Signal Processing and Machine Learning Advances in Automotive Radars

Theme

The ongoing automation of driving functions in cars results in the evolution of advanced driver assistance systems (ADAS) into ones capable of highly/fully automated driving in future. Radar has emerged as a core technology for sensing and perception of a vehicle’s surroundings to facilitate diverse driver assistance functions. With growing levels of automation, there is a need for high-performance radar sensing, especially with respect to angular resolution. To achieve this, advanced waveform designs and signal processing algorithms such as digital waveforms, MIMO radar, high-resolution DoA and sparse radar signal processing, and cognitive, adaptive radar approaches are needed. Furthermore, machine learning (ML)-powered radar systems are still in an early stage of development. Advances are required to design reliable, robust and explainable radar perception for real-time, safety-critical driving applications. The proposed workshop is aimed at providing a spotlight on the diverse signal processing and ML challenges in advancing automotive radar technology.


The workshop will be a mix of peer-reviewed technical papers and invited speakers from academia/industry covering a diversity of topics in automotive radar systems.

Topics

We are seeking high-quality paper submissions covering the following topics of interest:


Submission

Key dates:

Submitted workshop papers should abide by the IEEE ICASSP 2024 paper style, format, and length. Accepted workshop papers will be published at the IEEE Xplore Digital Library. 

Note that, similarly to the main conference, the Satellite Workshops are expected to foster the return to an in-person attendance experience. Accordingly, there must be an author of each accepted workshop paper presenting it in-person.

Questions?

Contact splaricassp@gmail.com to get more information on the satellite workshop