RADIal is available now! Check the download section

Related tags

Deep LearningRADIal
Overview

Watch the video

Latest news:

RADIal is available now! Check the download section. However, because we are currently working on the data anonymization, we provide for now a low resolution preview video stream. The full resolution will be provided once the anonymization is completed, planned by 2022, February.

RADIal dataset

RADIal stands for “Radar, Lidar et al.” It's a collection of 2-hour of raw data from synchronized automotive-grade sensors (camera, laser, High Definition radar) in various environments (citystreet, highway, countryside road) and comes with GPS and vehicle’s CAN traces.

RADIal contains 91 sequences of 1 to 4 minutes in duration, for a total of 2 hours. These sequences are categorized in highway, country-side and city driving. The distribution of the sequences is indicated in the figure below. Each sequence contains raw sensor signals recorded with their native frame rate. There are approximately 25,000 frames with the three sensors synchronized, out of which 8,252 are labelled with a total of 9,550 vehicles.

Sensor specifications

Central to the RADIal dataset, our high-definition radar is composed of NRx=16 receiving antennas and NTx= 12 transmitting antennas, leading to NRx·NTx= 192 virtual antennas. This virtual-antenna array enables reaching a high azimuth angular resolution while estimating objects’ elevation angles as well. As the radar signal is difficult to interpret by annotators and practitioners alike, a 16-layer automotive-grade laser scanner (LiDAR) and a 5 Mpix RGB camera are also provided. The camera is placed below the interior mirror behind the windshield while the radar and the LiDAR are installed in the middle of the front ventilation grid, one above the other. The three sensors have parallel horizontallines of sight, pointing in the driving direction. Their extrinsic parameters are provided together with the dataset. RADIal also offers synchronized GPS and CAN traces which give access to the geo-referenced position of the vehicle as well as its driving information such as speed, steering wheelangle and yaw rate. The sensors’ specifications are detailed in the table below.

Dataset structure

RADIal is a unique folder containing all the recorded sequences. Each sequence is a folder containing:

  • A preview video of the scene (low resolution);
  • The camera data compressed in MJPEG format (will be released by 2022, February);
  • The Laser Scanner point cloud data saved in a binary file;
  • The ADC radar data saved in a binary file. There are 4 files in total, one file for each radar chip, each chip containing 4 Rx antennas;
  • The GPS data saved in ASCII format
  • The CAN traces of the vehicle saved in binary format
  • And finally, a log file that provides the timestamp of each individual sensor event.

We provide in a Python library DBReader to read the data. Because all the radar data are recorded in a RAW format, that is to say the signal after the Analog to Digital Conversion (ADC), we provided too an optimized Python library SignalProcessing to process the Radar signal and generate either the Power Spectrums, the Point Cloud or the Range-Azimuth map.

Labels

Out of the 25,000 synchronized frames, 8,252 frames are labelled. Labels for vehicles are stored in a separated csv file. Each label containg the following information:

  • numSample: number of the current synchronized sample between all the sensors. That is to say, this label can be projected in each individual sensor with a common dataset_index value. Note that there might be more than one line with the same numSample, one line per label;
  • [x1_pix, y1_pix, x2_pix, y2_pix]: 2D coordinates of the vehicle' bouding boxes in the camera coordinates;
  • [laser_X_m, laser_Y_m, laser_Z_m]: 3D coordinates of the vehicle in the laser scanner coordinates system. Note that this 3D point is the middle of either the back or front visible face of the vehicle;
  • [radar_X_m, radar_Y_m, radar_R_m, radar_A_deg, radar_D, radar_P_db]: 2D coordinates (bird' eyes view) of the vehicle in the radar coordinates system either in cartesian (X,Y) or polar (R,A) coordinates. radar_D is the Doppler value and radar_P_db is the power of the reflected signal;
  • dataset: name of sequence it belongs to;
  • dataset_index: frame index in the current sequence;
  • Difficult: either 0 or 1

Note that -1 in all field means a frame without any label.

Labels for the Free-driving-space is provided as a segmentaion mask saved in a png file.

Download instructions

To download the raw dataset, please follow these instructions.

$ wget -c -i download_urls.txt -P your_target_path
$ unzip 'your_target_path/*.zip' -d your_target_path
$ rm -Rf your_target_path/*.zip

You will have then to use the SignalProcessing library to generate data for each modalities uppon your need.

