
English: 
Hello everyone.
We hope that you are staying healthy, staying safe.
We are working from home
welcome to DRIVE Labs, the “Home Edition”
today we’re going to talk about using a deep neural network to understand the structure of an intersection
things like how many lane lines are there in each direction?
Where are the intersection entry and exit
lines?
the application for this is autonomous intersection handling
in both semi-urban and urban scenarios
The DNN is able to detect and classify different types of intersection structure features
including intersection entry line points for
the ego car, shown in red
intersection entry line points for other cars shown in yellow
and intersection exit line points for all cars, shown in green
The DNN also detects non-drivable lane lines, as shown in black.
We see that the perception is robust to both partial and full occlusions
and that the DNN is able to predict both painted and inferred intersection structure lines
We also note that these are all per-frame DNN detection results

Chinese: 
大家好，希望你们平安无恙
欢迎居家办公的各位来到“居家版”NVIDIA 自动驾驶实验室
今天，我们将讨论如何使用深度神经网络来理解交叉口的结构
例如，交叉口的每个方向各有多少条车道线？
交叉口的入口线和出口线分别在哪里？
在城市和半城市场景中，自主驾驶交叉口处理可以解决此类问题
深度神经网络 (DNN) 可检测不同类型的交叉口结构特征并对其进行分类
包括测试车辆的交叉口入口线标记点（显示为红色）
其他车辆的交叉口入口线标记点（显示为黄色）
和所有车辆的交叉口出口线标记点（显示为绿色）
DNN 还能检测非驾驶区域的车道线（显示为黑色）
从视频中我们可以看出，感知结果十分稳定，并未受车辆遮挡程度的影响
无论是针对绘制的还是推断得出的交叉口结构线，DNN 都能进行预测
我们还发现，这些都是 DNN 逐帧检测的结果

English: 
with no tracking or fusion of any kind applied
The ego car can determine where to stop for the intersection based on the closest intersection entry line
can decide how to exit the intersection using all of the intersection exit line information
So in this case, the DNN correctly predicts
that the ego car could exit the intersection
by either proceeding straight through, taking a left or right turn, or making a U-turn.
And we also note that this perception information can be used to extrapolate how many lanes
and which types of lanes the intersection has
These DNN perception results can be used in several different ways for autonomous intersection handling
They can be used to generate paths to navigate the intersection.
They can be used to create a map of intersection structure.
And they can also be combined with previously mapped results, where available
to create additional diversity and redundancy.

Chinese: 
并未使用任何类型的追踪或融合技术
测试车辆可根据最近的交叉口入口线决定停在哪个位置，以便进入交叉口
而且还可利用所有交叉口出口线信息确定离开交叉口的路线
在这种情形下，DNN 准确预测出
测试车辆可通过直行、左转或右转、掉头操作离开交叉口
我们还发现，感知信息可用于推断车道数量以及交叉口的车道类型
这些 DNN 感知结果可用于自动驾驶交叉口处理的多种应用场景
例如，用于生成交叉口导航路线
用于创建交叉口结构图
还可与之前绘制的结果（如有）结合使用
从而提供额外的多样性和冗余
