Baidu’s Open Source Self-Driving Platform Apollo in Detail

Baidu’s Open Source Self-Driving Platform Apollo in Detail

The first presentation outside China of Apollo , the open source platform for the development of self-driving cars by the Chinese internet behemoth Baidu, took place yesterday in Baidu’s Silicon Valley-offices in Sunnyvale.

In front of several hundred participants Baidu talked about the motivation, functionality, architecture, partnerships and showcased several projects with partners. The official launch of Apollo 1.0 was July 5th 2017 at the Baidu Create AI conference . The source code, which can be found on GitHub , was downloaded 6,000 times and has seen 1,300 forks.

Baidu aims at democratizing autonomous driving, not monopolizing it. According to Baidu president Ya-Qig Zhang the company believes in the open source approach to hand over control to developers and accelerate the development of this technology.

It’s obvious that Baidu aims at closing the gap to companies such as Google and boost its own cloud services. Interesting to see is that Google used a similar tactics ten years ago against Apple, when it open source its mobile OS Android. Now Baidu finds itself in this role against Google.

The Apollo-framework is composed of four layers, starting with the reference vehicle platform, on which the reference hardware layer resides. On top comes the open source platform and the cloud service layer. The roadmap for version 1.5 and higher is pretty aggressive. July 2017: closed venue driving Control

Reference Hardware Reference Vehicle Data Platform 1.0 Labelled 3D obstacle data Road Hackers data HD map technology cooperation September 2017: fixed lane driving Obstacle Perception (Velodyne 64 Lidar, CUDA, CuDNN, Caffe, NVIDIA GPU 10Hz) Planning (Multi-dimensional inputs, traffic law plug-in, DP/QP path and speed optimizer) HD Map Simulation Service End-to-End Learning (Lateral: curvature; longitudinal: velocity and acceleration; FCNN, Convolutional LSTM) Reference Hardware-Lidar Artificial road scene data December 2017: simple urban road driving Advanced Perception Security […]

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