A framework of safety analysis with temporal feature based on MBSA and case study for ACC system

Abstract

The safety of automotive Adaptive Cruise Control (ACC) system is of great significance to prevent fatigue driving, improve driving comfort, reduce accident rate and promote the development of intelligent transportation and autonomous driving technology. However, the current safety analysis of ACC lacks consideration of the temporal dynamic property, so it is necessary to establish a set of safety analysis methods to consider the temporal characteristics. This paper proposes a new safety analysis method based on MBSA framework and introduces temporal features. Altarica3.0 is a high-level modeling language for safety analysis, and its basic mathematical form is Guardian Transformation System (GTS). In this paper, we outline an analysis approach that converts failure behavioral models (GTS) to temporal fault trees (TFTs), which can be analyzed using Pandora a recent technique for introducing temporal logic to fault trees. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. In order to improve the safety of the system, the proposal extends Bayesian Networks with Pandora and results to dependability analysis with temporal relationships to provide more reliable basis for safety design. As a typical case study, the safety analysis method proposed in this paper is applied to the safety analysis of adaptive cruise system, and the results show the effectiveness of the proposed method. Furthermore, it also provides new technologies for the automation and intelligence of safety analysis for smart internet of vehicle.