Use MacBook Pro or Raspberry to build automatic driving remote control bank based on Tensorflow

You may think that you don't build what you need to drive the car, but you are wrong. The error you commit is to assume that you have to control a 2-ton car. Instead, you can create another thinking without danger and relatively light budget. [OTAVIO] and [WILL] The automatic driving vehicle is completed using radio remote control (RC). [OTAVIO] A MacBook Pro is installed on a remote control to perform heavy operations and referred to as Carputer. The computer reads Hall effect sensor data from the motor to establish travel distance (this can be used to calculate the speed) and observe the stream from the network camera located on the chassis. Bet these two sources into neural networks using Tensorflow. You can train your system several times by manual driving vehicles, and then let it drive themselves. In the following video interview, you can look at this car, [OTAVIO] gives how the system works, because we have seen a few game playback, including SparkFun 2016 automatic driving car game. Carputer is definitely one-time, not the most suitable of the DIY of beginners. This is the land of [Will], he is a member of the Donkey Car team. Donkey Car is a standard platform for automatic driving a car. It is a combination of approximately $ 200 parts and some 3D printed chassis components. It can start and run after approximately two hours of assembly time, and its brain is a Raspberry 30, we Know that it has run Tensorflow. The user guide provided by the Donkey Car team is a very good start. They don't sell their suits - it is still a hacker project, we like it. If you encounter any obstacles during the construction process, the team will have a SLACK channel and is happy to help you complete the build. The only thing is another important question is to find the track of testing your car. Be Copyright statement: HackADAY authorization, Circuit City original translation video, declined! If you also like open source projects, and want to share your own open source work, contribute your strength for the open source world! Video content delivery channel: [email protected]

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