Improving the Learning of Self-driving Vehicles Based on Real Driving Behavior using Deep Neural Network Techniques

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes needs to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions, and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the learning of self-driving vehicles based on real driving behavior using deep neural network techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver during driving is recorded in different situations, and by converting the real driver’s driving video to images and transferring the data to an Excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. This study focuses on designing a convolutional network using behavioral cloning and motion planning of autonomous vehicle using a deep learning framework. Neural networks are effective systems for finding relationships between data, modeling, and predict new data or classify data. As a result Neural networks with input real data predict steer angle and speed for autonomous driving. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. The results confirmed that our scheme is capable of exhibiting high prediction accuracy (exceeding 92.93%). In addition, our proposed scheme has high speed (more than 64.41%), low FPR (less than 6.89%), and low FNR (less than 3.95%), in comparison with the other approaches currently being employed. By comparing other methods which were conducted on the simulator’s data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.