Supporting Safe Decision Making Through Holistic System-Level Representations & Monitoring -- A Summary and Taxonomy of Self-Representation Concepts for Automated Vehicles
Marcus Nolte, Inga Jatzkowski, Susanne Ernst, and Markus Maurer

TL;DR
This paper discusses the importance of holistic system-level representations and monitoring for automated vehicles, emphasizing their role in safety, system performance assessment, and enabling higher automation levels.
Contribution
It introduces a taxonomy of self-representation concepts and highlights their significance in scene understanding and decision-making for automated vehicles.
Findings
Holistic system-level monitoring enhances safety and system performance.
Self-representation models are crucial for scene and situation understanding.
Monitoring enables tactical decision-making and fallback activation.
Abstract
The market introduction of automated vehicles has motivated intense research efforts into the safety of automated vehicle systems. Unlike driver assistance systems, SAE Level 3+ systems are not only responsible for executing (parts of) the dynamic driving task (DDT), but also for monitoring the automation system's performance at all times. Key components to fulfill these surveillance tasks are system monitors which can assess the system's performance at runtime, e.g. to activate fallback modules in case of partial system failures. In order to implement reasonable monitoring strategies for an automated vehicle, holistic system-level approaches are required, which make use of sophisticated internal system models. In this paper we present definitions and an according taxonomy, subsuming such models as a vehicle's self-representation and highlight the terms' roles in a scene and situation…
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Taxonomy
TopicsHuman-Automation Interaction and Safety · Safety Systems Engineering in Autonomy
