Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

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.

Reliability Analysis of the Functional Capabilities of an Autonomous Vehicle

“…This is impacted by the functional capabilities of an AV, which mostly extend to questions related to its reliability. Grigorescu et al (2020) and Chy et al (2021) stated that the capabilities of Autonomous Vehicles (AVs) have over the years transformed and improved how scenarios along a driving route should be predicted, including reactions to complex, unknown, or unforeseen situations. Automotive industries involved with the planning, design, and manufacture of AVs, members of academia, researchers, and students in the fields of robotics, smart systems, reliability engineering, and government agencies, amongst others, are the main targets of this research.…”

“…According to Grigorescu et al (2020) and Chy et al (2021), AV research has over the years grown, especially in respect of how they predict situations and how they should react to complex and unknown situations or unforeseen situations. The improvements recorded were possible due to the method called deep learning or smart deep learning and the advancements of Artificial Intelligence (AI) technology and its applications.…”

Privacy and Personal Data Protection in Self-Driving Car: Suggestion for Legal Regulation in Serbia

“…High-quality and fast transportation of people and delivery of goods represent one of the pressing and key problems of modern cities and their inhabitants. To solve this problem, projects of so called self-driving cars (SDCs) are offered (Chy et al, 2022). The definition of the SDC is given by the International Society of Automotive Engineers (SAE) in the standard SAE J3016 (SAE, 2014) which is two times improved and modified.…”