# Towards Safe, Explainable, and Regulated Autonomous Driving

arXiv: https://arxiv.org/abs/2111.10518 · HTML: https://mlsanity.com/paper/2111.10518/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 25 of its 59 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 25 of 25)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances](https://mlsanity.com/paper/1911.10868/) (2019)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Unsupervised part representation by Flow Capsules](https://mlsanity.com/paper/2011.13920/) (2020)
- [Temporal-Difference Networks](https://mlsanity.com/paper/1504.05539/) (2015)
- [QUOTA: The Quantile Option Architecture for Reinforcement Learning](https://mlsanity.com/paper/1811.02073/) (2018)
- [VisualBackProp: efficient visualization of CNNs](https://mlsanity.com/paper/1611.05418/) (2016)
- [Improved Explainability of Capsule Networks: Relevance Path by Agreement](https://mlsanity.com/paper/1802.10204/) (2018)
- [Learning predictive representations in autonomous driving to improve deep reinforcement learning](https://mlsanity.com/paper/2006.15110/) (2020)
- [An Introduction to Deep Visual Explanation](https://mlsanity.com/paper/1711.09482/) (2017)
- [Using KL-divergence to focus Deep Visual Explanation](https://mlsanity.com/paper/1711.06431/) (2017)
- [Perception as prediction using general value functions in autonomous driving applications](https://mlsanity.com/paper/2001.09113/) (2020)
- [When is a Prediction Knowledge?](https://mlsanity.com/paper/1904.09024/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors](https://mlsanity.com/paper/2302.13137/) (2023)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios](https://mlsanity.com/paper/2408.04482/) (2024)
- [On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System](https://mlsanity.com/paper/2404.17350/) (2024)

## Abstract (arXiv)

There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning. However, as demonstrated by recent traffic accidents, autonomous driving technology is not fully reliable for safe deployment. As AI is the main technology behind the intelligent navigation systems of self-driving vehicles, both the stakeholders and transportation regulators require their AI-driven software architecture to be safe, explainable, and regulatory compliant. In this paper, we propose a design framework that integrates autonomous control, explainable AI (XAI), and regulatory compliance to address this issue, and then provide an initial validation of the framework with a critical analysis in a case study. Moreover, we describe relevant XAI approaches that can help achieve the goals of the framework.