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

arXiv: https://arxiv.org/abs/2406.01384 · HTML: https://mlsanity.com/paper/2406.01384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)

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

- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Foundations of Structural Causal Models with Cycles and Latent Variables](https://mlsanity.com/paper/1611.06221/) (2016)
- [From Ordinary Differential Equations to Structural Causal Models: the deterministic case](https://mlsanity.com/paper/1408.2063/) (2014)
- [Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions](https://mlsanity.com/paper/2210.16535/) (2022)
- [CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments](https://mlsanity.com/paper/2304.06848/) (2023)
- [A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks](https://mlsanity.com/paper/2209.05255/) (2022)
- [Causal Explanations for Sequential Decision-Making in Multi-Agent Systems](https://mlsanity.com/paper/2302.10809/) (2023)
- [Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour](https://mlsanity.com/paper/2302.00064/) (2023)
- [Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs](https://mlsanity.com/paper/2302.12676/) (2023)
- [Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities](https://mlsanity.com/paper/2201.11824/) (2022)
- [Causal Explanations for Sequential Decision Making Under Uncertainty](https://mlsanity.com/paper/2205.15462/) (2022)

## Built on by (most cited first, 1 of 1)

- [CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery](https://mlsanity.com/paper/2607.03177/) (2026)

## Abstract (arXiv)

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.