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Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

Shahin Atakishiyev, Mohammad Salameh, Randy Goebel

TL;DR

This work investigates how explainable AI (XAI) can affect safety in end-to-end autonomous driving, highlighting that explanations can reveal latent training-data patterns contributing to failures and guide targeted data augmentation. It analyzes safety concepts, XAI taxonomies (visual, RL/IL–based, and LLM/VLM–based explanations), and time granularity, then validates with analytical case studies and two experiments (VideoQA-based action explanations and SHAP-based feature attribution). The findings show safety benefits in real-time monitoring, failure detection, and regulatory compliance, while also noting limitations such as adversarial explanations and uncertainty handling. Overall, the paper provides a framework for integrating explanations to improve safety, trust, and regulatory acceptability for end-to-end AVs, differentiating the end-to-end approach from modular pipelines by emphasizing data-driven safety improvements via explanations.

Abstract

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices. However, a lack of explainability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles. Moreover, the issue is exacerbated when these vehicles are involved in or cause traffic accidents. Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation. With that said, automotive researchers have not yet rigorously explored safety benefits and consequences of explanations in end-to-end autonomous driving. This paper aims to bridge the gaps between these topics and seeks to answer the following research question: What are safety implications of explanations in end-to-end autonomous driving? In this regard, we first revisit established safety and explainability concepts in end-to-end driving. Furthermore, we present critical case studies and show the pivotal role of explanations in enhancing driving safety. Finally, we describe insights from empirical studies and reveal potential value, limitations, and caveats of practical explainable AI methods with respect to their potential impacts on safety of end-to-end driving.

Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

TL;DR

This work investigates how explainable AI (XAI) can affect safety in end-to-end autonomous driving, highlighting that explanations can reveal latent training-data patterns contributing to failures and guide targeted data augmentation. It analyzes safety concepts, XAI taxonomies (visual, RL/IL–based, and LLM/VLM–based explanations), and time granularity, then validates with analytical case studies and two experiments (VideoQA-based action explanations and SHAP-based feature attribution). The findings show safety benefits in real-time monitoring, failure detection, and regulatory compliance, while also noting limitations such as adversarial explanations and uncertainty handling. Overall, the paper provides a framework for integrating explanations to improve safety, trust, and regulatory acceptability for end-to-end AVs, differentiating the end-to-end approach from modular pipelines by emphasizing data-driven safety improvements via explanations.

Abstract

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices. However, a lack of explainability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles. Moreover, the issue is exacerbated when these vehicles are involved in or cause traffic accidents. Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation. With that said, automotive researchers have not yet rigorously explored safety benefits and consequences of explanations in end-to-end autonomous driving. This paper aims to bridge the gaps between these topics and seeks to answer the following research question: What are safety implications of explanations in end-to-end autonomous driving? In this regard, we first revisit established safety and explainability concepts in end-to-end driving. Furthermore, we present critical case studies and show the pivotal role of explanations in enhancing driving safety. Finally, we describe insights from empirical studies and reveal potential value, limitations, and caveats of practical explainable AI methods with respect to their potential impacts on safety of end-to-end driving.
Paper Structure (34 sections, 5 equations, 14 figures, 4 tables)

This paper contains 34 sections, 5 equations, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Main Contributions
  3. The Structure of the Paper
  4. Safety of autonomous driving
  5. End-to-End Learning Paradigms
  6. Reinforcement Learning
  7. Imitation Learning
  8. Behavior cloning (BC)
  9. Inverse Reinforcement Learning (IRL)
  10. End-to-End Differentiable Learning
  11. XAI in end-to-end autonomous driving
  12. Visual Explanations for End-to-End Autonomous Driving
  13. Post-hoc Explanations
  14. Intrinsic Explanations
  15. Reinforcement learning-based Explanations
  16. ...and 19 more sections

Figures (14)

  • Figure 1: The modular and end-to-end pipeline comparison in autonomous driving. The modular pipeline is more explainable by design as it contains several interlinked components, but an error in one module (e.g., misdetection in perception) cascades to the downstream module (planning/control). On the other hand, the end-to-end driving pipeline, while lacking explainability, directly maps sensor input to control commands, optimizing system performance. Continuous improvement through data-driven optimization makes end-to-end learning more scalable. The source of the image of the vehicle: Waymo.
  • Figure 2: V-model in ISO 26262. The figure drawn based on the content in V_model.
  • Figure 3: Potential takeover situations: (a) The blind corner ahead reduces the AV's perception ability and urges for a takeover of the human driver, and (b) Autopilot perceives edge-markings of an exit lane as the continuation of the present lane (upper left, while still on the main road), steers right and the vehicle exits the road incorrectly (upper right, exit). Edge markings disappear when the autopilot takes a wrong exit. Images adapted from brown2017trouble.
  • Figure 4: A diagram of safe autonomous driving. In (a), an AV (i.e., ego vehicle) interacts with the dynamic and stationary objects in the environment (i.e., vehicles ahead, pedestrians, and cyclists at the roadside identified with green, blue, and red bounding boxes, respectively) safely and keeps a distance from them. In (b), the ego vehicle faces the unexpected action of the other vehicle, understands its limited motion ability at that moment, and comes to a standstill as it can not drive safely at that time step.
  • Figure 5: A causal effect of removing a pedestrian from a scene: The driving behavior changes from "Stop" to "Go" by showing that the eliminated object is a risk object for the "Stop" command. Graphics credit: li2020make
  • ...and 9 more figures