Towards Runtime Monitoring of Complex System Requirements for Autonomous Driving Functions

TL;DR

This paper explores the use of Traffic Sequence Charts (TSCs) for formalizing and monitoring complex system requirements of autonomous driving functions during validation, offering a promising approach for compliance verification.

Contribution

It introduces a novel concept for runtime monitoring of ADF requirements using TSCs, demonstrating its application and benefits in validation scenarios.

Findings

TSCs are effective for formalizing complex traffic requirements.

The proposed monitor provides continuous runtime verdicts during validation.

TSC-based monitoring can be extended to other AI safety and traffic data collection areas.

Abstract

Autonomous driving functions (ADFs) in public traffic have to comply with complex system requirements that are based on knowledge of experts from different disciplines, e.g., lawyers, safety experts, psychologists. In this paper, we present a research preview regarding the validation of ADFs with respect to such requirements. We investigate the suitability of Traffic Sequence Charts (TSCs) for the formalization of such requirements and present a concept for monitoring system compliance during validation runs. We find TSCs, with their intuitive visual syntax over symbols from the traffic domain, to be a promising choice for the collaborative formalization of such requirements. For an example TSC, we describe the construction of a runtime monitor according to our novel concept that exploits the separation of spatial and temporal aspects in TSCs, and successfully apply the monitor on exemplary runs. The monitor continuously provides verdicts at runtime, which is particularly beneficial in ADF validation, where validation runs are expensive. The next open research questions concern the generalization of our monitor construction, the identification of the limits of TSC monitorability, and the investigation of the monitor's performance in practical applications. Perspectively, TSC runtime monitoring could provide a useful technique in other emerging application areas such as AI training, safeguarding ADFs during operation, and gathering meaningful traffic data in the field.