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Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
TL;DR
Autonomous vehicles increasingly make real-time decisions through powerful AI, but those decisions are often difficult for humans to understand, limiting social acceptance and regulatory compliance. This paper reviews XAI for autonomous driving, proposes a conceptual explainable end-to-end framework, and identifies future directions; it concludes that real-time regulatory-compliant safety and explainability remain central requirements.
Problem
Black-box autonomous-vehicle decisions are difficult for humans to understand, creating concerns about social acceptance and regulatory compliance.
Method
The paper systematically reviews state-of-the-art and emerging XAI approaches, develops a conceptual explainable end-to-end framework, and proposes future research directions.
Results
The paper presents a comprehensive overview, identifies existing gaps, and integrates missing elements into a conceptual framework for explainable autonomous driving.
Takeaways & Limitations
Explainable autonomous driving should combine regulatory-compliant operational safety with explanations of real-time decisions as vehicle automation increases.
Takeaways & Limitations
Accurate and timely explanations remain challenging across all trip phases; as of September 2023, LINGO-1 achieved roughly 60% of human-level linguistic and VQA-based explanation performance.
Abstract
from arXiv · showhide
Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly transportation systems. With the rapid progress in computationally powerful artificial intelligence (AI) techniques, AVs can sense their environment with high precision, make safe real-time decisions, and operate reliably without human intervention. However, intelligent decision-making in such vehicles is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, AVs must also explain their AI-guided decision-making process in order to be regulatory compliant across many jurisdictions. Our study sheds comprehensive light on the development of explainable artificial intelligence (XAI) approaches for AVs. In particular, we make the following contributions. First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving. We then propose a conceptual framework that considers the essential elements for explainable end-to-end autonomous driving. Finally, we present XAI-based prospective directions and emerging paradigms for future directions that hold promise for enhancing transparency, trustworthiness, and societal acceptance of AVs.
1 INTRODUCTION
Autonomous vehicles offer substantial safety and transportation benefits, but black-box decisions create societal concern and motivate explainability research. This paper reviews XAI for autonomous driving, proposes an explainable end-to-end framework, and maps future research directions.
- Nearly 94% of road accidents are attributed to human errors, motivating efforts to improve transportation safety and automation.
- Black-box decision-making in autonomous vehicles has contributed to public concern following traffic accidents involving such cars.
- The paper provides a structured review of state-of-the-art and emerging XAI approaches for autonomous driving.
- The paper proposes future XAI directions aimed at trustworthy, responsible, regulatory-compliant, and publicly acceptable autonomous vehicles.
- It presents a conceptual design framework for explainable end-to-end autonomous driving.
2 BACKGROUND
Autonomous vehicles combine sensing and learning technologies to navigate without human intervention, using either modular or end-to-end architectures. Their progress has improved core driving capabilities while raising explainability, ethical, liability, and regulatory concerns.
- 2.1 Autonomous driving at a glance: Autonomous vehicles use sensors, cameras, RADAR, LIDAR, GPS, and learning algorithms to navigate and operate without human intervention.
- 2.1 Autonomous driving at a glance: Modular architectures are more explainable and debuggable because they use standalone components, but errors can propagate between components.
- 2.2 Fundamental issues: Deep learning has improved perception, object detection, and planning in autonomous driving systems.
- 2.2 Fundamental issues: Autonomous driving raises unresolved questions about ethical crashing, responsibility, compensation, and the liability gap.
- 2.1 Autonomous driving at a glance: Modular driving pipelines separate perception, localization, planning, and control, whereas end-to-end systems map raw sensor data directly to control commands.
- 2.2 Fundamental issues: Regulatory frameworks seek to protect stakeholder rights and ensure control over data, including the EU GDPR’s right-to-explanation principle.
3 EXPLANATIONS IN AUTONOMOUS DRIVING
Explainability in autonomous driving is presented as a multidisciplinary expectation and requirement, shaped by safety, human-centered, philosophical, and legal perspectives. Its benefits include acceptance, trust, traceability, and accountability, while explanation design must reflect recipients’ knowledge and abilities.
- Multidisciplinary perspectives: The need for AV explanations is framed through psychological, sociotechnical, philosophical, and legal perspectives.These perspectives address safety concerns, human-centered deployment, causal information about critical actions, and regulatory compliance.
- Multidisciplinary perspectives: Explainability is described as both an expectation and a requirement for autonomous driving systems.
- Benefits for stakeholders: Explanations can support human-centered design, trustworthiness, traceability, and transparency or accountability for AV stakeholders.They can incorporate user input, build trust, support post-trip analysis, and help address liability and responsibility gaps.
- Explanation recipients: Explanation details, types, and delivery should vary with users’ identities, technical knowledge, and functional or cognitive abilities.Less technical users may need simple decision explanations, whereas engineers may require information useful for debugging.
