A Contextual Multimodal System for Increasing Situation Awareness and Takeover Quality in Conditionally Automated Driving

Abstract

Advanced driver assistances are becoming increasingly common in commercial cars, not only to assist but also to free drivers from manual driving whenever possible. Soon, drivers should be allowed to engage in non-driving-related tasks. The fact that responsibility for driving is shifting from humans to machines must be considered in the development of these assistances in order to guarantee safety and trust. In this article, we introduce AdVitam (for Advanced Driver-Vehicle Interaction to Make future driving safer), an autonomous system aiming at maintaining driver’s situation awareness and optimizing takeover quality during conditionally automated driving. The information conveyed to drivers is dynamically adapted to achieve these goals, depending on the driving environment and the driver’s physiological state. This system consists of three connected modules. The first module (Driver State) predicts the driver’s state with machine learning and physiological signals as inputs. The second module (Supervision) uses different interfaces (a haptic seat, a personal device, and ambient lights) to maintain the drivers’ situation awareness during the autonomous driving phases. The third module (Intervention) is a machine learning model that chooses the most appropriate combination among haptic, auditory, and visual modalities to request the driver to take over control and thus optimize takeover quality. To evaluate the system and each module independently, a preliminary user study with 35 drivers was conducted in a fixed-base driving simulator. All participants drove in two different environments (rural and urban). In addition, the activation of the Supervision and Intervention modules were manipulated as two between-subject factors. Results show that conveying information on the driving environment status through multimodal interfaces increases drivers’ situation awareness (i.e., better identification of potential problems in the environment) and trust in the automated vehicle. However, the system does not show positive outcomes on takeover quality. Besides, the Driver State module provided consistent predictions with the experimental manipulation. The system proposed in this paper could lead to better acceptance and safety when conditionally automated vehicles will be released by increasing drivers’ trust during phases of automated driving.

Keywords

• Automated driving
• driver’s state
• human-vehicle interaction
• machine learning
• multimodal interaction
• situation awareness