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Lightning Talk- Situation-Aware Tranmit Beamforming for Automotive radar

DOI:
10.60864/c154-aw88
Citation Author(s):
Edoardo Focante, Nitin J. Myers, Geethu Joseph, Ashish Pandharipande
Submitted by:
Edoardo Focante
Last updated:
6 June 2024 - 10:27am
Document Type:
Presentation Slides
Document Year:
2024
Event:
Presenters:
Edoardo Focante
Paper Code:
4870
 

Millimeter-wave radar is a common sensor modality used in automotive driving for target detection and perception. These radars can benefit from side information on the environment being sensed, such as lane topologies or data from other sensors. Existing radars do not leverage this information to adapt waveforms or perform prior-aware inference. In this paper, we model the side information as an occupancy map and design transmit beamformers that are customized to the map. Our method maximizes the probability of detection in regions with a higher uncertainty on the presence of a target. Simulation results on the nuScenes dataset show that the designed beamformer achieves substantially higher detection rates than a conventional omnidirectional beamformer for the same transmitted power.

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