Baidu Apollo Releases Massive Self-driving Dataset; Teams Up With Berkeley DeepDrive

Baidu Apollo Releases Massive Self-driving Dataset; Teams Up With Berkeley DeepDrive 2

This article was written by Synced.

 

Baidu this Thursday announced the release of ApolloScape, billed as the world’s largest open-source dataset for autonomous driving technology.

ApolloScape was released under Baidu’s autonomous driving platform Apollo, which Baidu hopes will become “the Android of the auto industry.” Apollo gives developers access to a complete set of service solutions and open-source codes and can enable for example a software engineer to convert a Lincoln MKZ into a self-driving vehicle in about 48 hours. ApolloScape’s open sourced data now provides developers a base for building self-driving vehicles.

Baidu Apollo Releases Massive Self-driving Dataset; Teams Up With Berkeley DeepDrive 3

The data volume of ApolloScape is 10 times greater than any other open-source autonomous driving dataset, including Kitti and CityScapes. This data can be utilized for perception, simulation scenes, road networks etc., as well as enabling autonomous driving vehicles to be trained in more complex environments, weather and traffic conditions. ApolloScape also defines 26 different semantic items — eg. cars, bicycles, pedestrians, buildings, streetlights, etc. — with pixel-by-pixel semantic segmentation technique.

The ApolloScape dataset will save researchers and developers a huge amount of time on real-world sensor data collection.

According to a Rand Corporation report, accumulating sufficient real road testing data to conclude a 20 percent advantage for autonomous vehicles over human drivers would require a fleet of 100 vehicles driving nonstop for 500 years.

Beyond data, ApolloScape will also facilitate advanced research on cutting-edge simulation technology aiming to create a simulation platform that aligns with real-world experience.

 

To read the rest of the article, click here.

 

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http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:910604