湖南江華縣第一中學(xué) 何高倫

為了安全起見(jiàn)，自動(dòng)駕駛汽車必須準(zhǔn)確跟蹤行人、非機(jī)動(dòng)車和周圍其他車輛的運(yùn)動(dòng)軌跡。 一般而言，可用于訓(xùn)練跟蹤系統(tǒng)的道路和交通數(shù)據(jù)越多，結(jié)果越好。

題材體裁 文章詞數(shù) 建議用時(shí)自動(dòng)駕駛汽車 說(shuō)明文約355 7分鐘

難詞探意

1.autonomous/???t?n?m?s/adj. 自治的；自主的；自發(fā)的

2. navigate /?n?v?ɡe?t/ v. 導(dǎo)航；航行

3. laborious /l??b??ri?s/ adj. 辛苦的；耗時(shí)費(fèi)力的

4. reverse /r??vз?s/ v. 使反轉(zhuǎn)

Generally speaking, the more road and traffic data available for training tracking systems, the better the results will be. Researchers have found a way to unlock a mountain of autonomous driving data for this purpose.“Our method is much more precise than previous methods because we can train on much larger datasets,” said Himangi Mittal, a researcher in CMU's Robotics Institute.

Most autonomous vehicles navigate primarily based on a sensor called lidar, a laser device that generates (生成) 3D information about the world surrounding the car. This 3D information isn't images,but a cloud of points. One way the vehicle makes sense of this data is using a technique known as scene flow. This involves calculating the speed of each 3D point.

In the past, state-of-the-art methods for training such a system have required the use of labeled datasets, which is laborious and expensive. Now, researchers take a different approach, using unlabeled data to perform scene flow training, which is relatively easy to generate.

The key to their approach was to develop a way for the system to detect its own errors in scene flow. At each instant, the system tries to predict where each 3D point is going and how fast it's moving. In the next instant, it measures the distance between the point's predicted location and the actual location of the point near that predicted location. This distance forms one type of error to be minimized. The system then reverses the process, starting with the predicted point location and working backward to map back to where the point originated. At this point, it measures the distance between the predicted position and the actual origination point, and the resulting distance forms the second type of error. The system then works to correct those errors.

The researchers calculated that scene flow accuracy using a training set of data was only 25%.When the data was adjusted with a small amount of real-world labeled data, the accuracy increased to 31%. When they added a large amount of unlabeled data to train the system using their approach,scene flow accuracy jumped to 46%.

Reading Check

1. What does the underlined word “precise” mean in paragraph 1?

A. Attractive. B. Complex. C. Exact. D. Common.

2. What advantage does unlabeled data have over labeled one?

A. It is easy to generate. B. It almost has no errors.

C. It can cover more objects. D. It can gather information quickly.

3. What's the most important factor about using unlabeled data?

A. Measuring the exact distance.

B. Predicting the speed of 3D points.

C. Checking the errors by the system.

D. Locating the position of objections.

4. Where is the text probably taken from?

A. A personal diary. B. A fashion newspaper.

C. An instruction book. D. A scientific magazine.

Language Study

Sentence for writing

Generally speaking, the more road and traffic data available for training tracking systems, the better the results will be.

【信息提取】“the +比較級(jí)..., the +比較級(jí)...”為固定結(jié)構(gòu)，意為“越……，越……”。

【句式仿寫(xiě)】你練習(xí)說(shuō)英語(yǔ)的次數(shù)越多，你的英語(yǔ)口語(yǔ)就會(huì)越好。