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

Shahin Atakishiyev, Mohammad Salameh, Randy Goebel

arXiv:2403.12176v5cs.ROcs.AI

TL;DR

The paper examines how explanations can contribute to safety analysis of end-to-end driving, including accident understanding and driver interaction. Across analytical case studies and empirical evidence, it identifies potential safety value alongside limitations and challenges.

  • Problem

    The paper addresses safety implications of explanations in end-to-end driving, including how to understand accidents involving an unoccupied AV when no eyewitness is present.

    Source: C. Solving the ”Molly Problem” with… · p. 9

  • Method

    The paper investigates explanations through analytical case studies and empirical analyses, including spatiotemporal accident analysis, video question answering, and feature-importance analysis.

    Source: D. Spatiotemporal Object-Centric… · p. 10, A. Experiment 1: Qualitative Analysis… · p. 11, C. Discussion · p. 14

  • Results

    The paper concludes that explanations can reveal latent training-data patterns that led to end-to-end model failures and traffic accidents.

    Source: §VIII · p. 16

  • Takeaways & Limitations

    Live explanations may help people aboard an AV intervene in critical scenarios, while concise explanations can reduce backup drivers’ cognitive load and support quicker responses.

    Source: C. Discussion · p. 14

    The study uses scenario-based evaluations within a specific scope and does not study explanation anomalies, robustness and time constraints, uncertainty-related explanations, or their safety guarantees.

    Source: §VII · pp. 14–15

Abstract

from arXiv · show

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.

I. INTRODUCTION

End-to-end driving maps sensor inputs directly to control commands, trading the modular pipeline’s traceability for reduced error propagation and computational efficiency promises. The paper examines safety implications of explanations through case studies, empirical insights, and comparison with modular driving.

I. INTRODUCTION

  • SAE’s autonomy scale ranges from Level 0, with no driving automation, to Level 5, with no human intervention in any traffic situation.Starting at Level 3, most human-controlled functions are replaced with autonomous control methods.Source: §I · p. 1
  • Modular systems use interlinked perception, localization, planning, and control components, making errors traceable but allowing them to propagate downstream,,,.End-to-end learning maps sensory information directly to control commands as one machine-learning task,; its lack of explicit components hinders explainability.Source: §I · pp. 1–2, Figure 1 · p. 1
  • End-to-end driving is not universally safer, though its proponents cite reduced error propagation, uncertainty handling, and processing latency as potential real-world safety benefits,.Eliminating intermediate representations and using shared backbones also promise computational efficiency.Source: §I · p. 2
  • Automotive researchers have scarcely explored the safety benefits and consequences of explanations in end-to-end driving, motivating the paper’s research question.Prior work explored explanations mainly for trust, transparency, and situational awareness,,,,,.Source: §I · p. 2

A. Main Contributions

  • The paper presents case studies on how real-time and retrospective explanations of temporal decisions can enhance end-to-end driving safety.These are among the paper’s three stated contributions.Source: A. Main Contributions · p. 2
  • The paper draws on empirical studies to describe XAI opportunities, challenges, and limitations for end-to-end driving safety.It also compares explanation benefits in end-to-end and modular driving using analytical and empirical studies.Source: A. Main Contributions · p. 2

B. The Structure of the Paper

The paper outlines its progression from safety concepts and end-to-end learning paradigms to analytical and empirical studies of explanation-informed safety. It also describes takeover and fail-safe concepts and introduces reinforcement learning as sequential decision-making.

