A Survey on Datasets for Decision-making of Autonomous Vehicle

• URL: http://arxiv.org/abs/2306.16784v2
• Date: Fri, 22 Sep 2023 09:21:34 GMT
• Title: A Survey on Datasets for Decision-making of Autonomous Vehicle
• Authors: Yuning Wang, Zeyu Han, Yining Xing, Shaobing Xu, Jianqiang Wang
• Abstract summary: Decision-making is one of the critical modules toward high-level automated driving. Data-driven decision-making approaches have aroused more and more focus. This study compares the state-of-the-art datasets of vehicle, environment, and driver related data.
• Score: 11.556769001552768
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope with well, data-driven decision-making approaches have aroused more and more focus. The datasets to be used in developing data-driven methods dramatically influences the performance of decision-making, hence it is necessary to have a comprehensive insight into the existing datasets. From the aspects of collection sources, driving data can be divided into vehicle, environment, and driver related data. This study compares the state-of-the-art datasets of these three categories and summarizes their features including sensors used, annotation, and driving scenarios. Based on the characteristics of the datasets, this survey also concludes the potential applications of datasets on various aspects of AV decision-making, assisting researchers to find appropriate ones to support their own research. The future trends of AV dataset development are summarized.

Related papers

• Collective Perception Datasets for Autonomous Driving: A Comprehensive Review [0.5326090003728084]
  This paper provides the first comprehensive review of collective perception datasets in the context of autonomous driving. The study aims to identify the key criteria of all datasets and to present their strengths, weaknesses, and anomalies.
  arXiv  Detail & Related papers  (2024-05-27T09:08:55Z)
• Collaborative Perception Datasets in Autonomous Driving: A Survey [0.0]
  The paper systematically analyzes a variety of datasets, comparing them based on aspects such as diversity, sensor setup, quality, public availability, and their applicability to downstream tasks. The importance of addressing privacy and security concerns in the development of datasets is emphasized, regarding data sharing and dataset creation.
  arXiv  Detail & Related papers  (2024-04-22T09:36:17Z)
• D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation [6.890077875318333]
  Driver to Evaluation dataset (D2E) is an autonomous decision-making dataset. It contains data on driver states, vehicle states, environmental situations, and evaluation scores from human reviewers. D2E contains over 1100 segments of interactive driving case data covering from human driver factor to evaluation results.
  arXiv  Detail & Related papers  (2024-04-12T21:29:18Z)
• SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control [59.20038082523832]
  We present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We develop a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data.
  arXiv  Detail & Related papers  (2024-03-28T14:07:13Z)
• UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction [93.77809355002591]
  We introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria. We conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. We provide insights into dataset characteristics to explain these findings.
  arXiv  Detail & Related papers  (2024-03-22T10:36:50Z)
• A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook [24.691922611156937]
  We present an exhaustive study of 265 autonomous driving datasets from multiple perspectives. We introduce a novel metric to evaluate the impact of datasets, which can also be a guide for creating new datasets. We discuss the current challenges and the development trend of the future autonomous driving datasets.
  arXiv  Detail & Related papers  (2024-01-02T22:35:33Z)
• Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving [38.28159034562901]
  Reason2Drive is a benchmark dataset with over 600K video-text pairs. We characterize the autonomous driving process as a sequential combination of perception, prediction, and reasoning steps. We introduce a novel aggregated evaluation metric to assess chain-based reasoning performance in autonomous systems.
  arXiv  Detail & Related papers  (2023-12-06T18:32:33Z)
• Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future [130.87142103774752]
  This review systematically assesses over seventy open-source autonomous driving datasets. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets. It also delves into the scientific and technical challenges that warrant resolution.
  arXiv  Detail & Related papers  (2023-12-06T10:46:53Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• 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)
• Diverse Complexity Measures for Dataset Curation in Self-driving [80.55417232642124]
  We propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
  arXiv  Detail & Related papers  (2021-01-16T23:45:02Z)

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.