Paper Info
Title
SIIoT: A Shortest Path Estimation and Obstacle Avoidance System For Autonomous Cars
Abstract
Autonomous cars are the next-generation connected cars and are receiving increased attention from both the academia and industry. Among several open research directions in such cars, two most important are the shortest path estimation and obstacle avoidance system. This paper presents a Swarm Intelligence (SI), Cloudlet and IoT based approach to address both the challenges. We present an Ant Colony Optimization algorithm for the shortest route estimation and vector field based collision avoidance system for future autonomous cars. The novel aspect of this work is in integrating the mentioned mechanisms into a Cloudlet based IoT architecture for the autonomous cars. We have performed extensive simulations of the presented approaches, described the results and performance analysis validating the approaches.
Year
DOI
Venue
2018
10.1109/GIOTS.2018.8534532
2018 Global Internet of Things Summit (GIoTS)
Keywords
Field
DocType
Ant-Colony Optimization,Autonomous Cars,Internet of Things,Multi-Agent Obstacle Avoidance,Swarm Intelligence,Traveling Salesman Problem
Open research,Ant colony optimization algorithms,Obstacle avoidance,Cloudlet,Shortest path problem,Computer science,Swarm intelligence,Computer network,Real-time computing,Collision avoidance system,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-6452-0
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Tanmay Chakraborty100.68
Soumya Kanti Datta224929.23