Predicting Takeover Quality in Conditionally Autonomous Vehicles based on Takeover Request Modalities, Driver Physiological State and the Environment
Authors: De Salis Emmanuel , Meteier Quentin , Capallera Marine , Pelletier Colin , Angelini Leonardo , Abou Khaled Omar , Mugellini Elena , Widmer Marino , Carrino Stefano 
Published: April 2022

ContextConditionally autonomous vehicles are studied for numerous reasons, due to the dual responsibility in the driving task, switching from the car to the driver depending on the situation. This transition of control, namely the takeover, is usually not a concern when initiated by the driver. However, when the car issues a takeover request (TOR), it does it without consideration for the driver state, or anything else. In such a situation, the driver can be out of the driving loop, unaware of the current environment or focusing on a non-driving-related task (NDRT), impacting the takeover quality.>ObjectiveIn this study, the goal is to find out if a Machine Learning can take advantages of many different factors and how they interact together, to create a model able to predict the takeover quality. Factors highlighted by the literature were considered: the driver physiological state, the external environment, the driver current activity and the modality of the TOR. Some studies tried to predict takeover quality, but they usually study the TOR modality impact, the NDRT or the driver state. In this paper, we attempt to study the bigger picture and grasps the impact of the interaction between those multiple factors. >Methodology>>Experiment15 drivers’ physiological signals (EDA, ECG and respiration) were recorded during a 50 minutes driving session on a fixed-base driving simulator. They encountered 9 takeover situations each, caused by a stationary obstacle appearing on a road with a time-to-collision of around 7 seconds. Physiological data were processed on the last 90 seconds before each TOR. The possible TOR modalities were combinations of visual (icon on the dashboard), auditory (short beep sound) and haptic (vibrating seat). Combinations tested were visual-haptic, visual-auditory and visual-auditory-haptic. The drivers had 3 task for each third of the driving session: Visual n-back task, auditory n-back task or monitoring the car (no task).Half the participants (8) had an adverse weather during the driving session, with low luminosity and heavy rain, while the other half (7) had a sunny weather. Reaction time between the TOR and the takeover, and the maximum steering wheel angle attained during the takeover process were recorded as takeover quality metrics. >>Machine LearningTakeover quality metrics were combined to create a unique label to predict. State-of-the-art Feature Selection techniques were applied to keep only to more relevant features, and after outliers suppressions and data processing, 80 takeovers were kept for the Machine Learning models training. Data Augmentation methods, such as SMOGN and Random Noise were implemented and tested to boost the training dataset. KNeighbors Regressor, Support Vector Regressor (SVR), Random Forest Regressors (RFR) and Neural Networks (NN) were trained using a grid search approach and cross validation. Evaluation was done using MSE, MAE and R2.>ResultsThe NN gave the best results, with a MSE of 0.0538, a R2 of 0.1040 and a MAE of 0.1614. The results are similar to the most recent literature. Most important physiological features included indicators of the Respiratory Sinus Arrhythmia, Heart Rate Variability and Skin Conductance Response