[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84105-en":3,"doc-seo-84105-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84105,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Driving the Wrong Way Leveraging Interpretability in End-to-End Autonomous Driving Models","End-to-end learning for autonomous driving increases model complexity and reduces transparency, making erroneous or unsafe behaviors harder to detect and debug. The work adds unsupervised dictionary learning as a post-hoc interpretability module to decompose driving behavior into semantically meaningful concepts. A stepwise framework extracts and links concepts to trajectory-level decision scores, exposing causal influences on future-trajectory prediction. Concept-level interventions suppress specific latent components to correct decisions and improve safety and performance metrics.","Driving the Wrong Way: Leveraging Interpretability in End2End  \nAutonomous Driving Models  \nFranz Motzkus, Sebastian Bernhard  \narXiv :2607 .06328v 1 [ cs .AI ] 7 Jul 2026  \nAbstract—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 stateof-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.  \nIndex Terms—Artificial Intelligence, Autonomous Driving, End-to-End Learning, Interpretability.  \nI. INTRODUCTION  \nEnd-to-end learning has recently emerged as a prominent approach for autonomous driving systems. In contrast to conventional modular pipelines with explicitly separated perception, prediction, and planning components, modern approaches integrate multimodal inputs, including camera recordings and top-level commands, into unified transformer-based architectures [1] . These systems jointly learn perception, temporal reasoning, and decision-making, achieving state-of-the-art performance on open-loop benchmarks such as NAVSIM [2], [3] .  \nHowever, this architectural unification comes at the cost of reduced transparency, as the model complexity increases and clear interfaces that previously enabled module-level testing disappear. Consequently, internal decision-making becomes increasingly opaque, making the analysis of failure modes more difficult and resulting in expensive error debugging through repeated data curation and retraining cycles guided mainly by best guesses. Especially in terms of safety and interpretability, the field of end-to-end autonomous driving lacks generalizing solutions for holistically describing model behavior and offering comprehensive insights into failure modes [4], [5] .  \nIn this work, we address this limitation by introducing a concept-based interpretability framework for end-to-end autonomous driving. Our approach reveals interpretable latentspace components and directly connects them to trajectorylevel decision scores. Specifically, we integrate dictionary learning via Sparse Autoencoders (SAEs) into the model’s  \nFranz Motzkus is with AUMOVIO, Germany, and with the Department of Applied Computer Science, University of Bamberg, Germany.  \nfeature space, decomposing latent activations into sparse, human-interpretable concepts. Rather than treating the latent space as an opaque collection of convoluted features, this decomposition provides a more transparent representation in which individual concepts correspond to meaningful patterns learned by the model. Consequently, complex driving decisions can be expressed as a composition of semantically meaningful elements with causal influence on the predicted future trajectory. This concept-based perspective offers a more interpretable view of the model’s internal reasoning process while preserving the representational capacity required for autonomous driving.  \nTo validate our approach, we perform concept-level interventions by selectively suppressing individual latent components. We show that interventions on concepts associated with erroneous behavior lead to measurable improvements in","cbCaifIbReVq5C8Z","https://ap.wps.com/l/cbCaifIbReVq5C8Z","pdf",34666508,1,12,"English","en",105,"# Introduction\n## Problem of opacity in end-to-end driving models\n## Concept-based interpretability framework\n# Related Work\n## End-to-End Autonomous Driving","[{\"question\":\"Why does end-to-end autonomous driving make model behavior harder to interpret?\",\"answer\":\"Unified transformer architectures combine perception, prediction, and planning in a single model, reducing transparency and removing module-level interfaces. As complexity grows, internal decision-making becomes more opaque and failure-mode analysis becomes harder and more costly.\"},{\"question\":\"How does the proposed method improve interpretability for end-to-end driving decisions?\",\"answer\":\"The approach integrates unsupervised dictionary learning using Sparse Autoencoders (SAEs) to decompose latent activations into sparse, human-interpretable concepts. Driving decisions are then expressed as a composition of semantically meaningful elements with causal influence on predicted trajectories.\"},{\"question\":\"What is the effect of interventions at the concept level?\",\"answer\":\"Targeted suppression of specific latent components associated with erroneous behavior leads to measurable improvements in downstream driving metrics. Reported improvements include collision avoidance, drivable area, and traffic light compliance.\"}]",1784192868,30,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"driving-the-wrong-way-leveraging-interpretability-in-end-to-end-autonomous-driving-models","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/driving-the-wrong-way-leveraging-interpretability-in-end-to-end-autonomous-driving-models/84105/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why does end-to-end autonomous driving make model behavior harder to interpret?","Question",{"text":74,"@type":75},"Unified transformer architectures combine perception, prediction, and planning in a single model, reducing transparency and removing module-level interfaces. As complexity grows, internal decision-making becomes more opaque and failure-mode analysis becomes harder and more costly.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed method improve interpretability for end-to-end driving decisions?",{"text":79,"@type":75},"The approach integrates unsupervised dictionary learning using Sparse Autoencoders (SAEs) to decompose latent activations into sparse, human-interpretable concepts. Driving decisions are then expressed as a composition of semantically meaningful elements with causal influence on predicted trajectories.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the effect of interventions at the concept level?",{"text":83,"@type":75},"Targeted suppression of specific latent components associated with erroneous behavior leads to measurable improvements in downstream driving metrics. 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