Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents

TL;DR

This paper extends structural causal models to better suit autonomous vehicle systems by enhancing modularity, temporal representation, and dynamic agent interactions, aiming to improve causal reasoning and safety in autonomous driving.

Contribution

The paper introduces theoretical extensions to structural causal models, enabling modular, temporal, and dynamic agent representations suitable for autonomous vehicle applications.

Findings

Extended causal models support dynamic sets of agents.

Temporal causal representations maintain constant space complexity.

Extensions facilitate causal reasoning in autonomous vehicle interactions.

Abstract

In this work we aim to bridge the divide between autonomous vehicles and causal reasoning. Autonomous vehicles have come to increasingly interact with human drivers, and in many cases may pose risks to the physical or mental well-being of those they interact with. Meanwhile causal models, despite their inherent transparency and ability to offer contrastive explanations, have found limited usage within such systems. As such, we first identify the challenges that have limited the integration of structural causal models within autonomous vehicles. We then introduce a number of theoretical extensions to the structural causal model formalism in order to tackle these challenges. This augments these models to possess greater levels of modularisation and encapsulation, as well presenting temporal causal model representation with constant space complexity. We also prove through the extensions we have introduced that dynamically mutable sets (e.g. varying numbers of autonomous vehicles across time) can be used within a structural causal model while maintaining a relaxed form of causal stationarity. Finally we discuss the application of the extensions in the context of the autonomous vehicle and service robotics domain along with potential directions for future work.