Toward a hybrid causal framework for autonomous vehicle safety analysis

Autonomous Vehicles (AVs), also known as self-driving cars, are a potentially transformative technology, but developing and demonstrating AV safety remains an open question. AVs offer some unique challenges that stretch the limits of traditional safety engineering practices. Most current safety standards and methodologies in the AV industry were not originally intended for application to autonomous vehicles, and they have significant limitations and shortcomings. In this article, we analyze the literature to first build an argument that a new safety framework is needed for AVs. We then use the identified limitations of current methodologies as a basis to formulate a set of fundamental requirements that must be met by any proposed AV safety framework. We propose a new AV safety framework based on the Hybrid Causal Logic (HCL) methodology, which combines Event Sequence Diagrams (ESDs), Fault Tree Analysis (FTA), and Bayesian Networks (BNs). The HCL framework is developed at a conceptual level and then evaluated versus the identified fundamental requirements. To further illustrate how the framework may meet the requirements, a simple example of an AV perception system scenario is developed using the HCL framework and evaluated. The results demonstrate that the HCL framework provides an integrated approach that has the potential to satisfy more completely the fundamental requirements than the current methodologies.

Automated summary

This article analyzes the limitations of current safety standards and methodologies in the AV industry and proposes a new AV safety framework based on Hybrid Causal Logic. The framework combines Event Sequence Diagrams, Fault Tree Analysis, and Bayesian Networks, providing an integrated approach that has the potential to more completely satisfy fundamental requirements.