How the Autonomic Nervous System and Driving Style Change With Incremental Stressing Conditions During Simulated Driving

Antonio Lanatà · Gaetano Valenza · Alberto Greco · Claudio Gentili · Riccardo Bartolozzi · Francesco Bucchi +2 more

145 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Human-Automation Interaction and Safety Heart Rate Variability and Autonomic Control Emotion and Mood Recognition

Abstract

This paper reports on the autonomic nervous system (ANS) changes and driving style modifications as a response to incremental stressing level stimulation during simulated driving. Fifteen subjects performed a driving simulation experiment consisting of three driving sessions. Starting from a first session where participants performed a steady motorway driving, the experimental protocol includes two additional driving sessions with incremental stress load. More specifically, the first stressing load consists of randomly administering mechanical stimuli to the vehicle during a steady motorway driving by means of a series of sudden and unexpected skids, such as those produced by a strong wind gust. These skids were supposed to produce in the driver a given level of stress. In order to assess this mental workload, dedicated psychological tests were performed. The second stressing load implied an incremental psychological load, consisting of a battery of time pressing arithmetical questions, added to the mechanical stimuli. For the whole experimental session, the driver's physiological signals and the vehicle's mechanical parameters were recorded and analyzed. In this paper, the ANS changes were investigated in terms of heart rate variability, respiration activity, and electrodermal response along with mechanical information such as that coming from steering wheel angle corrections, velocity changes, and time responses. Results are satisfactory and promising. In particular, significant statistical differences were found among the three driving sessions with an increasing stress level both in ANS responses and mechanical parameter changes. In addition, a good recognition of these sessions was carried out by pattern classification algorithms achieving an accuracy greater than 90%.

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References

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