Effects of lidar and radar resolution on DNN-based vehicle detection
- 27 September 2021
- journal article
- research article
- Published by Optica Publishing Group in Journal of the Optical Society of America A
- Vol. 38 (10), B29-B36
- https://doi.org/10.1364/josaa.431582
Abstract
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.This publication has 17 references indexed in Scilit:
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