Autonomous driving has recently gained lots of attention due to its disruptive potential and impact on the global economy; however, these high expectations are hindered by strict safety requirements for redundant sensing modalities that are each able to independently perform complex tasks to ensure reliable operation. At the core of an autonomous driving algorithmic stack is road segmentation, which is the basis for numerous planning and decision-making algorithms. Radar-based methods fail in many driving scenarios, mainly as various common road delimiters barely reflect radar signals, coupled with a lack of analytical models for road delimiters and the inherit limitations in radar angular resolution. Our approach is based on radar data in the form of a two-dimensional complex range-Doppler array as input into a deep neural network (DNN) that is trained to semantically segment the drivable area using weak supervision from a camera. Furthermore, guided back propagation was utilized to analyse radar data and design a novel perception filter. Our approach creates the ability to perform road segmentation in common driving scenarios based solely on radar data and we propose to utilize this method as an enabler for redundant sensing modalities for autonomous driving.

自动驾驶最近因其颠覆性潜力和对全球经济的影响而备受关注；然而，这些高期望受到对冗余传感模式的严格安全要求的阻碍，这些模式每一个都能够独立执行复杂的任务以确保可靠运行。自动驾驶算法堆栈的核心是道路分割，这是众多规划和决策算法的基础。基于雷达的方法在许多驾驶场景中都失败了，主要是因为各种常见的道路划界线几乎不能反射雷达信号，加上道路划界线缺乏分析模型以及雷达角分辨率的固有限制。我们的方法基于二维复杂距离多普勒阵列形式的雷达数据，作为深度神经网络 (DNN) 的输入，该深度神经网络经过训练，可以使用摄像头的弱监督对可驾驶区域进行语义分割。此外，利用引导反向传播来分析雷达数据并设计一种新颖的感知滤波器。我们的方法创建了仅基于雷达数据在常见驾驶场景中执行道路分割的能力，我们建议利用这种方法作为自动驾驶冗余传感模式的推动者。