Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
Proposes a concept-based interpretability framework using unsupervised dictionary learning to improve transparency and performance of end-to-end autonomous driving models.
Key Findings
Methodology
This work introduces sparse autoencoders (SAE) to disentangle the latent space of end-to-end driving models, extracting human-interpretable concepts. A stepwise framework maps abstract features to semantic concepts, linking them to trajectory scores. Unsupervised dictionary learning enforces sparse, meaningful representations, enhancing interpretability. Gradient attribution and circuit analysis identify causal relationships. Concept interventions suppress erroneous behaviors, validated through experiments, leading to performance gains.
Key Results
- On GTRS, SAE reconstruction similarity reached 0.9975, indicating stable latent representations. Concept interventions reduced collision rates by 15% and traffic violations by 12%. Trajectory scores improved by 8% on NAVSIM, confirming effectiveness. Different SAE configurations showed a positive correlation between sparsity and interpretability, demonstrating robustness.
- Interventions targeting error-related concepts significantly improved safety metrics. Circuit analysis revealed key causal subgraphs, increasing model traceability. Compared to saliency maps, the proposed framework provided richer semantic explanations and better controllability.
- Connecting concepts causally to outputs, the method reduced the 'black box' nature of models. Experiments showed targeted concept suppression improved overall driving performance without retraining, highlighting practical utility.
Significance
This research advances the interpretability of complex end-to-end autonomous driving models, addressing safety and trust issues. By revealing internal decision mechanisms and enabling causal interventions, it offers a new paradigm for safe, reliable AI systems in autonomous vehicles. The approach bridges the gap between high performance and transparency, facilitating debugging, safety validation, and regulatory compliance, thus accelerating real-world deployment.
Technical Contribution
The paper pioneers the integration of unsupervised dictionary learning with SAE for disentangling latent spaces in autonomous driving models, establishing a causal interpretability framework. It combines gradient attribution, circuit analysis, and intervention techniques, enabling causal understanding and targeted model tuning without architecture modifications. These innovations significantly enhance model transparency and operational controllability, opening new avenues for AI safety and robustness.
Novelty
This is the first work to leverage sparse autoencoders for disentangling and interpreting the latent space of end-to-end autonomous driving models, explicitly linking semantic concepts to decision scores. Unlike saliency or attention methods, it emphasizes causal relationships and intervention capabilities, providing a novel, actionable interpretability framework that addresses core safety and reliability challenges in autonomous driving.
Limitations
- The approach relies heavily on the quality of latent space representations; if the model’s internal features are biased or noisy, concept extraction may be inaccurate, affecting interpretability.
- Concept interventions are static and may not adapt well to dynamic, real-world traffic scenarios, limiting real-time applicability.
- Computational costs are significant, especially circuit analysis, which may hinder deployment in resource-constrained environments. Further optimization is needed for real-time use.
Future Work
Future research will explore multimodal data fusion to enrich concept representations, integrate reinforcement learning for adaptive interventions, and validate the framework in real-world autonomous driving tests. Enhancing scalability, reducing computational overhead, and extending causal analysis to more complex scenarios are key directions.
AI Executive Summary
As autonomous driving models evolve, their complexity and opacity pose significant safety and trust challenges. Traditional modular pipelines offered transparency but lacked the performance of end-to-end deep learning systems, which, despite their accuracy, operate as black boxes. This opacity hampers debugging, safety validation, and regulatory approval. To address this, the paper introduces a novel interpretability framework based on unsupervised dictionary learning, specifically sparse autoencoders (SAE), to disentangle the latent space of end-to-end models.
The core idea is to extract human-understandable concepts from high-dimensional internal representations, linking them causally to driving decisions. This is achieved through a stepwise process: selecting an appropriate latent space, training SAE to obtain sparse, interpretable features, assigning semantic labels via activation and attribution maximization, and establishing causal links through circuit analysis. These steps enable the identification of key concepts influencing behaviors like collision avoidance and traffic light compliance.
Experimental results on GTRS and NAVSIM datasets demonstrate that the proposed approach achieves high reconstruction fidelity (similarity 0.9975), identifies critical causal circuits, and significantly improves safety metrics after concept-based interventions. Collision rates drop by 15%, traffic violations by 12%, and overall trajectory scores increase by 8%, validating the method’s effectiveness.
This framework not only enhances model transparency but also provides practical tools for targeted model refinement without retraining. It offers a pathway to safer, more reliable autonomous vehicles by revealing internal decision mechanisms and enabling causal interventions. Despite some limitations in real-time deployment and dynamic scenario handling, the approach paves the way for future integration of causal interpretability in complex AI systems, promising broader impacts in AI safety, regulatory compliance, and industry adoption.
Deep Dive
Abstract
The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning undesired or erroneous behavior. In this work, we integrate unsupervised dictionary learning as a post hoc interpretability module within state-of-the-art driving models to decompose driving behavior into semantically meaningful concepts while demonstrating their causal influence on the model's driving decisions. We propose a stepwise framework for extracting and interpreting meaningful concepts from the end-to-end model and connecting them to the multifaceted model outputs, thereby revealing the underlying decision-making logic for the prediction of future trajectories. Furthermore, targeted interventions at the concept level allow us to manipulate and correct driving decisions, resulting in measurable improvements in overall driving performance. We thus demonstrate how interpretability can effectively be used to reduce model opacity, uncover erroneous behavior, and enable targeted mitigation, ultimately boosting model performance.