arXiv:2511.13744v1 Announce Type: new 
Abstract: End-to-end (E2E) autonomous driving heavily relies on closed-loop simulation, where perception, planning, and control are jointly trained and evaluated in interactive environments. Yet, most existing datasets are collected from the real world under non-interactive conditions, primarily supporting open-loop learning while offering limited value for closed-loop testing. Due to the lack of standardized, large-scale, and thoroughly verified datasets to facilitate learning of meaningful intermediate representations, such as bird's-eye-view (BEV) features, closed-loop E2E models remain far behind even simple rule-based baselines. To address this challenge, we introduce nuCarla, a large-scale, nuScenes-style BEV perception dataset built within the CARLA simulator. nuCarla features (1) full compatibility with the nuScenes format, enabling seamless transfer of real-world perception models; (2) a dataset scale comparable to nuScenes, but with more balanced class distributions; (3) direct usability for closed-loop simulation deployment; and (4) high-performance BEV backbones that achieve state-of-the-art detection results. By providing both data and models as open benchmarks, nuCarla substantially accelerates closed-loop E2E development, paving the way toward reliable and safety-aware research in autonomous driving.

تم تقديم مجموعة بيانات nuCarla كبيانات إدراك من منظور الطائر على طراز nuScenes، مصممة لبيئة محاكاة CARLA. تعالج هذه المجموعة القيود المفروضة على مجموعات البيانات الحالية التي تدعم بشكل أساسي التعلم في الحلقة المفتوحة من خلال توفير إطار محاكاة في الحلقة المغلقة. تتوافق nuCarla تمامًا مع تنسيق nuScenes، مما يسمح بنقل نماذج الإدراك من العالم الحقيقي، وتقدم مقياسًا قابلًا للمقارنة مع nuScenes، مما يعزز تدريب نماذج القيادة الذاتية من النهاية إلى النهاية.

Se ha introducido el conjunto de datos nuCarla como un conjunto de datos de percepción en vista cenital de estilo nuScenes, diseñado para el entorno de simulación CARLA. Este conjunto de datos aborda las limitaciones de los conjuntos de datos existentes que apoyan principalmente el aprendizaje en bucle abierto al proporcionar un marco de simulación en bucle cerrado. nuCarla es completamente compatible con el formato nuScenes, lo que permite la transferencia de modelos de percepción del mundo real, y ofrece una escala comparable a nuScenes, mejorando así el entrenamiento de modelos de conducció…

Le jeu de données nuCarla a été introduit comme un ensemble de données de perception en vue de dessus de style nuScenes, conçu pour l'environnement de simulation CARLA. Ce jeu de données répond aux limitations des ensembles de données existants qui soutiennent principalement l'apprentissage en boucle ouverte en fournissant un cadre de simulation en boucle fermée. nuCarla est entièrement compatible avec le format nuScenes, permettant le transfert de modèles de perception du monde réel, et offre une échelle comparable à nuScenes, améliorant ainsi l'entraînement des modèles de conduite autonome d…

The nuCarla dataset has been introduced as a large-scale, nuScenes-style bird's-eye view perception dataset designed for the CARLA simulation environment. This dataset addresses the limitations of existing datasets that primarily support open-loop learning by providing a closed-loop simulation framework. nuCarla is fully compatible with the nuScenes format, allowing for the transfer of real-world perception models, and offers a scale comparable to nuScenes, enhancing the training of end-to-end autonomous driving models.

nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation

arXiv:2503.07168v3 Announce Type: replace 
Abstract: As an essential component of autonomous driving systems, high-definition (HD) maps provide rich and precise environmental information for auto-driving scenarios; however, existing methods, which primarily rely on query-based detection frameworks to directly model map elements or implicitly propagate queries over time, often struggle to maintain consistent temporal perception outcomes. These inconsistencies pose significant challenges to the stability and reliability of real-world autonomous driving and map data collection systems. To address this limitation, we propose a novel end-to-end tracking framework for global map construction by temporally tracking map elements' historical trajectories. Firstly, instance-level historical rasterization map representation is designed to explicitly store previous perception results, which can control and maintain different global instances' history information in a fine-grained way. Secondly, we introduce a Map-Trajectory Prior Fusion module within this tracking framework, leveraging historical priors for tracked instances to improve temporal smoothness and continuity. Thirdly, we propose a global perspective metric to evaluate the quality of temporal geometry construction in HD maps, filling the gap in current metrics for assessing global geometric perception results. Substantial experiments on the nuScenes and Argoverse2 datasets demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in both single-frame and temporal metrics. The project page is available at: https://yj772881654.github.io/HisTrackMap.

