Skip to content

bqcao/ChauffeurNet

 
 

Repository files navigation

ChauffeurNet

ChauffeurNet : Learning to Drive by Imitating the Best and Synthesizing the Worst. Reproduction the result according to this paper[https://arxiv.org/pdf/1812.03079.pdf]. I just implement it on the basis of my comprehension,because the paper didn't introduce the neural network in every detail. The model is implemented by Keras with Tensorflow backend.

Roadmap: 1.Model and train and prediction with mocked data.[done] 2.Data pipeline for real data. 3.Train it in real world data. 4.Other approachs in paper. 5.Test it in simulation. I want the model can be used in different simulation environment. Welcome other contributors to integrate different open source or private simulators. I will combine my company's simulator and some simple simulators first. 6.Test it in Real world on china's urban road.

Model options: 1.use conv layers like U-Net(Conv+Upsampling/Deconv) [done] 2.Conv + Full Connect like artari-net 3.Fully Conv 4.Fully Conv + GRU Links: https://github.com/Iftimie/ChauffeurNet

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%