Citation

Chy MKA, Masum AKM, Sayeed KAM, Uddin MZ. Sensors (Basel) 2022; 22(1): e126.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s22010126

PMID

35009669

Abstract

The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system's IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system's infrastructure is far too low-cost and easy to install.

Language: en

Keywords

Automobiles; Bangladesh; Computers; Humans; *Deep Learning; *Automobile Driving; computer vision; convolution neural network; Internet of Things; Raspberry Pi 3; self-driving car; smart product delivery