يقدم HisTrackMap إطار عمل جديدًا للتتبع من البداية إلى النهاية لبناء خرائط عالية الدقة العالمية، حيث يتناول التحديات المتعلقة بالحفاظ على نتائج الإدراك الزمني المتسقة في أنظمة القيادة الذاتية. يستخدم هذا الإطار مسارات تاريخية لعناصر الخريطة لتحسين استقرار وموثوقية جمع بيانات الخرائط.

HisTrackMap presenta un nuevo marco de seguimiento de extremo a extremo para la construcción de mapas de alta definición globales, abordando los desafíos de mantener resultados de percepción temporal coherentes en los sistemas de conducción autónoma. Este marco utiliza las trayectorias históricas de los elementos del mapa para mejorar la estabilidad y la fiabilidad de la recopilación de datos cartográficos.

HisTrackMap présente un nouveau cadre de suivi de bout en bout pour la construction de cartes haute définition mondiales, répondant aux défis de maintien de résultats de perception temporelle cohérents dans les systèmes de conduite autonome. Ce cadre utilise les trajectoires historiques des éléments de la carte pour améliorer la stabilité et la fiabilité de la collecte de données cartographiques.

HisTrackMap introduces a novel end-to-end tracking framework for global high-definition map construction, addressing the challenges of maintaining consistent temporal perception outcomes in autonomous driving systems. This framework utilizes historical trajectories of map elements to enhance the stability and reliability of map data collection.

HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking

arXiv:2506.18124v3 Announce Type: replace-cross 
Abstract: Multiobject tracking (MOT) is an important task in applications including autonomous driving, ocean sciences, and aerospace surveillance. Traditional MOT methods are model-based and combine sequential Bayesian estimation with data association and an object birth model. More recent methods are fully data-driven and rely on the training of neural networks. Both approaches offer distinct advantages in specific settings. In particular, model-based methods are generally applicable across a wide range of scenarios, whereas data-driven MOT achieves superior performance in scenarios where abundant labeled data for training is available. A natural thought is whether a general framework can integrate the two approaches. This paper introduces a hybrid method that utilizes neural networks to enhance specific aspects of the statistical model in Bayesian MOT that have been identified as overly simplistic. By doing so, the performance of the prediction and update steps of Bayesian MOT is improved. To ensure tractable computation, our framework uses belief propagation to avoid high-dimensional operations combined with sequential Monte Carlo methods to perform low-dimensional operations efficiently. The resulting method combines the flexibility and robustness of model-based approaches with the capability to learn complex information from data of neural networks. We evaluate the performance of the proposed method based on the nuScenes autonomous driving dataset and demonstrate that it has state-of-the-art performance.

يقدم مقال جديد طريقة هجينة لتتبع الكائنات المتعددة باستخدام نماذج بايزي، حيث يتم دمج الشبكات العصبية لتعزيز النماذج الإحصائية التقليدية، مما يعالج القيود الموجودة في الأساليب الحالية. يهدف هذا التطور إلى تحسين الأداء في تطبيقات متنوعة، بما في ذلك القيادة الذاتية والمراقبة الجوية.

Un nuevo artículo presenta un método híbrido para el seguimiento multiobjetos bayesiano (MOT) que integra redes neuronales para mejorar los modelos estadísticos tradicionales, abordando las limitaciones de los enfoques existentes. Este desarrollo busca mejorar el rendimiento en diversas aplicaciones, incluidas la conducción autónoma y la vigilancia aeroespacial.

Un nouvel article présente une méthode hybride pour le suivi multi-objets bayésien (MOT) qui intègre des réseaux neuronaux pour améliorer les modèles statistiques traditionnels, en répondant aux limitations des approches existantes. Ce développement vise à améliorer les performances dans diverses applications, y compris la conduite autonome et la surveillance aérospatiale.

A new paper introduces a hybrid method for Bayesian Multiobject Tracking (MOT) that integrates neural networks to enhance traditional statistical models, addressing limitations in existing approaches. This development aims to improve performance in various applications, including autonomous driving and aerospace surveillance.

Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models

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nuCarla: A nuScenes-Style Bird's-Eye View Perception Dataset for CARLA Simulation

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