An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware

Autonomous vehicles rely on sophisticated hardware and software technologies for acquiring holistic awareness of their immediate surroundings. Deep learning methods have effectively equipped modern self-driving cars with high levels of such awareness. However, their application requires high-end computational hardware, which makes utilization infeasible for the legacy vehicles that constitute most of today’s automotive industry. Hence, it becomes inherently challenging to achieve high performance while at the same time maintaining adequate computational complexity. In this paper, a monocular vision and scalar sensor-based model car is designed and implemented to accomplish autonomous driving on a specified track by employing a lightweight deep learning model. It can identify various traffic signs based on a vision sensor as well as avoid obstacles by using an ultrasonic sensor. The developed car utilizes a single Raspberry Pi as its computational unit. In addition, our work investigates the behavior of economical hardware used to deploy deep learning models. In particular, we herein propose a novel, computationally efficient, and cost-effective approach. The designed system can serve as a platform to facilitate the development of economical technologies for autonomous vehicles that can be used as part of intelligent transportation or advanced driver assistance systems. The experimental results indicate that this model can achieve real-time response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.

I. Introduction

A vehicle capable of perceiving its surrounding environment and driving by itself safely without human intervention is known as an autonomous vehicle (also referred to as self-driving, driverless, or robotic vehicle) [1], [2]. Autonomous cars are constantly making headline news over the last few years. Different manufacturing companies and startups are targeting to develop safer, more responsive, and reliable cars for consumers of the next generation [3]. There is a growing competition among the biggest car manufacturing companies, each making their own version of a self-driving car.