B. The Structure of the Paper

  • The paper’s structure moves from safety, learning paradigms, and XAI background to analytical studies, empirical safety assessment, future prospects, and conclusions.The analytical studies are in Section V, empirical studies in Section VI, and future prospects in Section VII.Source: B. The Structure of the Paper · p. 2

II. SAFETY OF AUTONOMOUS DRIVING

  • ISO 21448 addresses safety of intended functionality, ISO 26262 covers electrical and electronic system safety, and ISO/PAS 8800 is being developed for AI safety in AVs [25],,.Takeovers and fail-safe transitions to a safe state are also discussed, including slowing, pulling over, or stopping,,,.Source: §II · pp. 2–3

III. END-TO-END LEARNING PARADIGMS

  • The paper introduces reinforcement, imitation, and differentiable learning as three approaches behind end-to-end autonomous driving.The supplied section describes reinforcement learning in more depth.Source: §III · p. 3

A. Reinforcement Learning

  • In reinforcement learning, an agent’s policy maps states to action distributions, and the goal is to maximize return from the starting distribution.For autonomous driving, the AV is the agent, its position is the state, the driving scenario is the environment, and a scalar reward approximates decision correctness.Source: A. Reinforcement Learning · p. 3

B. Imitation Learning

Imitation learning trains an agent to match an expert driver’s demonstrated policy. Behavior cloning minimizes planning loss on expert data and is efficient for simple driving tasks, but complex situations challenge it.

B. Imitation Learning

  • Imitation learning trains an agent to match an expert driver’s policy by learning from the driver’s demonstrated actions,.Its goal is to match the policy demonstrated by an expert.Source: B. Imitation Learning · p. 3

1) Behavior cloning (BC):

  • Behavior cloning minimizes planning loss on expert behavior data and is computationally efficient, but complex driving situations challenge it,,.It works well for simple driving tasks and relies on large amounts of data from a human expert.Source: 1) Behavior cloning (BC) · p. 3
  • In the illustrated scenario, the ego vehicle stops when another vehicle acts unexpectedly and it cannot drive safely at that time step.The diagram also depicts the ego vehicle keeping distance from vehicles, pedestrians, and cyclists.Source: Figure 4 · p. 4

2) Inverse Reinforcement Learning (IRL):

The section describes inverse reinforcement learning and surveys visual explanations for end-to-end driving, from local post-hoc methods to scarce global explanations.

2) Inverse Reinforcement Learning (IRL):

  • IRL, also known as Inverse Optimal Control, learns an expert’s reward function, then improves the policy with reinforcement learning.It is more computationally complex than behavioral cloning, but has been effective at generalizing to rare and unseen states.Source: 2) Inverse Reinforcement Learning (IRL) · p. 4

C. End-to-End Differentiable Learning

  • Differentiable end-to-end learning jointly optimizes prediction and control while allowing gradients to propagate through the system’s components.This planning-oriented hybrid prioritizes planning and supports backpropagation across its modules.Source: C. End-to-End Differentiable Learning · p. 4

IV. XAI IN END-TO-END AUTONOMOUS DRIVING

  • The XAI section frames its approaches around explanation content and form, timing, and faithfulness as factors relevant to end-to-end driving safety.The section discusses algorithmic approaches alongside the timing and credibility of explanations.Source: §IV · p. 4

1) Post-hoc Explanations:

  • Post-hoc visual explanations can be local, justifying a particular task’s output, or global, summarizing the whole model’s decision process.Global explanations for end-to-end driving remain scarce; the passage identifies DeepTest as the sole example known to the authors.Source: 1) Post-hoc Explanations · pp. 4–5
  • Local visual explanations include saliency methods, which highlight influential image components, and counterfactuals, which examine how modified inputs change model outcomes.Counterfactuals can assess potential consequences of different AV decisions; examples include ChauffeurNet, STEEX, and OCTET.Source: 1) Post-hoc Explanations · p. 4

2) Intrinsic Explanations:

The section distinguishes explanations built into a model from reinforcement-learning explanations intended mainly to help researchers and developers inspect model behavior.

