DeepAI AI Chat
Log In Sign Up

Grasping Causality for the Explanation of Criticality for Automated Driving

by   Tjark Koopmann, et al.

The verification and validation of automated driving systems at SAE levels 4 and 5 is a multi-faceted challenge for which classical statistical considerations become infeasible. For this, contemporary approaches suggest a decomposition into scenario classes combined with statistical analysis thereof regarding the emergence of criticality. Unfortunately, these associational approaches may yield spurious inferences, or worse, fail to recognize the causalities leading to critical scenarios, which are, in turn, prerequisite for the development and safeguarding of automated driving systems. As to incorporate causal knowledge within these processes, this work introduces a formalization of causal queries whose answers facilitate a causal understanding of safety-relevant influencing factors for automated driving. This formalized causal knowledge can be used to specify and implement abstract safety principles that provably reduce the criticality associated with these influencing factors. Based on Judea Pearl's causal theory, we define a causal relation as a causal structure together with a context, both related to a domain ontology, where the focus lies on modeling the effect of such influencing factors on criticality as measured by a suitable metric. As to assess modeling quality, we suggest various quantities and evaluate them on a small example. As availability and quality of data are imperative for validly estimating answers to the causal queries, we also discuss requirements on real-world and synthetic data acquisition. We thereby contribute to establishing causal considerations at the heart of the safety processes that are urgently needed as to ensure the safe operation of automated driving systems.


Fundamental Considerations around Scenario-Based Testing for Automated Driving

The homologation of automated vehicles, being safety-critical complex sy...

On Quantification for SOTIF Validation of Automated Driving Systems

Automated driving systems are safety-critical cyber-physical systems who...

Architecting Safety Supervisors for High Levels of Automated Driving

The complexity of automated driving poses challenges for providing safet...

The Causal Loss: Driving Correlation to Imply Causation

Most algorithms in classical and contemporary machine learning focus on ...

A taxonomy for quality in simulation-based development and testing of automated driving systems

Ensuring the safety and performance requirements of automated driving sy...