Visit Baidu AI
Brief Introduction of Cooperation
In the CES Asia 2017, Baidu units Great Wall Motor and NVIDIA to show the end-to-end automatic driving solution based on monocular camera + Baidu deep learning technology in Hover H7, and conducted a public test ride.
Technological Advantage

Rapid expansion

Enable to quickly achieve scale up (expansion). When multiple vehicles deploy this system, brains in the cloud can learn driving data of a large number of drivers at the same time, as well as get large amount of driving experience in short time, and then quickly become an "experienced driver" with driving experience far more than the human being.

Deep learning

Input of the system is the original sensor signal, which is transferred into horizontal and vertical control instructions, and then directly output through the deep neural network processing, that is, the vehicle's automatic driving is completed through the learned driving behaviors.

Low cost

Featured by low cost because of much imitation and practice, low cost of sensors and low system engineering complexity.

Cooperation Details
Details of cooperation case
Based on Deep Learning Technology: Apollo & Great Wall Motor End-to-End Cooperation

In the Baidu AI Developers Conference of CES Asia 2017, Baidu demonstrated the end-to-end autopilot solution based on deep learning technology. Automatic driving vehicles based on the solution can learn from human’s driving behaviors, to achieve functions like normal driving on straight roads and corners, automatic acceleration and deceleration, identification of traffic identifiers. "Blunt" and "suddenness" of automatic driving vehicles in the process of turning, braking and acceleration can be better avoided with the help of this system, to present comfortable ride experience.

In the CES Asia 2017, Baidu units Great Wall Motor and NVIDIA to show the end-to-end automatic driving solution based on monocular camera + Baidu deep learning technology in Hover H7, and conducted a public test ride.

Application Scenarios
Used for the line operation of parks and other places.
Used for scenarios, such as low-speed logistics and distribution.