Recently the research field of autonomous self-driving vehicles is one of the popular. The motion planning technology is essential for autonomous driving vehicles because it mitigates prevailing on-road obstacles. Unlike a modern rule-based method, this study applies an end-to-end, which is deep learning. This work aims to execute a motion planning method based on deep learning curves for the effective steering of autonomous driving vehicles. The main purpose of this study is to train a convolutional neural network to drive an autonomous vehicle in the simulator. Therefore, model training and simulation are conducted using the UDACITY platform. The simulator has two ways one is the training and the second one is the autonomous way. The autonomous way has two tracks track_1 is “easy” means not too many turns and track_2 has many turns as compare to track_1. In our study, we used track_1 for autonomous driving in the simulator. The training way gives the selection to record the dataset its control through the keyboard in the simulator.

The dataset contains 8036 images with four attributes. The capture raw images and steering angle data used in this method. Images were sequentially fed into the convolutional neural network to predict the driving factors for making end planning decisions and execution of autonomous motion of vehicles. The loss value of the proposed model is 0.0537 as compared to the other two exists once 0.0708 and 0.1056. The experiment analyses show that the proposed model generates fruitful and accurate graphical motion planning results for autonomous driving vehicles.

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