Predict Vehicle collision moments before it happens in Carla!. CNN and LSTM hybrid architecture is used to understand a series of images.
GPL-3.0 License
基于树莓派的自动驾驶小车,利用树莓派和tensorflow实现小车在赛道的自动驾驶。(Self-driving car based on raspberry pi(tensorflow))
Udacity Self-Driving Car Engineer Nanodegree. Project: Road Semantic Segmentation
Build tensorflow keras model pipelines in a single line of code. Now with mlflow tracking. Create...
Adventure into using multi attention recurrent neural networks for time-series (city traffic) for...
DeepTraffic is a deep reinforcement learning competition, part of the MIT Deep Learning series.
This provides a sandbox simulator for training a self-driving car. This uses Unity for simulation...
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in ...
Deep Learning Autonomous Car based on Raspberry Pi, SunFounder PiCar-V Kit, TensorFlow, and Googl...
Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)
Automated Driving in NFS using CNN.
"MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the v...
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN...
An Image classifier to identify whether the given image is Batman or Superman using a CNN with hi...