arXiv Machine Learning
Jun 3

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

arXiv:2606. 02956v1 Announce Type: cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity.

By Richard Schwarzkopf, Fabian Immel, Alexander Blumberg, Jonas Merkert, Nils Rack, Kaiwen Wang, Fabian Konstantinidis, Julian Truetsch, Carlos Fernandez, Annika B\"atz, Kevin R\"osch, Marlon Steiner, Willi Poh, Yinzhe Shen, Royden Wagner, Felix Hauser, Dominik Strutz, Jaime Villa, Gleb Stepanov, Holger Caesar, \"Omer \c{S}ahin Ta\c{s}, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller
arXiv AI
Jul 2

Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark

arXiv:2607. 00710v1 Announce Type: cross Abstract: Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategically design impactful ones.

By Richard Schwarzkopf, Jonas Merkert, Frank Bieder, Annika B\"atz, Alexander Blumberg, Carlos Fernandez, Felix Hauser, Fabian Immel, Christian Kinzig, Hendrik K\"onigshof, Fabian Konstantinidis, Martin Lauer, Willi Poh, Nils Rack, Kevin R\"osch, Yinzhe Shen, Marlon Steiner, Gleb Stepanov, Dominik Strutz, \"Omer \c{S}ahin Ta\c{s}, Julian Truetsch, Kaiwen Wang, Royden Wagner, Jan-Hendrik Pauls, Christoph Stiller
Hugging Face Trending Papers
Aug 18

Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving

The paper introduces a plug‑and‑play method that injects traffic‑element signals—such as traffic lights and road signs—into end‑to‑end autonomous driving models with minimal architectural changes. By augmenting several public datasets with comprehensive traffic‑element annotations, the authors evaluate this integration across diverse driving paradigms, consistently improving performance on nuScenes, NAVSIM‑v1, NAVSIM‑v2, and Bench2Drive. The approach achieves a new state‑of‑the‑art result on the challenging NAVSIM‑v2 benchmark, demonstrating the broad utility of traffic‑element awareness.

arXiv Computer Vision
Oct 2

A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform

The paper reviews end‑to‑end autonomous driving (E2E‑AD) training, framing it as a Data‑Strategy‑Platform system. It surveys recent advances in data pipelines, learning paradigms, and training infrastructures, and discusses how these layers interact to influence model performance, robustness, and deployability. The authors highlight current limitations and propose a future vision that prioritizes data value, foundation‑driven generalization, and integrated training‑testing loops for more robust, scalable, and trustworthy autonomous driving systems.

By Chengkai Xu, Yiming Cui, Jiaqi Liu, Yicheng Guo, Cheng Qin, Geyuan Zhang, Xinwei Dong, Shiyu Fang, Peng Hang, Jian Sun
arXiv AI
Jun 10

TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.

By Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu, Halil Ibrahim Kuru, Baris Can Cam, Halil Ibrahim Ozturk, Ozsel Kilinc