Subjective overload: impact of driving experience and situation complexity

The aim of the present study is to identify when drivers perceive that they are overloaded by an unexpected event, as a function of the situation complexity and their driving practice. The main contribution of this paper to the Cognitive Ergonomics field is that the experimentation allows identifying several factors which show that drivers' activity is not always adapted to unexpected situations. Fifty-seven young drivers (15 novices with a traditional driving education, 12 early-trained novices, 15 drivers with three years of experience and 15 drivers with at least five years of experience) were randomly assigned to three levels of situation complexity (simple, moderately complex and very complex) in a driving simulator. Self-reported levels of workload during unexpected pedestrian crossings were collected by a questionnaire (NASA-TLX) between each situation. Driving performance (reaction time to a pedestrian crossing that suddenly appears; number of collisions with this pedestrian) was also analysed. The experiment assessed the effect of four levels of driving experience and three levels of situation complexity on subjective workload and driving performance. Results confirmed that early-trained drivers have a higher subjective workload than more experienced drivers. Nevertheless, whatever the situation and the group, the increase of workload and RT provoke an increase of the number of collisions. Therefore, the driving automation acquired with experience doesn't allow avoiding accidents when an unexpected event appears. Subjective and physiological data will be compared in a second study in order to identify if drivers' behavior is more based on their state perception or on their physiological change.

[1]  Catherine Berthelon,et al.  The Evaluation of Traditional and Early Driver Training With Simulated Accident Scenarios , 2011, Hum. Factors.

[2]  Albert Kircher,et al.  Driver experience and cognitive workload in different traffic environments. , 2006, Accident; analysis and prevention.

[3]  Ray Fuller HUMAN FACTORS AND DRIVING , 2002 .

[4]  E. Hollnagel The changing nature of risk , 2008 .

[5]  Jens Rasmussen,et al.  Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  A James McKnight,et al.  Young novice drivers: careless or clueless? , 2003, Accident; analysis and prevention.

[7]  Wolfgang Fastenmeier,et al.  Driving Task Analysis as a Tool in Traffic Safety Research and Practice , 2007 .

[8]  A.W.K. Gaillard,et al.  Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks , 2003 .

[9]  M. Eysenck,et al.  Anxiety and Performance: The Processing Efficiency Theory , 1992 .

[10]  Jean-Michel Hoc,et al.  Cognitive styles as an explanation of experts' individual differences: A case study in computer-assisted troubleshooting diagnosis , 2006, Int. J. Hum. Comput. Stud..

[11]  David Meister,et al.  Behavioral foundations of system development , 1984 .

[12]  Saskia de Craen,et al.  The development of a method to measure speed adaptation to traffic complexity: identifying novice, unsafe, and overconfident drivers. , 2008, Accident; analysis and prevention.

[13]  Alessandro De Gloria,et al.  Towards the Automotive HMI of the Future: Overview of the AIDE-Integrated Project Results , 2010, IEEE Transactions on Intelligent Transportation Systems.

[14]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[15]  Stephane Espie,et al.  Driving Simulators Validation: The Issue of Transferability of Results Acquired on Simulator , 2005 .

[16]  P. V. Elslande,et al.  Erreurs de conduite et besoins d'aide : une approche accidentologique en ergonomie , 2003 .

[17]  A. Tricot Charge cognitive et apprentissage. Une présentation des travaux de John Sweller , 1998 .