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.

中文翻译：

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激光雷达和雷达分辨率对基于 DNN 的车辆检测的影响

车辆检测在自动驾驶中起着至关重要的作用，其中两个核心传感模式是激光雷达和雷达。尽管已经提出了许多基于深度神经网络 (DNN) 的方法来解决此任务，但仍然缺少对数据对这些方法的影响的系统和方法论检查。在这项工作中，我们研究了分辨率对激光雷达和雷达传感器车辆检测性能的影响。我们提出了子采样方法，可以提高基于 DNN 的解决方案的性能和效率，并提供一种替代传统传感器设计权衡的方法。