- Explanation recipients: XAI research considers who needs explanations, why they need them, what can be generated, and when explanations should be delivered.
4 XAI FOR AUTONOMOUS DRIVING: A SURVEY
The paper positions its survey alongside earlier reviews of explainable AI for autonomous vehicles. It extends that literature by addressing timing, newer AI paradigms, and algorithmic or methodological classification.
- Previous surveys: Earlier XAI-for-AV reviews examined explanation needs, legal requirements, standards, consumer expectations, and explainability hurdles.Omeiza et al. also used these considerations to present a conceptual framework for modular autonomous driving.
- Survey extension: The paper identifies explanation timing as important because autonomous vehicles are real-time decision-making systems.
- Survey extension: It covers attention-based transformers, large language models, and vision-language models, which earlier surveys had not explored.
- Survey extension: The survey adds a classification of XAI approaches for AVs from an algorithmic and methodological perspective.
4.2 Structure of our survey
The survey organizes XAI for autonomous driving across multiple methodological families and emerging language-based paradigms. It also connects these approaches to a conceptual end-to-end framework and future directions, with visual methods illustrating how model evidence can explain vehicle behavior.
- Survey organization: The survey classifies AV XAI approaches across vision, reinforcement learning, imitation learning, decision trees, logic, user studies, and language-based paradigms.The emerging category includes large-language- and vision-language-based explanations.
- Conceptual framework: The paper presents a conceptual framework for explainable end-to-end autonomous driving and identifies essential elements for explanation design.
- Future directions: Future directions target missing pieces in the state of the art, with the stated goal of improving transparency and social acceptance in next-generation AVs.
- Visual explanations: Visual XAI methods expose image regions or internal CNN representations associated with predicted labels and vehicle behavior.Examples include deconvolution, CAM, Grad-CAM, guided backpropagation, layer-wise relevance propagation, and DeepLift.
- Visual explanations: Causal attention models identify input regions that affect steering control, while related work produces textual explanations of decisive vehicle actions.
- Interactive explanations: Human-advice approaches use sensed surroundings and human guidance to learn vehicle control.
4.4 Reinforcement learning and Imitation learning-based explanations
Reinforcement- and imitation-learning explanations connect environmental states to vehicle actions through interpretable policies, visual representations, logic, and external explanatory methods.
- Reinforcement learning: Explainable reinforcement learning justifies vehicle actions through intrinsically interpretable designs or post-hoc explanations.Semantic Predictive Control predicts future semantic segmentation while producing visual explanations of its learned policy.
- Overview: Table 2 summarizes studies using reinforcement-learning and imitation-learning-based explanations for autonomous vehicles.
- Imitation learning: A two-phase imitation-learning pipeline combines a pretrained bird-eye-view mask with a Pure-Pursuit steering angle for end-to-end driving.The CARLA experiment reports improved interpretability and robustness across various circumstances.
- Imitation learning: PlanT explains action decisions by identifying the most important object in a driving segment and outperforms state-of-the-art work on CARLA’s Longest6 Benchmark by 10 points.
- External explanatory methods: Combining Signal Temporal Logic with generative imitation learning provides an interpretable policy, while logic and vision can externally explain otherwise challenging RL or IL agents.
4.5 Decision tree-based explanations
Decision-tree-based explanations represent driving decisions semantically, making predictions and observation-to-action mappings easier to understand.
- Decision trees can describe the rationale semantically for each prediction made by a convolutional neural network.
- Scenario-based decision-tree explanations map observations to actions according to traffic rules.
4.6 Logic-based explanations
Logic-based methods address autonomous-driving safety by representing and identifying interpretable failure cases with formal temporal specifications.
- Logical reasoning has been used to verify the safety of self-driving vehicles alongside research on interpretable deployed control models.
- Signal Temporal Logic expressions describe failures in unprotected-left-turn and pedestrian-crossing scenarios.Genetic programming optimizes the expressions to find disturbance trajectories that cause decisive-action failures.
4.7 User study-based explanations
User studies examine how people understand and shape explanations for autonomous-driving systems, including explanation interfaces and human input to forecasting and control.
- User studies seek to improve the adequacy and quality of explanations by collecting people’s input in designated driving tasks.
- Wiegand et al. identify users’ mental model for implementing an effective practical explanation interface.
- A graphical interface lets human drivers provide scene forecasting through purposeful gaze and examines effects on intelligent-vehicle prediction and control.A simulator tests three driving situations.
4.8 Large Language Models and Vision-Language Models-based explanations
Recent explainable autonomous-driving research increasingly uses large language models and vision-language models to interpret vehicle decisions and describe traffic scenes. These approaches support natural-language explanations and user interaction during journeys.