2) Intrinsic Explanations:

  • Intrinsic explanation-by-design approaches include attention models, semantic inputs, auxiliary information, representation visualization, and live natural-language explanations.These approaches aim to make models interpretable by design rather than explain predictions only after the fact.Source: 2) Intrinsic Explanations · p. 5

B. Reinforcement learning-based Explanations

  • Removing a pedestrian from a scene changes the illustrated driving behavior from “Stop” to “Go,” identifying the pedestrian as a risk object for stopping.The figure is credited to [56].Source: Figure 5 · p. 5
  • End-to-end reinforcement-learning explanations include feature importance, policy-level, reward-decomposition, logic-based, and saliency-map approaches.These explanations are primarily intended for researchers and developers, who can use their logs to investigate actions, identify potential model issues, and refine performance.Source: B. Reinforcement learning-based… · p. 5

C. Imitation learning-based Explanations

The section surveys imitation-learning, feature-importance, and language-model explanations, then considers explanation timing and passenger responses to risk-adaptive information.

C. Imitation learning-based Explanations

  • Imitation-learning explanations use image attention or other approaches to show how an agent makes decisions, including NEAT, MILE, HIIL, PlanT, and a GAN-based approach combined with signal temporal logic.A preliminary CARLA study used a conditional imitation-learning agent with attention to explain the influence of image regions on predictions.Source: C. Imitation learning-based Explanations · p. 6

D. Feature Importance-based Explanations

  • Feature-importance explanations use decision-tree metrics and SHAP values to quantify how inputs contribute to driving-model predictions.Reported application areas include vehicle positioning, risk assessment, takeover time, anomaly detection, and trust management.Source: D. Feature Importance-based Explanations · p. 6

E. Pretrained Large Language Models and Vision-Language Models-based Explanations

  • LLMs and VLMs have been explored for unsafe-scenario prediction, textual justifications, and question-answering, but their authoritative yet incorrect explanations can threaten safety understanding in real driving environments.Examples include Talk2BEV, DriveGPT4, LingoQA, and VLAAD.Source: E. Pretrained Large Language Models… · p. 6

F. Time Granularity and Faithfulness of Explanations

  • Explanation timing ranges from immediate feedback in milliseconds to perceptual explanations in seconds, behavioral explanations over seconds to minutes, and post-trip analysis.Timing should reflect urgency, relevance, and potential impact; takeover timing also involves a scenario-dependent trade-off between effectiveness and stress.Source: F. Time Granularity and Faithfulness… · pp. 6–7
  • Risk-adaptive explanations were more effective when driving-related information overwhelmed passengers and enhanced overall trust and perceived safety in an AV.The study found that the “what” and “why” facets provided more value to passengers in critical scenarios.Source: F. Time Granularity and Faithfulness… · p. 7

V. INSIGHTS FROM ANALYTICAL CASE STUDIES

The case studies consider how explanations can support safety monitoring and failure analysis in end-to-end driving, both in real time and retrospectively.

A. Real-time Explanations for Safety Monitoring

  • Real-time explanations can give passengers scenario-based information about vehicle behavior, limitations, and issues, supporting situation awareness, trust, and system debugging.The case study distinguishes proactive and reactive scenarios, each of which may be critical or non-critical; Figure 7 illustrates three categories.Source: A. Real-time Explanations for Safety… · p. 8, Figure 7 · p. 8

B. Failure Detection with Explanations

  • In a model-hacking test, Tesla’s vision system read an altered 35-mph sign as 85 mph and accelerated, illustrating a potential perception failure.The example used a black sticker to alter the sign; the authors note that such a misdetection could have dire consequences in real driving.Source: B. Failure Detection with Explanations · p. 8, Figure 8 · p. 9
  • A real-time rationale for acceleration could help a passenger or backup driver recognize the sign misreading and possibly intervene, while post-hoc action-explanation histories could support debugging.The proposed real-time example is a dashboard message that the speed limit is 85 mph and the vehicle is accelerating.Source: B. Failure Detection with Explanations · p. 9

C. Solving the ”Molly Problem” with Explanations

The Molly problem asks how an unwitnessed AV-pedestrian collision can be investigated, while spatiotemporal explanations can trace accident evolution and inform prevention. For end-to-end driving, those explanations may also expose data patterns behind failures.

