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Identification of Biomarkers to Predict Driver Take-over Control Quality (ANTIDOTE)

Primary Purpose

Healthy Subjects, Attention Deficit

Status

Completed

Phase

Not Applicable

Locations

France

Study Type

Interventional

Intervention

Driving simulator sessions

About this trial

This is an interventional basic science trial for Healthy Subjects focused on measuring Automated driving, Conditional automation, Driver distraction, Driver take-over quality, Biomarkers

Eligibility Criteria

20 Years - 75 Years (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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)

Sites / Locations

Bordeaux University Hospital

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Driving session

Arm Description

The volunteers will be placed in a driving simulator that will simulate autonomous highway driving.

Outcomes

Primary Outcome Measures

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

Secondary Outcome Measures

EEG (electroencephalogram)

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

ECG (electrocardiogram)

Physiological parameter : ECG recordings and heart rate variability based on time and frequency domain will be analyzed.

EMG (electromyogram)

Physiological parameter : surface EMG will be recorded and Derive Average Rectified, derive Integrated Root and means Square EMG will be analyzed and EMG Frequency & Power Analysis.

Electrodermal activity 1 (EDA)

Physiological parameter : EDA will be recorded and skin conductance level analyzed.

Electrodermal activity 2 (EDA)

Physiological parameter : EDA will be recorded and skin conductance response analyzed.

Respiration

Physiological parameter : Respiratory frequency recorded

Physical activity

Physiological parameter : Physical activity expressed in count/min

Eye tracking

Physiological parameter :Eye tracking will be recorded and point of gaze, Perclos, blinks, diameters of pupils analyzed.

Subjective driving take-over control quality: Visual analogic scale

Visual analogue scale to assess subjective driving take-over control quality (Subjective scale). The scale ranges from 0 " bad" to 100 "good"

Subjective level of attention and distraction before take-over control request

Visual analogue scale to assess subjective level of attention and distraction just before take-over control request The scale ranges from 0 "attentive" to 100 "inattentive"

Full Information

NCT ID

NCT04188626

First Posted

November 18, 2019

Last Updated

September 18, 2020

Sponsor

PSA Automobiles S.A.

Collaborators

University of Bordeaux, University Hospital, Bordeaux

1. Study Identification

Unique Protocol Identification Number

NCT04188626

Brief Title

Identification of Biomarkers to Predict Driver Take-over Control Quality

Acronym

ANTIDOTE

Official Title

Identification of Physiological and Behavioural Biomarkers to Predict Take-over Control Quality in Level 3 Conditionally Automated Vehicles

Study Type

Interventional

2. Study Status

Record Verification Date

September 2020

Overall Recruitment Status

Completed

Study Start Date

December 9, 2019 (Actual)

Primary Completion Date

August 6, 2020 (Actual)

Study Completion Date

August 6, 2020 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title

Sponsor

Name of the Sponsor

PSA Automobiles S.A.

Collaborators

University of Bordeaux, University Hospital, Bordeaux

4. Oversight

Studies a U.S. FDA-regulated Drug Product

Studies a U.S. FDA-regulated Device Product

Data Monitoring Committee

5. Study Description

Brief Summary

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.

Detailed Description

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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study

Healthy Subjects, Attention Deficit

Keywords

Automated driving, Conditional automation, Driver distraction, Driver take-over quality, Biomarkers

7. Study Design

Primary Purpose

Basic Science

Study Phase

Not Applicable

Interventional Study Model

Single Group Assignment

Model Description

Monocentric preliminary study including an experimental driving session in a driving simulator.

Masking

None (Open Label)

Allocation

N/A

Enrollment

32 (Actual)

8. Arms, Groups, and Interventions

Arm Title

Driving session

Arm Type

Experimental

Arm Description

The volunteers will be placed in a driving simulator that will simulate autonomous highway driving.

Intervention Type

Behavioral

Intervention Name(s)

Driving simulator sessions

Intervention Description

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.

Primary Outcome Measure Information:

Title

Quality of driving take-over behaviour

Description

Time Frame

8 secondes after take-over request

Secondary Outcome Measure Information:

Title

EEG (electroencephalogram)

Description

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

Time Frame