We provide too a "ready to use" dataset that can be loaded with the PyTorch data loader example provided in the Loader folder.

$ wget https://www.dropbox.com/s/bvbndch5rucyp97/RADIal.zip
Owner
valeo.ai
We are an international team based in Paris, conducting AI research for Valeo automotive applications, in collaboration with world-class academics.
valeo.ai
It's a powerful version of linebot

CTPS-FINAL Linbot-sever.py 主程式 Algorithm.py 推薦演算法,媒合餐廳端資料與顧客端資料 config.ini 儲存 channel-access-token、channel-secret 資料 Preface 生活在成大將近4年,我們每天的午餐時間看著形形色色

1 Oct 17, 2022
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022
[EMNLP 2021] MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

MuVER This repo contains the code and pre-trained model for our EMNLP 2021 paper: MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity

24 May 30, 2022
Code and description for my BSc Project, September 2021

BSc-Project Disclaimer: This repo consists of only the additional python scripts necessary to run the agent. To run the project on your own personal d

Matin Tavakoli 20 Jul 19, 2022
The official homepage of the COCO-Stuff dataset.

The COCO-Stuff dataset Holger Caesar, Jasper Uijlings, Vittorio Ferrari Welcome to official homepage of the COCO-Stuff [1] dataset. COCO-Stuff augment

Holger Caesar 715 Dec 31, 2022
To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features

Ashish Kumar 4 Jun 11, 2022
:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)

R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirement

huybery 60 Dec 31, 2022
Real time sign language recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone in real time.

Mohit Kaushik 6 Jun 13, 2022
本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。

说明 本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。 python依赖 tf2.3 、cv2、numpy、pyqt5 pyqt5安装 pip install PyQt5 pip install PyQt5-tools 使用 程

4 May 04, 2022
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation Official PyTorch Implementation

: We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the w

Yuval Nirkin 182 Dec 14, 2022
PyTorch implementation of "LayoutTransformer: Layout Generation and Completion with Self-attention"

PyTorch implementation of "LayoutTransformer: Layout Generation and Completion with Self-attention" to appear in ICCV 2021

Kamal Gupta 75 Dec 23, 2022
BrainGNN - A deep learning model for data-driven discovery of functional connectivity

A deep learning model for data-driven discovery of functional connectivity https://doi.org/10.3390/a14030075 Usman Mahmood, Zengin Fu, Vince D. Calhou

Usman Mahmood 3 Aug 28, 2022
Dynamic Attentive Graph Learning for Image Restoration, ICCV2021 [PyTorch Code]

Dynamic Attentive Graph Learning for Image Restoration This repository is for GATIR introduced in the following paper: Chong Mou, Jian Zhang, Zhuoyuan

Jian Zhang 84 Dec 09, 2022
Really awesome semantic segmentation

really-awesome-semantic-segmentation A list of all papers on Semantic Segmentation and the datasets they use. This site is maintained by Holger Caesar

Holger Caesar 400 Nov 28, 2022
Differentiable rasterization applied to 3D model simplification tasks

nvdiffmodeling Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Automatic 3D Model

NVIDIA Research Projects 336 Dec 30, 2022
This repository contains code accompanying the paper "An End-to-End Chinese Text Normalization Model based on Rule-Guided Flat-Lattice Transformer"

FlatTN This repository contains code accompanying the paper "An End-to-End Chinese Text Normalization Model based on Rule-Guided Flat-Lattice Transfor

THUHCSI 74 Nov 28, 2022
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

231 Jan 05, 2023
A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling"

SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th

Zekarias Tilahun 24 Jun 21, 2022
PERIN is Permutation-Invariant Semantic Parser developed for MRP 2020

PERIN: Permutation-invariant Semantic Parsing David Samuel & Milan Straka Charles University Faculty of Mathematics and Physics Institute of Formal an

ÚFAL 40 Jan 04, 2023
Universal Adversarial Triggers for Attacking and Analyzing NLP (EMNLP 2019)

Universal Adversarial Triggers for Attacking and Analyzing NLP This is the official code for the EMNLP 2019 paper, Universal Adversarial Triggers for

Eric Wallace 248 Dec 17, 2022