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.

© 2021 Optical Society of America

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References

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Data Availability

Publicly available data used in this study are available at [17]. Publicly unavailable data used for this study are available from the corresponding author upon reasonable request.

17. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: the KITTI dataset,” Int. J. Rob. Res. 32, 1231–1237 (2013). [CrossRef]  

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