Effects of lidar and radar resolution on DNN-based vehicle detection

Itai Orr, Harel Damari, Meir Halachmi, Mark Raifel, Kfir Twizer, Moshik Cohen, Zeev Zalevsky

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Vehicle detection plays a critical role in autonomous driving, where two central sensing modalities are lidar and radar. Although many deep neural network (DNN)-based methods have been proposed to solve this task, a systematic and methodological examination on the influence of the data on those methods is still missing. In this work, we examine the effects of resolution on the performance of vehicle detection for both lidar and radar sensors. We propose subsampling methods that can improve the performance and efficiency of DNN-based solutions and offer an alternative approach to traditional sensor-design trade-offs.

Original languageEnglish
Pages (from-to)B29-B36
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number10
StatePublished - 1 Oct 2021

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