Preprint Article Version 2 This version is not peer-reviewed

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

Version 1 : Received: 23 January 2020 / Approved: 24 January 2020 / Online: 24 January 2020 (14:29:39 CET)
Version 2 : Received: 30 January 2020 / Approved: 31 January 2020 / Online: 31 January 2020 (11:45:37 CET)

How to cite: Zaghari, N.; Fathy, M.; Jameii, S.M.; Sabokrou, M.; Shahverdy, M. Improving the Learning of Self-driving Vehicles Based on Real Driving Behavior using Deep Neural Network Techniques. Preprints 2020, 2020010283 (doi: 10.20944/preprints202001.0283.v2). Zaghari, N.; Fathy, M.; Jameii, S.M.; Sabokrou, M.; Shahverdy, M. Improving the Learning of Self-driving Vehicles Based on Real Driving Behavior using Deep Neural Network Techniques. Preprints 2020, 2020010283 (doi: 10.20944/preprints202001.0283.v2).

Abstract

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 need 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 is 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. 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. 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.

Subject Areas

Autonomous vehicle; Self-driving; Real Driving Behavior; Deep Neural Network; LSV-DNN

Comments (1)

Comment 1
Received: 31 January 2020
Commenter: Nayereh Zaghari
Commenter's Conflict of Interests: Author
Comment: Hello dear The first author's family had mistakenly added the letter h. We also made some changes to the text and sources. Thanks
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