- Emerging paradigms: LLMs and VLMs have emerged as a novel paradigm for interpreting AV decisions and describing traffic scenes.The reviewed models include both domain-agnostic and domain-specific language and vision-language systems.
- Live natural-language explanations: LINGO-1 generates concise explanations of driving actions and their causal factors while reflecting temporal changes in the environment.It was trained on diverse multimodal vision-and-language datasets and has also been demonstrated in simulation.
- Video Question Answering: Video Question Answering treats conversational interaction between passengers and AVs as a way to answer questions about traffic situations and vehicle actions.This supports users-in-the-loop during a journey.
- Explainability requirements: Explainable autonomous driving combines real-time safety decisions, timely action explanations, and compliance with traffic rules.
5 AN XAI FRAMEWORK: INTEGRATING END-TO-END CONTROL, SAFETY, AND EXPLANATIONS
The proposed XAI framework integrates end-to-end control, safety-regulatory compliance, and explanation components for regulatory-compliant autonomous driving. It combines environment-to-action mapping with safety verification and explanations delivered during and after trips.
- Framework overview: The framework combines XAI methods, end-to-end learning, and safety components to inform regulatory-principle processes.
- End-to-end control: The end-to-end control component maps perceived environments to corresponding autonomous-vehicle actions.The control system is represented as C : E 7→A and is intended to map every environment instance to a relevant action.
- End-to-end learning: Reinforcement learning maps sensor information to states and control signals, whereas imitation learning trains agents by copying expert behavior.Behavior cloning and inverse reinforcement learning are identified as applied imitation-learning forms.
- Safety-regulatory compliance: The safety-regulatory compliance component represents regulatory verification of the safety of an end-to-end controller together with autonomous-vehicle actions.It is expressed as srC = f(eeC, A).
- Compliance assurance: Compliance can involve simulation and verification, with ISO 26262 requiring evidence-based rationales for functional-safety requirements in context.The cited software guidance covers design, development, testing, and verification of automotive software systems.
- Explanation component: The explanation component justifies action choices and communicates information to relevant users during journeys and through post-trip analysis.Explanations may be visual, textual, feature-based, hybrid, or multimodal, and may use light, audio, or vibrotactile channels.
6 TOWARD AV 2.0: UNIFYING VISION, LANGUAGE, AND ACTION WITHIN EMBODIED AI FOR SAFE AND EXPLAINABLE END-TO-END AUTONOMOUS DRIVING
AV 2.0 research seeks to unify vision, language, and action within embodied AI while addressing safety and explainability challenges. The section highlights human oversight, fail-safe behavior, distribution shifts, reward hacking, and richer interaction with road users.
- AV 2.0 vision: Foundation-model advances in LLMs and VLMs motivate a transition toward next-generation autonomous-driving software systems.
- AV 2.0 design dimensions: AV 2.0 design comparisons emphasize differences in pipeline design, interpretability, scalability, human involvement, fail-safe ability, and improvement methodology.The listed comparison contrasts modular and end-to-end pipelines and rule-based and adaptive improvement.
- Human cooperation: Cooperative inverse reinforcement learning allows humans to cooperate with an autonomous system and shut it down when it exhibits undesirable behavior.Sympathetic Cooperative Driving extends this idea by promoting altruistic behavior toward human-controlled vehicles.
- Reward design: Reward formulation must account for long horizons so adaptive systems do not optimize short-term safe behavior while transitioning across environment dynamics.
- Scalable oversight: Scalable oversight asks whether humans can evaluate AV performance across driving situations when judging end-to-end behavior is difficult.Semi-supervised reinforcement learning is discussed as a potential response using rewards from only a subset of episodes or time steps for optimization.
- Safety and robustness: Safe exploration and distributional shift remain concerns because alternative routes may be hazardous and strong simulation performance may not transfer to real roads.
- Explainability challenges: Explainability still faces gaps in accuracy, timing, and resistance to hallucinations in large pre-trained models.The section reports roughly 60% human-level performance for LINGO-1 linguistic and VQA-based explanations as of September 2023.
7 CONCLUSION
The paper synthesizes XAI research for autonomous driving, identifies gaps, and proposes a conceptual framework plus future directions. Its stated aim is to support regulatory-compliant safety and real-time explainability as vehicle automation increases.
- Synthesis: The paper provides a systematic overview of state-of-the-art investigations, emerging paradigms, and future perspectives in XAI for autonomous driving.
- Framework contribution: Its proposed framework incorporates missing pieces to address regulatory-compliant operational safety and explainability in real-time decisions.
- Research direction: The paper presents its contribution as a cause-effect-solution perspective linking identified gaps and concerns with a framework and future XAI approaches.