Towards Vision Zero: The Accid3nD Dataset

• URL: http://arxiv.org/abs/2503.12095v1
• Date: Sat, 15 Mar 2025 11:42:16 GMT
• Title: Towards Vision Zero: The Accid3nD Dataset
• Authors: Walter Zimmer, Ross Greer, Daniel Lehmberg, Marc Pavel, Holger Caesar, Xingcheng Zhou, Ahmed Ghita, Mohan Trivedi, Rui Song, Hu Cao, Akshay Gopalkrishnan, Alois C. Knoll,
• Abstract summary: We present the Accid3nD dataset, a collection of real-world highway accidents in different weather and lighting conditions.<n>The dataset contains 111,945 labeled frames recorded from four roadside cameras and LiDARs at 25 Hz.<n>We propose an accident detection model that combines a rule-based approach with a learning-based one.
• Score: 18.133662927802614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable and sporadic outcomes of traffic networks. No public dataset contains 3D annotations of real-world accidents recorded from roadside sensors. We present the Accid3nD dataset, a collection of real-world highway accidents in different weather and lighting conditions. It contains vehicle crashes at high-speed driving with 2,634,233 labeled 2D bounding boxes, instance masks, and 3D bounding boxes with track IDs. In total, the dataset contains 111,945 labeled frames recorded from four roadside cameras and LiDARs at 25 Hz. The dataset contains six object classes and is provided in the OpenLABEL format. We propose an accident detection model that combines a rule-based approach with a learning-based one. Experiments and ablation studies on our dataset show the robustness of our proposed method. The dataset, model, and code are available on our website: https://accident-dataset.github.io.

Related papers

• Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors [6.420737230522813]
  Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year.<n>To reduce these fatalities, it is essential to address human errors like speeding, drunk driving, and distractions.<n>We propose an accident detection framework that combines a rule-based approach with a learning-based one.
  arXiv  Detail & Related papers  (2025-02-01T11:34:16Z)
• DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving [76.29141888408265]
  We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2023-04-03T17:37:00Z)
• Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving [91.39625612027386]
  We propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects.
  arXiv  Detail & Related papers  (2023-02-08T07:11:36Z)
• Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style Translation [0.3441021278275805]
  There is no difference between accident and near-miss at the time before the accident happened. Our contribution is to redefine the accident definition and re-annotate the accident inconsistency on DADA-2000 dataset together with near-miss. The proposed method integrates two different components: conditional style translation (CST) and separable 3-dimensional convolutional neural network (S3D)
  arXiv  Detail & Related papers  (2023-01-06T22:04:47Z)
• IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes [79.18349050238413]
  Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models. We build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames.
  arXiv  Detail & Related papers  (2022-10-23T23:03:17Z)
• Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions [0.0]
  We present a new dataset to enable robust autonomous driving via a novel data collection process. The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects.
  arXiv  Detail & Related papers  (2022-08-01T22:55:32Z)
• Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception [59.2014692323323]
  Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them. We propose a novel, end-to-end trainable Hindsight framework to extract contextual information from past data. We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets.
  arXiv  Detail & Related papers  (2022-03-22T00:58:27Z)
• 3D-VField: Learning to Adversarially Deform Point Clouds for Robust 3D Object Detection [111.32054128362427]
  In safety-critical settings, robustness on out-of-distribution and long-tail samples is fundamental to circumvent dangerous issues. We substantially improve the generalization of 3D object detectors to out-of-domain data by taking into account deformed point clouds during training. We propose and share open source CrashD: a synthetic dataset of realistic damaged and rare cars.
  arXiv  Detail & Related papers  (2021-12-09T08:50:54Z)
• How to Build a Curb Dataset with LiDAR Data for Autonomous Driving [11.632427050596728]
  Video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. Camera-based curb detection methods suffer from challenging illumination conditions. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand.
  arXiv  Detail & Related papers  (2021-10-08T08:32:37Z)
• One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
  We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available. We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
  arXiv  Detail & Related papers  (2021-06-21T12:28:08Z)
• Towards Anomaly Detection in Dashcam Videos [9.558392439655012]
  We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos. We present a large and diverse dataset of truck dashcam videos, namely RetroTrucks. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks.
  arXiv  Detail & Related papers  (2020-04-11T00:10:40Z)

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.