Maintaining driver attentiveness in shared-control autonomous driving

TL;DR

This paper proposes a self-adaptive system that monitors driver biometrics and car state, analyzes attentiveness with deep learning, and uses verified control strategies to enhance driver alertness in automated vehicles.

Contribution

It introduces a novel MAPE control loop integrating biometrics, deep neural analysis, and probabilistic model checking for driver attentiveness in autonomous driving.

Findings

Design of a self-adaptive monitor-analyze-plan-execute system

Use of deep neural networks for attentiveness analysis

Application of probabilistic model checking for controller synthesis

Abstract

We present a work-in-progress approach to improving driver attentiveness in cars provided with automated driving systems. The approach is based on a control loop that monitors the driver's biometrics (eye movement, heart rate, etc.) and the state of the car; analyses the driver's attentiveness level using a deep neural network; plans driver alerts and changes in the speed of the car using a formally verified controller; and executes this plan using actuators ranging from acoustic and visual to haptic devices. The paper presents (i) the self-adaptive system formed by this monitor-analyse-plan-execute (MAPE) control loop, the car and the monitored driver, and (ii) the use of probabilistic model checking to synthesise the controller for the planning step of the MAPE loop.