Scrapy in action: Baidu drives smart car crawler application case sharing

Scrapy in action: Baidu-driven smart car crawler application case sharing

With the continuous development of artificial intelligence technology, smart car technology is becoming more and more mature, and the future is promising. In the development of smart cars, a large amount of data collection and analysis is inevitable. Therefore, crawler technology is crucial. This article will introduce a crawler application case implemented through the Scrapy framework to show you how to use crawler technology to obtain smart car-related data.

1. Case Background

Baidu Drive Smart Car is an autonomous driving solution launched by Baidu. It realizes autonomous driving by carrying Baidu Apollo intelligent driving platform related products, such as high-precision maps, positioning, perception, decision-making and control. To gain a deeper understanding of Baidu-driven smart cars, a large amount of relevant data needs to be collected, such as map data, trajectory data, sensor data, etc. The acquisition of these data can be achieved through crawler technology.

2. Crawler framework selection

Scrapy is an open source framework based on Python that is specially used for data crawling. It is very suitable for crawling large-scale and efficient data, and has strong flexibility and scalability. Therefore, we chose the Scrapy framework to implement this case.

3. Practical Case

This practical case takes crawling Baidu-driven smart car map data as an example. First, we need to analyze the target website and confirm the data paths and rules that need to be crawled. Through analysis, we found that the data path that needs to be crawled is: http://bigfile.baidu.com/drive/car/map/{ID}.zip, where ID is an integer from 1 to 70. Therefore, we need to write a Scrapy crawler program to traverse the entire ID range and download the map zip file corresponding to each ID.

The following is the main code of the program:

import scrapy class MapSpider(scrapy.Spider): name = "map" allowed_domains = ["bigfile.baidu.com"] start_urls = ["http://bigfile.baidu.com/drive/car/map/" + str(i) + ".zip" for i in range(1, 71)] def parse(self, response): url = response.url yield scrapy.Request(url, callback=self.save_file) def save_file(self, response): filename = response.url.split("/")[-1] with open(filename, "wb") as f: f.write(response.body)

Code explanation:

1. MapSpider is a class inherited from scrapy.Spider, which defines the name and target of the crawler Website and starting URL.
2. start_urls is the starting point of the program and defines the data path that needs to be crawled. Here a list comprehension is used to generate all the URLs that need to be accessed. Note that Baidu drives smart car map data only has 70 IDs, so range(1,71) is the range of IDs.
3. The parse function is a general function for processing response. In this program, use it to send a download request for the map corresponding to each ID, and call it back to the save_file function.
4. save_file function is the focus of this program. It handles the download of each map zip file, storing them to local disk.

4. Program execution

Before running this program, you need to install the requests library of Scrapy and Python. After the installation is complete, enter the following command in the command line:

scrapy runspider map_spider.py

The program will automatically traverse the map data of all IDs and download it to the local disk.

5. Summary

This article introduces the application case of Baidu-driven smart car map data crawler implemented through the Scrapy framework. Through this program, we can quickly obtain a large amount of map data, which provides strong support for the research and development of smart car-related technologies. Crawler technology has great advantages in data acquisition. I hope this article can be helpful to readers.

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