. J. Robotics Res..

[18]  Mubarak Shah,et al.  Monitoring head/eye motion for driver alertness with one camera , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[19]  M Vallet,et al.  HOW TO DETECT LOW VIGILANCE OF THE DRIVER BY SOME DYNAMIC VEHICLE'S AND ELECTROPHYSIOLOGICAL PARAMETERS , 1993 .

[20]  Jacques Bergeron,et al.  Monotony of road environment and driver fatigue: a simulator study. , 2003, Accident; analysis and prevention.

[21]  L. Petersson,et al.  Road scene monotony detection in a fatigue management driver assistance system , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[22]  Dick de Waard,et al.  The measurement of drivers' mental workload , 1996 .

[23]  Dean A. Pomerleau,et al.  Driver-adaptive lane departure warning systems , 1999 .

[24]  D. Dinges,et al.  EVALUATION OF TECHNIQUES FOR OCULAR MEASUREMENT AS AN INDEX OF FATIGUE AND THE BASIS FOR ALERTNESS MANAGEMENT , 1998 .

[25]  Andry Rakotonirainy Human-Computer Interactions: Research Challenges for In-vehicle Technology , 2003 .