
English: 
In DRIVE Labs episode five we showed you surround
camera object tracking.
Today we're going to show you surround radar
obstacle detection and tracking, and how we
combine the camera and radar perception results
using sensor fusion.
Here we're looking at a three-dimensional
top-down view of results generated by an eight
radar surround perception setup.
The ego car is shown in white at the center
and the concentric rings around it denote
10-meter distance intervals from the ego car's
rear axle.
Diamonds denote objects tracked by the surround
radar with moving objects shown in green and
stationary ones in purple.
We see that the track IDs are stable even
as objects move around the ego car which enables
accurate velocity measurements.
And here we see the surround camera view of
the same scene that we just saw in 3D radar
view but with camera radar fusion also enabled.
The small white squares denote the radar tracks
with track histories visualized as orange
traces.
When the camera object detections shown by
the bounding boxes are fused with the radar

Chinese: 
在 NVIDIA 自动驾驶实验室第五集中
我们为你展示了环绕摄像头物体追踪
今天，我们将展示环绕雷达障碍物检测和追踪
以及如何利用传感器融合来整合摄像头和雷达的感知结果
我们此时看到的是一个三维俯视图
它展示了由八个雷达组成的环绕感知装置所生成的结果
测试车辆处于中心位置并显示为白色
它周围的同心环从后轴开始，以 10 米的间距向外扩展
菱形表示环绕雷达追踪的物体
其中运动的物体显示为绿色
静止的物体则显示为紫色
我们看到，即使物体在测试车辆周围移动，追踪 ID 依旧保持不变
这有利于准确测量运动速度
这是我们刚刚在 3D 雷达视图中所看同一场景的环绕摄像头视图
只不过还启用了摄像头雷达融合技术
白色小方块表示雷达航迹
航迹历史记录则由橙色进行直观展示
当边框显示的摄像头物体检测轨迹与雷达航迹融合时

English: 
tracks the small squares are visualized in
green.
With camera radar fusion the object position,
velocity, and acceleration measurements become
a more precise and reliable input into planning
and control functions.
In this clip, we see camera radar fusion in
side by side views.
In the radar view on the left, camera radar
fusion is shown by the association between
yellow cubes, camera objects, and green diamonds,
radar objects.
While in the camera view on the right, it
is shown by the association between green
bounding boxes and small green squares.
Camera radar sensor fusion brings improved
accuracy, diversity and redundancy to overall
surround obstacle perception, and is available
in the NVIDIA DRIVE Software 10.0 release.

Chinese: 
小方块便显示为绿色
借助摄像头雷达融合技术，物体位置、运动速度和加速度的测量结果将更为精确可靠
继而能为规划和控制功能提供更可信的输入
在此视频片段中，我们可以通过并排视图观察摄像头雷达融合效果
在左侧的雷达视图中
摄像头雷达融合通过黄色方块（摄像头中的物体）和绿色菱形（雷达检测的物体）之间的关联实现
而在右侧的摄像头视图中
它则由绿色边框和绿色小方块之间的关联实现
摄像头雷达传感器融合提高了障碍物整体环绕感知的准确性和多元化
同时还能减少冗余
该技术现已在 NVIDIA DRIVE 10.0 版软件中提供
