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Active PROJECT GRANT Swedish Research Council

Advanced Radar Sensor Front-end Modules and Solutions for Increased Road Safety

145.07M kr SEK

Funder Vinnova
Recipient Organization Chalmers University of Technology
Country Sweden
Start Date Aug 01, 2024
End Date Jul 31, 2027
Duration 1,094 days
Number of Grantees 1
Roles Principal Investigator
Data Source Swedish Research Council
Grant ID 2024-00821_Vinnova
Grant Description

Purpose and goal:

The project is related to traffic safety. The proposed project will develop polarimetric radars and high-resolution radar sensors which will drastically improve the object detection and object tracking capabilities of the radar sensors, thereby ensuring early manoeuvring of autonomous vehicles in case of possible collision. Also, the polarimetric configuration of the developed radars will allow high probability detection and classification of large objects as well as vulnerable road users (VRUs) such as motorcycles, cyclists, etc. in challenging traffic environment.

Expected results and effects:

Road accidents are one of the major causes of death even in developed countries. Innovative radar sensor sets, and vehicle perception units capable of providing accurate driving environment information in all-weather condition can play a key role here and reduce road accidents significantly. The proposed project aims to develop concrete technology enablers for future autonomous cars with improved active radar sensors at 77GHz and perception systems, which will lead towards a sustainable transportation.

Approach and implementation:

We aim to develop gap waveguide based fully polarimetric as well has high resolution imaging automotive radars with dual linear polarized (LP) signal or dual circular polarized (CP) signals or combined linear-circular polarized signal. Hence, in this project we aim to use interference mitigation and multi-path suppression in analog domain by using CP antenna array.

Also, we will exploit the full potential of the radar data in terms of its localization capability for the task of bird eye view (BEV) and 3D object detection which are arguably more relevant in automotive applications.

All Grantees

Chalmers University of Technology

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