
Chinese: 
在今天的自动驾驶实验室中我们将讨论如何“预测未来”
具体来说，是利用循环神经网络，预测汽车和行人等动态障碍物
在驾驶场景中的未来位置和运动速度
在该画面当中，白色方框代表当前目标位置
黄色方框代表这些目标在大约半秒钟后的预测位置
后者是借助循环神经网络预测得出
为了便于观察，预测结果每半秒钟更新一次
我们可以看到，随着时间的推移
白色方框在 2D 图像中向黄色方框移动
这说明该神经网络正在准确预测目标的后续运动
这一信息可以帮助车辆根据需要来预测和调整其轨迹
现在该神经网络正在预测测试车辆和场景中目标之间的碰撞时间（TTC）
红色方框代表车辆正在向我们靠近并且拥有较短的TTC
也就是说需要紧急规避以免与其发生碰撞
而白色方框代表正在向前行驶
速度与我们基本一致的车辆

English: 
Today's DRIVE mission is about predicting
the future, specifically predicting the future
position and the velocity of dynamic obstacles
on the scene such as cars and pedestrians
using recurrent neural networks.
In this clip, the white boxes represent current
object positions, while the yellow boxes are
the recurrent neural network's predictions
on where these objects will be about half
a second in the future.
And to simplify visualization, predictions
are refreshed every half second.
We can see that as time passes, the white
boxes move towards the yellow ones in 2D image
space, which shows that the network is correctly
predicting future object motion.
And this information would help the car to
anticipate and adjust its trajectory as needed.
Here the network is predicting time to collision
information or TTC between the ego car and
objects on the scene.
The red boxes represent cars that are getting
closer to us and would have lower TTC or equivalently
higher urgency to avoid a collision, while
the white boxes represent cars that are moving
forward and about the same speed as us.

English: 
The green boxes represent those that are moving
further away.
And the number at the top of each box represents
the computed urgency, which mathematically,
is the inverse of the time to collision.
And here we see the same computation performed
for a scene with pedestrians.
And that was DRIVE mission 32 which we affectionately
refer to around here as project back to the
future.

Chinese: 
绿色方框代表已经驶远的车辆
每个方框上方的数字代表计算后得出的紧急程度
从数学上来说，就是碰撞时间的倒数计时
在这个场景中有很多行人
我们可以看到循环神经网络在进行相同的计算
以上就是编号32的自动驾驶任务
我们也称之为“回到未来”计划
