search
HomeTechnology peripheralsAIBerkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

As autonomous driving technology continues to iterate, vehicle behavior and trajectory prediction are of extremely important significance for efficient and safe driving. Although traditional trajectory prediction methods such as dynamic model deduction and accessibility analysis have the advantages of clear form and strong interpretability, their modeling capabilities for the interaction between the environment and objects are relatively limited in complex traffic environments. Therefore, in recent years, a large number of research and applications have been based on various deep learning methods (such as LSTM, CNN, Transformer, GNN, etc.), and various data sets such as BDD100K, nuScenes, Stanford Drone, ETH/UCY, INTERACTION, ApolloScape, etc. have also emerged. , which provides strong support for training and evaluating deep neural network models. Many SOTA models such as GroupNet, Trajectron, MultiPath, etc. have shown good performance.

The above models and data sets are concentrated in normal road driving scenarios, and make full use of infrastructure and features such as lane lines and traffic lights to assist in the prediction process; due to limitations of traffic regulations, The movement patterns of most vehicles are also relatively clear. However, in the "last mile" of autonomous driving - autonomous parking scenarios, we will face many new difficulties:

  • Traffic rules in the parking lot The requirements for lane lines and lane lines are not strict, and vehicles often drive at will and "take shortcuts"
  • In order to complete the parking task, vehicles need to complete more complex parking actions, including frequent reversing, Parking, steering, etc. When the driver is inexperienced, parking may become a long process
  • There are many obstacles and clutter in the parking lot, and the distance between vehicles is close. If you are not careful, you may cause Collisions and scratches
  • Pedestrians often walk through the parking lot at will, and vehicles need more avoidance actions
    In such a scenario, simply apply the existing It is difficult for the trajectory prediction model to achieve ideal results, and the retraining model lacks the support of corresponding data. Current parking scene-based data sets such as CNRPark EXT and CARPK are only designed for free parking space detection. The pictures come from the first-person perspective of surveillance cameras, have low sampling rates, and have many occlusions, making them unable to be used for trajectory prediction.

In the just concluded 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022) in October 2022, from University of California, Berkeley Researchers released the first high-definition video & trajectory data set for parking scenes, and based on this data set, used CNN and Transformer architecture to propose a trajectory prediction model called "ParkPredict" .

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

  • Paper link: https://arxiv.org/abs/2204.10777
  • Dataset home page, trial and download application: https://sites.google.com/berkeley.edu/dlp-dataset (If you cannot access, you can Try the alternative page https://www.php.cn/link/966eaa9527eb956f0dc8788132986707 )
  • ##Dataset Python API: https://github.com/MPC- Berkeley/dlp-dataset
Dataset information

The data set was collected by drone, with a total duration of 3.5 hours, video resolution For 4K, the sampling rate is 25Hz. The view covers a car park area of ​​approximately 140m x 80m, with a total of approximately 400 parking spaces. The dataset is accurately annotated, and a total of 1216 motor vehicles, 3904 bicycles, and 3904 pedestrian trajectories were collected.

After reprocessing, the trajectory data can be read in the form of JSON and loaded into the data structure of the connection graph (Graph):

  • Individual (Agent): Each individual (Agent) is an object moving in the current scene (Scene). It has attributes such as geometric shape and type. Its movement trajectory is stored as a file containing Linked List of Instances
  • Instance: Each instance is the state of an individual (Agent) in a frame (Frame). Contains its position, angle, speed and acceleration. Each instance contains a pointer to the instance of the individual in the previous frame and the next frame
  • Frame (Frame): Each frame (Frame) is a sampling point, and its Contains all visible instances (Instance) at the current time, and pointers to the previous frame and the next frame
  • Obstacle (Obstacle): The obstacle is in this record Objects that do not move at all, including the position, corner and geometric size of each object
  • Scene (Scene): Each scene (Scene) corresponds to a recorded video file, which contains pointers , pointing to the first and last frames of the recording, all individuals (Agents) and all obstacles (Obstacles)

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

provided by the data set Two download formats:

JSON only (recommended) : JSON file contains the types, shapes of all individuals , trajectories and other information can be directly read, previewed, and generated semantic images (Semantic Images) through the open source Python API. If the research goal is only trajectory and behavior prediction, the JSON format can meet all needs.

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

##Original video and annotation: If the research is based on the original camera For topics in the field of machine vision such as target detection, separation, and tracking of raw images, you may need to download the original video and annotation. If this is required, the research needs need to be clearly described in the dataset application. In addition, the annotation file needs to be parsed by itself.

Behavior and trajectory prediction model: ParkPredict

As an application example, in the paper "ParkPredict: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN" at IEEE ITSC 2022 and Transformer", the research team used this data set to predict the vehicle's intent (Intent) and trajectory (Trajectory) in the parking lot scene based on the CNN and Transformer architecture.

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

The team used the CNN model to predict the distribution probability of vehicle intent (Intent) by constructing semantic images. This model only needs to construct local environmental information of the vehicle, and can continuously change the number of available intentions according to the current environment.

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

The team improved the Transformer model and provided the intent (Intent) prediction results, the vehicle's movement history, and the semantic map of the surrounding environment as inputs to achieve Multi-modal intention and behavior prediction.

Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.

