Learning from Maps: Visual Common Sense for Autonomous Driving
Learning from Maps: Visual Common Sense for Autonomous Driving
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发表时间：
2016-11
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通讯作者：
Ari Seff;Jianxiong Xiao
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作者：
Ari Seff;Jianxiong Xiao
今天的自动驾驶汽车广泛依赖高清3D地图来导航环境。虽然这种方法在这些地图完全是最新的情况下效果很好，但安全的自动驾驶车辆必须能够通过基于真实的时间传感器的系统来证实地图的信息。我们在这项工作中的目标是开发一个模型，道路布局推理给定的图像从车载摄像头，而不依赖于高清地图。然而，没有足够的数据集来训练这样的模型。在这里，我们利用标准导航地图和相应的街景图像的可用性，为这个复杂的场景理解问题构建一个自动标记的大规模数据集。通过将来自导航地图的道路矢量和元数据与谷歌街景图像进行匹配，我们可以分配地面实况道路布局属性（例如，到十字路口的距离，单向街道与双向街道）到图像。然后，我们训练深度卷积网络来预测给定单个单目RGB图像的这些道路布局属性。实验评估表明，我们的模型学习正确推断道路属性，只使用车载摄像头捕获的道路作为输入。此外，我们的研究结果表明，这种方法可能适用于推荐基础设施安全改进的新应用（例如，建议用于街道的替代速度限制）。
Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment. While this approach works well when these maps are completely up-to-date, safe autonomous vehicles must be able to corroborate the map's information via a real time sensor-based system. Our goal in this work is to develop a model for road layout inference given imagery from on-board cameras, without any reliance on high-definition maps. However, no sufficient dataset for training such a model exists. Here, we leverage the availability of standard navigation maps and corresponding street view images to construct an automatically labeled, large-scale dataset for this complex scene understanding problem. By matching road vectors and metadata from navigation maps with Google Street View images, we can assign ground truth road layout attributes (e.g., distance to an intersection, one-way vs. two-way street) to the images. We then train deep convolutional networks to predict these road layout attributes given a single monocular RGB image. Experimental evaluation demonstrates that our model learns to correctly infer the road attributes using only panoramas captured by car-mounted cameras as input. Additionally, our results indicate that this method may be suitable to the novel application of recommending safety improvements to infrastructure (e.g., suggesting an alternative speed limit for a street).