Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection
URL: http://arxiv.org/abs/2308.15895v1
archive: archived pipeline: cataloged verified
Abstract
Semi-autonomous driving, as it is already available today and will eventually become even more accessible, implies the need for driver and automation system to reliably work together in order to ensure safe driving. A particular challenge in this endeavour are situations in which the vehicle's automation is no longer able to drive and is thus requesting the human to take over. In these situations the driver has to quickly build awareness for the traffic situation to be able to take over control and safely drive the car. Within this context we present a software and hardware framework to asses how aware the driver is about the situation and to provide human-centred assistance to help in building situation awareness. The framework is developed as a modular system within the Robot Operating System (ROS) with modules for sensing the environment and the driver state, modelling the driver's situation awareness, and for guiding the driver's attention using specialized Human Machine Interfaces (HMIs). A particular focus of this paper is on an Answer Set Programming (ASP) based approach for modelling and reasoning about the driver's interpretation and projection of the scene. This is based on scene data, as well as eye-tracking data reflecting the scene elements observed by the driver. We present the overall application and discuss the role of semantic reasoning and modelling cognitive functions based on logic programming in such applications. Furthermore we present the ASP approach for interpretation and projection of the driver's situation awareness and its integration within the overall system in the context of a real-world use-case in simulated as well as in real driving.
Summary
Application paper (Suchan & Osterloh, DLR) presenting the SituWare framework: a modular ROS-based software/hardware system that assesses a semi-autonomous vehicle driver's situation awareness during takeover requests and provides human-centred attention-guidance HMI. The paper's particular focus is an Answer Set Programming (ASP) approach to symbolically model the driver's interpretation and projection of the traffic scene, combining scene data with eye-tracking observations of which scene elements the driver actually fixated. The ASP approach reasons over Endsley-style SA levels (perception, comprehension, projection) as declarative rules, and is integrated with sensor and HMI modules to support a real-world use case demonstrated in both simulated and on-road driving. The contribution is methodological: arguing that declarative logic programming offers transparent, semantically grounded modelling of cognitive functions for SA assessment in semi-autonomous driving.
Key finding
An ASP-based declarative framework integrated with eye-tracking and scene sensors provides transparent symbolic modelling of driver SA (perception/comprehension/projection) during semi-autonomous takeover, demonstrated in simulated and real driving.
Methodology
experimental
Sample size: Exp 1: N=10; Exp 2: N=20
Quality score: 5 / 5