Identification of Biomarkers to Predict Driver Take-over Control Quality

PSA Automobiles S.A.32 enrolled

Overview

At level 3 conditionally automated, the vehicle ensures driving and the driver disengages from driving to perform another activity independent of driving (ex: read a book, play on his phone ....). However, drivers are expected to be available to take over control for the case of system failure or limitation. This take-over control must take place in a limited time, very short, of the order of a few seconds. To take-over control of the vehicle quickly and efficiently, the driver must be, at the time of take-over, vigilant, efficient, and attentive to the environment and focused on the take-over of manual driving. Predicting the driver's reengagement capabilities to ensure that the driver will be able to take-over control of the vehicle is crucial at level 3 of autonomous driving. The objective of ANTIDOTE is to determine physiological and behavioural parameters capable of predicting the take-over quality in level 3 conditionally automated vehicles in a simulated highway driving situation in healthy drivers or drivers with attention disorders.

At level 3 conditionally automated, the vehicle ensures driving and the driver disengages from driving to perform non-related driving tasks (ex: read a book, play on his phone ....). However, drivers are expected to be available to take over control for the case of system failure or limitation. This take-over control must take place in a limited time, very short, of the order of a few seconds. To take-over control of the vehicle quickly and efficiently, the driver must be, at the time of take-over, vigilant, efficient, and attentive to the environment and focused on the take-over of manual driving. Predicting the driver's reengagement capabilities to ensure that the driver will be able to take-over control of the vehicle is crucial at level 3 of autonomous driving. In this context, the objective of ANTIDOTE is to determine physiological and behavioural parameters capable of predicting the take-over quality in level 3 conditionally automated vehicles in a simulated highway driving situation. This study will examine how engagement will impact take-over control quality in 6 non-driving related secondary tasks. A driving simulator study will be conducted and data from a total of 32 healthy drivers and 16 drivers with attention disorders will be used to evaluate take-over quality. Electrophysiological (EEG, ECG, EDA, EMG, respiration) and behavioral data will be recorded before, during and after the take-over control.

Study Type

INTERVENTIONAL

Allocation

Interventions

Driving simulator sessionsBEHAVIORAL

The volunteers will be placed in a driving simulator that will simulate autonomous highway driving. This autonomous driving will be interrupted by take-over requests related to events that disrupt autonomous driving. During autonomous driving, the driver will have to disengage from driving by performing non-related driving tasks. During each non-related driving tasks, a take-over request will be sent. Electrophysiological (EEG, ECG, EDA, EMG, respiration) and behavioural data will be recorded before, during and after the take-over control.

Eligibility

Sex: ALLMin age: 20 YearsMax age: 75 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: Common inclusion criteria: * male or female aged between 20 and 75 years old * BMI between 18 and 27 * Subject size between 1.50 m and 1.95 m * Without sleep complains (Item of Basic Nordic Sleep Questionnaire ≤ 3) * Without excessive daytime sleepiness (Epworth score ≤ 11) * Non-professional drivers * Subjects with a driver's license for at least one year * Subjects driving at least 5000 km per year. * Having normal visual acuity (correction with lenses accepted) and normal color vision * Affiliated to a national health service * Having given written informed consent to participate in the trial. Healthy volunteers specific inclusion criteria: * SCL90R score \< 60 for anxiety and depression subscales * MMSE ≥ 30 ADHD patients specific inclusion criteria: * Patients with an ADHD disorder according to DSM 5, * Patients agreeing to discontinue psychostimulant treatment 48 hours prior to the experimental session, Exclusion Criteria: * Severe life-threatening conditions in the short term, * Unstable endocrine diseases * Progressive cardiovascular diseases * Progressive neurological diseases treated or not, * Addiction to a substance * Night and shift-workers who has taken a constraints in the last 72 hours, * Psychotropic medication taking * Benzodiazepine or Z-drug medication taking * Cardiotropic medication taking * Volunteers who need glasses to drive * Having simulator-sickness during the first practice session Healthy volunteers specific inclusion criteria: * Psychiatric co-morbidities: current major depressive episode, current hypomanic or manic episode, psychotic disorders, autism spectrum disorder * Exceeded consumption of coffee, tea or caffeinated drinks(\> 5 cups / day) * Exceeded consumption of alcohol drinks (\> 2 drinks / day during the last 6 months) ADHD patients specific inclusion criteria: * Psychiatric co-morbidities: current major depressive episode, current hypomanic or manic episode, psychotic disorders, autism spectrum disorder (except ADHD) * Exceeded consumption of alcohol drinks(\> 3 drinks / day during the last 6 months)

Outcomes

Primary Outcomes

Quality of driving take-over behaviour

Quality of driving take-over behaviour (Good/bad) will be assessed by collision (collision or driving off the road) and critical encounters (Time To Collision). Time to collision (TTC) refers to the time required for the vehicle to collide with the stationary obstacle obstructing the driving lane if it continues at its speed at the time it changes to the next lane completely. Good : no collision AND TTC \>= 1.5 secondes Bad : collision or no collision AND TTC \< 1.5 secondes

Time frame: 8 secondes after take-over request

Secondary Outcomes

EEG (electroencephalogram)

Physiological parameter: EEG will be recorded and Alpha, theta and gamma activity will be analyzed in the waking EEG.