Summary

  • As the first high-precision data set for parking scenarios, the Dragon Lake Parking (DLP) data set can achieve large-scale target recognition and tracking, idle Parking space detection, vehicle and pedestrian behavior and trajectory prediction, imitation learning and other research provide data and API support
  • By using CNN and Transformer architecture, the ParkPredict model’s behavior and performance in parking scenarios In addition to showing good capabilities in trajectory prediction
  • Dragon Lake Parking (DLP) data set is open for trial and application. You can visit the data set homepage https://sites.google.com/ berkeley.edu/dlp-dataset for more information (if you cannot access, you can try the alternative page https://www.php.cn/link/966eaa9527eb956f0dc8788132986707

The above is the detailed content of Berkeley open sourced the first high-definition data set and prediction model in parking scenarios, supporting target recognition and trajectory prediction.. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:51CTO.COM. If there is any infringement, please contact admin@php.cn delete
解读CRISP-ML(Q):机器学习生命周期流程解读CRISP-ML(Q):机器学习生命周期流程Apr 08, 2023 pm 01:21 PM

译者 | 布加迪审校 | 孙淑娟目前,没有用于构建和管理机器学习(ML)应用程序的标准实践。机器学习项目组织得不好,缺乏可重复性,而且从长远来看容易彻底失败。因此,我们需要一套流程来帮助自己在整个机器学习生命周期中保持质量、可持续性、稳健性和成本管理。图1. 机器学习开发生命周期流程使用质量保证方法开发机器学习应用程序的跨行业标准流程(CRISP-ML(Q))是CRISP-DM的升级版,以确保机器学习产品的质量。CRISP-ML(Q)有六个单独的阶段:1. 业务和数据理解2. 数据准备3. 模型

人工智能的环境成本和承诺人工智能的环境成本和承诺Apr 08, 2023 pm 04:31 PM

人工智能(AI)在流行文化和政治分析中经常以两种极端的形式出现。它要么代表着人类智慧与科技实力相结合的未来主义乌托邦的关键,要么是迈向反乌托邦式机器崛起的第一步。学者、企业家、甚至活动家在应用人工智能应对气候变化时都采用了同样的二元思维。科技行业对人工智能在创建一个新的技术乌托邦中所扮演的角色的单一关注,掩盖了人工智能可能加剧环境退化的方式,通常是直接伤害边缘人群的方式。为了在应对气候变化的过程中充分利用人工智能技术,同时承认其大量消耗能源,引领人工智能潮流的科技公司需要探索人工智能对环境影响的

找不到中文语音预训练模型?中文版 Wav2vec 2.0和HuBERT来了找不到中文语音预训练模型?中文版 Wav2vec 2.0和HuBERT来了Apr 08, 2023 pm 06:21 PM

Wav2vec 2.0 [1],HuBERT [2] 和 WavLM [3] 等语音预训练模型,通过在多达上万小时的无标注语音数据(如 Libri-light )上的自监督学习,显著提升了自动语音识别(Automatic Speech Recognition, ASR),语音合成(Text-to-speech, TTS)和语音转换(Voice Conversation,VC)等语音下游任务的性能。然而这些模型都没有公开的中文版本,不便于应用在中文语音研究场景。 WenetSpeech [4] 是

条形统计图用什么呈现数据条形统计图用什么呈现数据Jan 20, 2021 pm 03:31 PM

条形统计图用“直条”呈现数据。条形统计图是用一个单位长度表示一定的数量,根据数量的多少画成长短不同的直条,然后把这些直条按一定的顺序排列起来;从条形统计图中很容易看出各种数量的多少。条形统计图分为:单式条形统计图和复式条形统计图,前者只表示1个项目的数据,后者可以同时表示多个项目的数据。

自动驾驶车道线检测分类的虚拟-真实域适应方法自动驾驶车道线检测分类的虚拟-真实域适应方法Apr 08, 2023 pm 02:31 PM

arXiv论文“Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving“,2022年5月,加拿大滑铁卢大学的工作。虽然自主驾驶的监督检测和分类框架需要大型标注数据集,但光照真实模拟环境生成的合成数据推动的无监督域适应(UDA,Unsupervised Domain Adaptation)方法则是低成本、耗时更少的解决方案。本文提出对抗性鉴别和生成(adversarial d

数据通信中的信道传输速率单位是bps,它表示什么数据通信中的信道传输速率单位是bps,它表示什么Jan 18, 2021 pm 02:58 PM

数据通信中的信道传输速率单位是bps,它表示“位/秒”或“比特/秒”,即数据传输速率在数值上等于每秒钟传输构成数据代码的二进制比特数,也称“比特率”。比特率表示单位时间内传送比特的数目,用于衡量数字信息的传送速度;根据每帧图像存储时所占的比特数和传输比特率,可以计算数字图像信息传输的速度。

数据分析方法有哪几种数据分析方法有哪几种Dec 15, 2020 am 09:48 AM

数据分析方法有4种,分别是:1、趋势分析,趋势分析一般用于核心指标的长期跟踪;2、象限分析,可依据数据的不同,将各个比较主体划分到四个象限中;3、对比分析,分为横向对比和纵向对比;4、交叉分析,主要作用就是从多个维度细分数据。

聊一聊Python 实现数据的序列化操作聊一聊Python 实现数据的序列化操作Apr 12, 2023 am 09:31 AM

​在日常开发中,对数据进行序列化和反序列化是常见的数据操作,Python提供了两个模块方便开发者实现数据的序列化操作,即 json 模块和 pickle 模块。这两个模块主要区别如下:json 是一个文本序列化格式,而 pickle 是一个二进制序列化格式;json 是我们可以直观阅读的,而 pickle 不可以;json 是可互操作的,在 Python 系统之外广泛使用,而 pickle 则是 Python 专用的;默认情况下,json 只能表示 Python 内置类型的子集,不能表示自定义的

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
2 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. How to Fix Audio if You Can't Hear Anyone
2 weeks agoBy尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

VSCode Windows 64-bit Download

VSCode Windows 64-bit Download

A free and powerful IDE editor launched by Microsoft