C. Solving the ”Molly Problem” with Explanations

  • The Molly problem concerns assigning responsibility after an unoccupied AV hits a pedestrian without witnesses, making the vehicle’s action history important for post-accident analysis.The proposed review examines the AV’s behavior before, at, and after impact.Source: C. Solving the ”Molly Problem” with… · p. 9
  • Survey respondents expected the AV software to be aware of the collision (97%), stop at the scene (94%), and signal a hazard to bystanders (94%).The survey included 296 respondents; the authors propose analyzing explanations across three phases: before, at, and after impact.Source: C. Solving the ”Molly Problem” with… · p. 9
  • Whether the AV stopped after impact could affect legal implications and help address responsibility, liability, and accountability.Accurate action-explanation logs could support forensic analysis when no one witnessed the collision.Source: C. Solving the ”Molly Problem” with… · pp. 9–10

D. Spatiotemporal Object-Centric Accident Analysis with Explanations

  • Object-centric accident explanations describe involved actors, temporal evolution, root causes, and prevention; in end-to-end driving, they can reveal failure-linked training-data patterns and dataset gaps.The cited MM-AU example uses textual explanations to describe accident evolution and prevention advice.Source: D. Spatiotemporal Object-Centric… · pp. 10–11, Figure 9 · p. 10

E. Overall Analysis of the Case Studies Above from the Driving Paradigm Perspective: End-to-end vs. Modular Pipeline

The paper contrasts explanations’ data-focused safety implications in end-to-end driving with module-level diagnosis in modular systems, then examines video question answering as an action explainer. The experiments show that explanations can be correct or misleading, including under deliberate questioning.

E. Overall Analysis of the Case Studies Above from the Driving Paradigm Perspective: End-to-end vs. Modular Pipeline

  • In modular driving, explanations can attribute faults to specific modules; in end-to-end driving, they can expose training-data patterns and motivate targeted data augmentation.The authors note that end-to-end systems are more data-hungry and may benefit from relevant, less-seen data.Source: E. Overall Analysis of the Case… · p. 11

VI. INSIGHTS FROM EXPERIMENTAL ANALYSES

  • The empirical analyses examine qualitative and quantitative implications of explanation faithfulness for end-to-end driving safety.The paper describes the experiments as a follow-up to analytical safety analyses.Source: §VI · p. 11

A. Experiment 1: Qualitative Analysis on Video Question Answering as Action Explainer

  • VideoQA uses driving videos and questions as input and produces textual responses; this experiment samples six 4–12-second scenes from SHIFT and BDD-A using Video-LLaVA.The study treats Video-LLaVA as a plug-and-play video-reasoning model, regardless of whether it was pretrained on driving data.Source: A. Experiment 1: Qualitative Analysis… · p. 11
  • The study distinguishes correct actions with correct explanations from correct actions with incorrect explanations, including a false explanation to an adversarial question about a turn.The authors argue that an explanation interface should detect incorrect questions and respond accurately.Source: A. Experiment 1: Qualitative Analysis… · p. 11
  • A model may reason beyond the video, such as predicting a red traffic light that is not present; the authors recommend describing the scene as shown or failing safely.The passage identifies this as a potential limitation of explanation models.Source: A. Experiment 1: Qualitative Analysis… · p. 11
  • A correct explanation of an incorrect AV action can help engineers detect system errors, but an unsafe action paired with an incorrect explanation is the least desirable case.The example concerns continuing through a red light and an explanation that incorrectly says it turned green.Source: A. Experiment 1: Qualitative Analysis… · pp. 11, 13

B. Experiment 2: Quantitative Analysis on Feature Importance Scores for Understanding Influential Driving Features

The experiment applies SHAP to deep reinforcement-learning decisions in a vehicle-following task, using a defined state space, three discrete actions, and rewards for speed, distance, and collision avoidance.

