WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios

• URL: http://arxiv.org/abs/2510.26125v2
• Date: Wed, 05 Nov 2025 00:47:06 GMT
• Title: WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios
• Authors: Runsheng Xu, Hubert Lin, Wonseok Jeon, Hao Feng, Yuliang Zou, Liting Sun, John Gorman, Kate Tolstaya, Sarah Tang, Brandyn White, Ben Sapp, Mingxing Tan, Jyh-Jing Hwang, Dragomir Anguelov,
• Abstract summary: Vision-based end-to-end (E2E) driving has garnered significant interest in the research community.<n>Current E2E driving benchmarks primarily feature nominal scenarios.<n>Existing open-loop evaluation metrics often fall short in capturing the multi-modal nature of driving.
• Score: 27.692139875029238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-based end-to-end (E2E) driving has garnered significant interest in the research community due to its scalability and synergy with multimodal large language models (MLLMs). However, current E2E driving benchmarks primarily feature nominal scenarios, failing to adequately test the true potential of these systems. Furthermore, existing open-loop evaluation metrics often fall short in capturing the multi-modal nature of driving or effectively evaluating performance in long-tail scenarios. To address these gaps, we introduce the Waymo Open Dataset for End-to-End Driving (WOD-E2E). WOD-E2E contains 4,021 driving segments (approximately 12 hours), specifically curated for challenging long-tail scenarios that that are rare in daily life with an occurring frequency of less than 0.03%. Concretely, each segment in WOD-E2E includes the high-level routing information, ego states, and 360-degree camera views from 8 surrounding cameras. To evaluate the E2E driving performance on these long-tail situations, we propose a novel open-loop evaluation metric: Rater Feedback Score (RFS). Unlike conventional metrics that measure the distance between predicted way points and the logs, RFS measures how closely the predicted trajectory matches rater-annotated trajectory preference labels. We have released rater preference labels for all WOD-E2E validation set segments, while the held out test set labels have been used for the 2025 WOD-E2E Challenge. Through our work, we aim to foster state of the art research into generalizable, robust, and safe end-to-end autonomous driving agents capable of handling complex real-world situations.

Related papers

• Driving with A Thousand Faces: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving [39.31712441721641]
  Person2Drive is a comprehensive personalized E2E-AD platform and benchmark.<n>It includes an open-source, flexible data collection system that simulates realistic scenarios to generate personalized driving datasets.<n>Our dataset and code will be released after acceptance.
  arXiv  Detail & Related papers  (2026-02-21T08:42:32Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine [73.74077186298523]
  CoReVLA is a continual learning framework for autonomous driving.<n>It improves the performance in long-tail scenarios through a dual-stage process of data Collection and behavior Refinement.<n>CoReVLA achieves a Driving Score (DS) of 72.18 and a Success Rate (SR) of 50%, outperforming state-of-the-art methods by 7.96 DS and 15% SR under long-tail, safety-critical scenarios.
  arXiv  Detail & Related papers  (2025-09-19T13:25:56Z)
• TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions [17.019567722324666]
  We introduce the Trail-based Off-road Multimodal dataset (TOMD), a comprehensive dataset specifically designed for such environments.<n>TOMD features high-fidelity multimodal sensor data -- including 128-channel LiDAR, stereo imagery, IMU, and illumination measurements.<n>We also propose a dynamic multiscale data fusion model for accurate traversable pathway prediction.
  arXiv  Detail & Related papers  (2025-06-24T23:58:44Z)
• ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation [44.16465715911478]
  We propose ORION, a holistic E2E autonomous driving framework by vision-language instructed action generation.<n>Our method achieves an impressive closed-loop performance of 77.74 Driving Score (DS) and 54.62% Success Rate (SR) on the challenge Bench2Drive datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:18:43Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving [59.705635382104454]
  We present Bench2Drive, the first benchmark for evaluating E2E-AD systems' multiple abilities in a closed-loop manner.<n>We implement state-of-the-art E2E-AD models and evaluate them in Bench2Drive, providing insights regarding current status and future directions.
  arXiv  Detail & Related papers  (2024-06-06T09:12:30Z)
• DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving [81.04174379726251]
  This paper collects a comprehensive end-to-end driving dataset named DriveCoT. It contains sensor data, control decisions, and chain-of-thought labels to indicate the reasoning process. We propose a baseline model called DriveCoT-Agent, trained on our dataset, to generate chain-of-thought predictions and final decisions.
  arXiv  Detail & Related papers  (2024-03-25T17:59:01Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• 2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection [26.086623067939605]
  In this report, we introduce a real-time method to detect the 2D objects from images. We leverage accelerationRT to optimize the inference time of our detection pipeline. Our framework achieves the latency of 45.8ms/frame on an Nvidia Tesla V100 GPU.
  arXiv  Detail & Related papers  (2021-06-16T11:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.