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.
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.
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.
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.
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.
arXiv:2610.01746v1 Announce Type: cross Abstract: Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures off...
arXiv:2607. 01639v1 Announce Type: new Abstract: Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a scientific basis for transportation planning, management and control.
arXiv:2505.08854v2 Announce Type: replace Abstract: Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled ca...
arXiv:2607. 26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks.
nuReasoning is a large‑scale, real‑world dataset and benchmark designed to advance reasoning‑centric autonomous driving in long‑tail scenarios. It contains 20,000 twenty‑second clips from multiple cities, each annotated with synchronized multi‑camera images, LiDAR, HD maps, object labels, and human‑verified reasoning labels covering spatial, decision, and counterfactual reasoning. The benchmark supports both reasoning evaluation and planning evaluation, demonstrating that fine‑tuning vision‑language models on nuReasoning improves question answering and that reasoning supervision boosts planning performance even without textual outputs at inference time.
The paper presents a comparative study of motion planning methods from major autonomous driving benchmarks—CARLA, nuPlan, and the Waymo Open Dataset—using CARLA Leaderboard v2.1 as a unified evaluation platform. Eight representative planners (TF++, InterFuser, TCP, PDM‑Lite, MTR+MPC, CaRL, PlanT 2.0, Diffusion planner) are evaluated to highlight their strengths, weaknesses, prevailing trends, and common challenges in motion planning research.
The paper presents NOMAD, a method that uses self‑play multi‑agent reinforcement learning to adapt autonomous driving policies to new cities without any human demonstration data from those cities. By leveraging only the target city’s map and meta‑information, NOMAD improves task success rates and trajectory realism in simulation, offering a scalable alternative to data‑intensive city‑transfer approaches. The approach demonstrates that reliable driving can be achieved in substantially different urban environments through map‑based self‑play.