# Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios

arXiv: https://arxiv.org/abs/2005.02031 · HTML: https://mlsanity.com/paper/2005.02031/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 9 of 9)

- [RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model](https://mlsanity.com/paper/2402.10828/) (2024)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning](https://mlsanity.com/paper/2103.00869/) (2021)
- [Textual Explanations for Automated Commentary Driving](https://mlsanity.com/paper/2304.08178/) (2023)
- [Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques](https://mlsanity.com/paper/2306.09462/) (2023)
- [Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data](https://mlsanity.com/paper/2402.17653/) (2024)
- [Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles](https://mlsanity.com/paper/2503.14557/) (2025)
- [Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles](https://mlsanity.com/paper/2605.04299/) (2026)

## Abstract (arXiv)

This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.