Navigating Dimensionality through State Machines in Automotive System Validation

TL;DR

This paper proposes a state chart-based methodology to model and analyze interactions with external systems in automated vehicle validation, aiming to reduce scenario space and improve testing efficiency.

Contribution

It introduces a novel approach combining state charts with scenario-based testing to handle complex external interactions in vehicle validation.

Findings

Reduces the number of scenarios needed for testing.

Enhances testability and coverage analysis.

Demonstrated on a simulated intersection with roadside units.

Abstract

The increasing automation of vehicles is resulting in the integration of more extensive in-vehicle sensor systems, electronic control units, and software. Additionally, vehicle-to-everything communication is seen as an opportunity to extend automated driving capabilities through information from a source outside the ego vehicle. However, the validation and verification of automated driving functions already pose a challenge due to the number of possible scenarios that can occur for a driving function, which makes it difficult to achieve comprehensive test coverage. Currently, the establishment of Safety Of The Intended Functionality ( SOTIF ) mandates the implementation of scenario-based testing. The introduction of additional external systems through vehicle-to-everything further complicates the problem and increases the scenario space. In this paper, a methodology based on state charts is proposed for modeling the interaction with external systems, which may remain as black boxes. This approach leverages the testability and coverage analysis inherent in state charts by combining them with scenario-based testing. The overall objective is to reduce the space of scenarios necessary for testing a networked driving function and to streamline validation and verification. The utilization of this approach is demonstrated using a simulated signalized intersection with a roadside unit that detects vulnerable road users.