B. Experiment 2: Quantitative Analysis on Feature Importance Scores for Understanding Influential Driving Features

  • The vehicle-following state represents relative distance and the longitudinal and lateral velocities of the AV and the vehicle ahead.The variables are d, egovx, egovy, frontvx, and frontvy.Source: B. Experiment 2: Quantitative Analysis… · p. 13
  • The action space contains three discrete choices: deceleration, maintaining current speed, and acceleration.The experiment defines rewards for driving fast, keeping a safe following distance, and avoiding collision.Source: B. Experiment 2: Quantitative Analysis… · p. 13
  • SHAP is used to simplify the feature space and describe feature dependencies and their influence on AV action decisions.The experiment interprets deep RL decisions in a vehicle-following task.Source: B. Experiment 2: Quantitative Analysis… · p. 13

C. Discussion

The discussion describes how explanations may serve occupants, developers, security work, adaptation, and regulatory review. It also bounds the study: several explanation-safety properties remain unexamined.

C. Discussion

  • Correct action explanations may support occupant trust, while inaccurate ones can alert passengers to a malfunction and help them avoid foreseeable danger.The authors also say human-interpretable action logs could help auditors and accident inspectors assess accountability after crashes.Source: C. Discussion · p. 14
  • Feature-importance analysis identifies egovx, d, and frontvx as most influential for each action, suggesting egovy and frontvy could be removed from the state representation.The authors describe possible benefits including reward-function adjustment, reduced model complexity, and support for scalable evaluation, uncertainty quantification, and runtime monitoring.Source: C. Discussion · p. 14
  • The paper groups explanation value into control, enhancement, defense, adaptation, and compliance, including takeover support, system improvement, vulnerability analysis, and audits.Post-hoc explanation patterns may guide software updates, while explanations of rare or unseen situations may support adaptive learning.Source: C. Discussion · p. 14
  • The paper contrasts monolithic end-to-end systems mapping sensor inputs to control commands with modular systems that require explanation within and across components.The authors note that the described safety benefits also apply to modular and other autonomous-driving architectures.Source: C. Discussion · p. 14

VII. LIMITATIONS AND FUTURE DIRECTIONS

  • The study’s safety assessment is limited to scenario-based evaluations and does not examine explanation-anomaly detection, robustness, time constraints, uncertainty-related trigger rates, or safety guarantees.The authors leave these topics as directions for future work.Source: §VII · pp. 14–15

A. Building Robust Interactive Explanations

The section identifies challenges in making interactive explanations robust, timely, and calibrated to uncertainty. It concludes that explanations can expose training-data patterns behind failures and support iterative improvements to end-to-end driving.

A. Building Robust Interactive Explanations

  • Interactive explanation models, including transformer-based QA, can produce falsified responses to adversarial questions, risking users’ perceived safety and regulatory approval.The models should understand the joint semantics of the question and scene and provide context-aware responses.Source: A. Building Robust Interactive… · p. 15

B. Reaching a Consensus on Time Constraint of Explanation Conveyance

  • Explanation timing remains underexplored; timely, adequately formatted messages must account for takeover lead time and users’ diverse cognitive and functional abilities.The paper points to scenario-based evaluations with human actors as one way to assess time needed for takeover situations.Source: B. Reaching a Consensus on Time… · p. 15

C. Safety Analysis with Explanations in Uncertainty

  • Under adverse weather or reduced visibility, confidence-aware explanations remain relatively unexplored, although granular explanations could communicate residual risks across hazard levels.The paper notes that only a few studies have explored explainability under uncertainty.Source: C. Safety Analysis with Explanations… · pp. 15–16

VIII. CONCLUSION

  • Explanations can expose latent training-data patterns behind failures and accidents, while situation-awareness explanations could guide data augmentation through iterative learning.The paper presents these as potential routes to enhancing driving safety.Source: §VIII · p. 16
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