Road detection is a vital task for autonomous vehicles, as it has a direct link to passengers’ safety. Given its importance, researchers aimed to improve its accuracy and robustness. We look at the task from a holistic point of view, where we aim to balance computation and accuracy. A multimodal road detection pipeline is proposed, which fuses the camera image with the preprocessed LIDAR input. First, the LIDAR input is preprocessed using three-dimensional models inspired from computer graphics to generate image-like representations. Then, the preprocessed LIDAR input is combined with the camera image using a fusion module named inputs cross-fusion module, to reduce the computation amount required by other fusion strategies. To prevent the accuracy loss caused by the computation gain, we introduce the surface normal information to add distinctiveness. Furthermore, we propose a cost/benefit metric to evaluate the trade-off between computation cost and accuracy of road detection approaches. Several tests were conducted using the KITTI road detection benchmark based on deep convolutional neural networks, the obtained results were considered very satisfactory. In particular, the robustness of the proposed approach resulted in accuracies higher than 95% on different road types, comparable to those of the state-of-the-art techniques. In addition to marginally reducing the inference time of the used DCNN on images with a resolution of 1248 × 352 pixels to 130 ms using an NVIDIA GTX-1080TI.

中文翻译：

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走向自动驾驶汽车的实时道路检测

道路检测对于自动驾驶汽车至关重要，因为它与乘客的安全有直接联系。鉴于其重要性，研究人员旨在提高其准确性和鲁棒性。我们从整体的角度来看待任务，我们的目标是平衡计算和准确性。提出了一种多模式道路检测管线，该管线将摄像机图像与预处理的LIDAR输入融合。首先，使用受计算机图形学启发的三维模型对LIDAR输入进行预处理，以生成类似图像的表示形式。然后，使用名为输入交叉融合模块的融合模块将预处理的LIDAR输入与相机图像进行组合，以减少其他融合策略所需的计算量。为了防止由于计算增益导致的精度损失，我们介绍表面法线信息以增加独特性。此外，我们提出了一种成本/收益指标来评估计算成本与道路检测方法的准确性之间的权衡。使用基于深度卷积神经网络的KITTI道路检测基准进行了几次测试，认为获得的结果非常令人满意。特别是，所提出方法的鲁棒性导致不同道路类型的准确度高于95％，可与最新技术相媲美。除了使用NVIDIA GTX-1080TI将使用的DCNN在1248×352像素分辨率的图像上的推理时间减少至130 ms之外，还可以将其最小化。使用基于深度卷积神经网络的KITTI道路检测基准进行了几次测试，得出的结果被认为是非常令人满意的。特别是，所提出方法的鲁棒性导致不同道路类型的准确度高于95％，可与最新技术相媲美。除了使用NVIDIA GTX-1080TI将使用的DCNN在1248×352像素分辨率的图像上的推理时间减少至130 ms之外，还可以将其最小化。使用基于深度卷积神经网络的KITTI道路检测基准进行了几次测试，得出的结果被认为是非常令人满意的。特别是，所提出方法的鲁棒性导致不同道路类型的准确度高于95％，可与最新技术相媲美。除了使用NVIDIA GTX-1080TI将使用的DCNN在1248×352像素分辨率的图像上的推理时间减少至130 ms之外，还可以将其最小化。