Real-Time End-to-End Self-Driving Car Navigation
Keywords:
Self-driving car, deep neural network, embedded systems, end-to-end learningAbstract
In this study, a deep neural network (DNN)-based vision-based navigation for autonomous vehicles is proposed. This novel DNN-based system obtains the data from a single camera to provide vehicle control outputs that modify both the steering wheel angle and the vehicle’s velocity. In addition, it plays a major role in safely navigating the vehicle in a road traffic environment. Numerous autonomous driving algorithms use end-to-end DNN, where camera data is fed into complex machine learning algorithms exclusively to estimate the steering angle value, but this research proposes a light-novel network model that controls both steering and speed values with much more simplicity. Various neural blocks are organized with the ultimate objective of producing control actions to achieve the aim of the research. Experimental modifications are made to parameters such as the number of convolutional layers, the model size, padding, stride, and the number of neurons in fully-connected layers to make the model simpler and lighter to execute during inference. Using an embedded system called Jetson Nano 2GB, the designed model was trained and tested using the images collected along two different paths. Our DNN-based autonomous driving system successfully predicts speed and steering values with a mean error of 1.58% and maintains performance, allowing for highly efficient autonomous driving. Furthermore, the suggested DNN network maintains performance, attaining autonomous driving success with comparable efficacy to the other autonomous driving models. The lightweight end-to-end architecture with superb performance is especially suited for autonomous driving.
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