# Grasping Causality for the Explanation of Criticality for Automated Driving

arXiv: https://arxiv.org/abs/2210.15375 · HTML: https://mlsanity.com/paper/2210.15375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment](https://mlsanity.com/paper/2012.06319/) (2020)
- [An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference](https://mlsanity.com/paper/1906.00442/) (2019)
- [Fundamental Considerations around Scenario-Based Testing for Automated Driving](https://mlsanity.com/paper/2005.04045/) (2020)
- [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022)
- [Object-Oriented Bayesian Networks](https://mlsanity.com/paper/1302.1554/) (2013)
- [Identifiability and Transportability in Dynamic Causal Networks](https://mlsanity.com/paper/1610.05556/) (2016)
- [On the validation of complex systems operating in open contexts](https://mlsanity.com/paper/1902.10517/) (2019)
- [Phenomenon-Signal Model: Formalisation, Graph and Application](https://mlsanity.com/paper/2207.09996/) (2022)
- [Path Dependent Structural Equation Models](https://mlsanity.com/paper/2008.10706/) (2020)

## Built on by (most cited first, 2 of 2)

- [CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models](https://mlsanity.com/paper/2308.01375/) (2023)
- [Causality-based Transfer of Driving Scenarios to Unseen Intersections](https://mlsanity.com/paper/2404.02046/) (2024)

## Abstract (arXiv)

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.