CORTEX Automotive Radar and Video with GPS/IMU Ground Truth Dataset from Trials at MIRA Test Track - Run5

Contains data collected from varions sensors used for data collection campaign conducted by the University of Birmingham (UoB) and the other CORTEX (Cognitive Real-Time System for Autonomous Vehicles) project partners on 10th September 2021 at Horiba MIRA test track. Sensor data that is included is from 1) a 8Tx + 16Rx 77GHz MIMO Radar, 2) ZED Stereo camera, 3) Lidar system and 4) IMU systems. Also include is sample code to time syncronize data between the radar and camera sensors

CORTEX Automotive Radar and Video with GPS/IMU Ground Truth Dataset from Trials at MIRA Test Track - Run1

Contains data collected from varions sensors used for data collection campaign conducted by the University of Birmingham (UoB) and the CORTEX (Cognitive Real-Time System for Autonomous Vehicles) project partners on 10th September 2021 at Horiba MIRA test track. Sensor data that is included is from 1) a 8Tx + 16Rx 77GHz MIMO Radar, 2) ZED Stereo camera, 3) Lidar system and 4) IMU systems. Also include is sample code to time syncronize data between the radar and camera sensors

CORTEX Automotive Radar and Video with GPS/IMU Ground Truth Dataset from Trials at MIRA Test Track - Run2

Contains data collected from varions sensors used for data collection campaign conducted by the University of Birmingham (UoB) and the other CORTEX (Cognitive Real-Time System for Autonomous Vehicles) project partners on 10th September 2021 at Horiba MIRA test track. Sensor data that is included is from 1) a 8Tx + 16Rx 77GHz MIMO Radar, 2) ZED Stereo camera, 3) Lidar system and 4) IMU systems. Also include is sample code to time syncronize data between the radar and camera sensors

CORTEX Automotive Radar and Video with GPS/IMU Ground Truth Dataset from Trials at MIRA Test Track - Run4

Contains data collected from varions sensors used for data collection campaign conducted by the University of Birmingham (UoB) and the other CORTEX (Cognitive Real-Time System for Autonomous Vehicles) project partners on 10th September 2021 at Horiba MIRA test track. Sensor data that is included is from 1) a 8Tx + 16Rx 77GHz MIMO Radar, 2) ZED Stereo camera, 3) Lidar system and 4) IMU systems. Also include is sample code to time syncronize data between the radar and camera sensors

CORTEX Automotive Radar and Video with GPS/IMU Ground Truth Dataset from Trials at MIRA Test Track - Run3

Contains data collected from varions sensors used for data collection campaign conducted by the University of Birmingham (UoB) and the other CORTEX (Cognitive Real-Time System for Autonomous Vehicles) project partners on 10th September 2021 at Horiba MIRA test track. Sensor data that is included is from 1) a 8Tx + 16Rx 77GHz MIMO Radar, 2) ZED Stereo camera, 3) Lidar system and 4) IMU systems. Also include is sample code to time syncronize data between the radar and camera sensors

MISL Measurement Campaign for the project CORTEX (Cognitive Real-Time System for Autonomous Vehicles)

The dataset contains data collected by the the MISL (Microwave Integrated Systems Laboratory) group at the University of Birmingham, for the project CORTEX (Cognitive Real-Time System for Autonomous Vehicles), which is funded by Innovate UK. The objectives of the CORTEX project are to provide novel sensing technology, robust in all-weather and all-road conditions, to support the delivery of L4/5 autonomy, using advanced sensor fusion, tracking and cognitive radar techniques

High-Resolution Automotive Imaging Using MIMO Radar and Doppler Beam Sharpening

A highly detailed sensing of a vehicle's surrounding environment is a key requirement for the advancement of autonomous driving technology. While conventional automotive radar sensors remain robust under challenging weather conditions, poor cross-range resolution and high sidelobe levels present significant challenges. In this article, we propose an approach that combines multiple-input multiple-output (MIMO) beamforming with Doppler beam sharpening. We demonstrate a significant improvement in terms of cross-range resolution and, importantly, nearly 20-dB sidelobe suppression compared to conventional MIMO processing. This approach is investigated in detail and validated through theoretical analysis, simulation, and experiment using data recorded on a moving vehicle. We demonstrate performance that is comparable to a high-resolution mechanically scanned radar using a commercially available MIMO sensor.</p

COSMOS dataset for co-existence/ interference analysis and simultaneous scene representation by automotive radar and video with GPS/IMU ground truth - Sub-Dataset B

The objectives of the described radar trials were to conduct radar measurements at the background of interference in the 76 – 81 GHz frequency band to: • Estimate the impact of interference in an adaptive cruise control (ACC) and cross-traffic alert (CTA) scenarios. • Analyse radar field of view shadowing due to a close target. • Identify an oncoming vehicle from the received interference which is otherwise blind due to radar field of view (FoV) obstruction. • Estimate the multipath interference in a reflective scenario. Repository Overview: The full dataset collected during the trails is over 2 TB. Depending upon the scenario and data collection duration, the size of raw data captured from INRAS Radarlog varies from 1 GB to 7 GB whereas for INRAS Radarbook, it varies from 1 GB to 4 GB. Therefore, due to extremely large files sizes, only the most suitable representative of the defined use-cases is included in the repository. Moreover, the full post-processed radar imagery is only shown for a few example cases. Additional data may be available on request

COSMOS dataset for co-existence/ interference analysis and simultaneous scene representation by automotive radar and video with GPS/IMU ground truth

The objectives of the described radar trials were to conduct radar measurements at the background of interference in the 76 – 81 GHz frequency band to: • Estimate the impact of interference in an adaptive cruise control (ACC) and cross-traffic alert (CTA) scenarios. • Analyse radar field of view shadowing due to a close target. • Identify an oncoming vehicle from the received interference which is otherwise blind due to radar field of view (FoV) obstruction. • Estimate the multipath interference in a reflective scenario. Repository Overview: The full dataset collected during the trails is over 2 TB. Depending upon the scenario and data collection duration, the size of raw data captured from INRAS Radarlog varies from 1 GB to 7 GB whereas for INRAS Radarbook, it varies from 1 GB to 4 GB. Therefore, due to extremely large files sizes, only the most suitable representative of the defined use-cases is included in the repository. Moreover, the full post-processed radar imagery is only shown for a few example cases. Additional data may be available on request