# Context-aware, Ante-hoc Explanations of Driving Behaviour

arXiv: https://arxiv.org/abs/2511.14428 · HTML: https://mlsanity.com/paper/2511.14428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 18 Nov 2025.

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

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1804.06760/) (2018)
- [Formalizing Traffic Rules for Machine Interpretability](https://mlsanity.com/paper/2007.00330/) (2020)
- [Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload](https://mlsanity.com/paper/1905.08878/) (2019)
- [Is Trust Correlated With Explainability in AI? A Meta-Analysis](https://mlsanity.com/paper/2504.12529/) (2025)
- [Towards Self-Explainable Cyber-Physical Systems](https://mlsanity.com/paper/1908.04698/) (2019)
- [Towards a Characterization of Explainable Systems](https://mlsanity.com/paper/1902.03096/) (2019)
- [What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence](https://mlsanity.com/paper/2409.05731/) (2024)
- [Towards Human-centered Design of Explainable Artificial Intelligence (XAI): A Survey of Empirical Studies](https://mlsanity.com/paper/2410.21183/) (2024)
- [Towards Runtime Monitoring of Complex System Requirements for Autonomous Driving Functions](https://mlsanity.com/paper/2209.14032/) (2022)
- [From Specification Models to Explanation Models: An Extraction and Refinement Process for Timed Automata](https://mlsanity.com/paper/2209.14034/) (2022)
- [A Consistency Analysis Method for Traffic Sequence Charts](https://mlsanity.com/paper/2409.03774/) (2024)

## Abstract (arXiv)

Autonomous vehicles (AVs) must be both safe and trustworthy to gain social acceptance and become a viable option for everyday public transportation. Explanations about the system behaviour can increase safety and trust in AVs. Unfortunately, explaining the system behaviour of AI-based driving functions is particularly challenging, as decision-making processes are often opaque. The field of Explainability Engineering tackles this challenge by developing explanation models at design time. These models are designed from system design artefacts and stakeholder needs to develop correct and good explanations. To support this field, we propose an approach that enables context-aware, ante-hoc explanations of (un)expectable driving manoeuvres at runtime. The visual yet formal language Traffic Sequence Charts is used to formalise explanation contexts, as well as corresponding (un)expectable driving manoeuvres. A dedicated runtime monitoring enables context-recognition and ante-hoc presentation of explanations at runtime. In combination, we aim to support the bridging of correct and good explanations. Our method is demonstrated in a simulated overtaking.