TITLE:
In-Vehicle Stereo Vision Systems with Improved Ant Colony Optimization Based Lane Detection: A Solution to Accidents Involving Large Goods Vehicles Due to Blind Spots
AUTHORS:
Ibrahim Adamu Umar, Shengbo Hu, Hongqiu Luo
KEYWORDS:
Large Good Vehicles, Blind Spot Detection, Lane Detection, Ant Colony Optimization
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.12 No.3,
March
22,
2022
ABSTRACT: This paper presents an in-vehicle stereo vision system as a solution to
accidents involving large good vehicle due to blind spots using Nigeria as a
case study. In this paper, a stereo-vision system was attached to the front of
Large Good Vehicles (LGVs) with a view to presenting live feeds of vehicles
close to the LGV vehicles and their distance away. The captured road images
using the stereo vision system were optimized for effectiveness and optimal
vehicle maneuvering using a modified metaheuristics algorithm called the
simulated annealing Ant Colony Optimization (saACO) algorithm. The concept of
simulated annealing is strategies used to automatically select the control
parameters of the ACO algorithm. This helps to stabilize the performance of the
ACO algorithm irrespective of the quality of the lane images captured in the
in-vehicle vision system. The system is capable of notifying drivers through
lane detection techniques of blind spots. This technique enables the driver to
be more aware of what surrounds the vehicle and make decisions early. In order
to test the system, the stereo-vision device was mounted on a Large good
vehicle, driven in Zaria (a city in Kaduna state in Nigeria), and data were in
the record. Out of 180 events, 42.22% of potential accident events were caused
by Passenger Cars, while 27.22%, 18.33% and 12.22% were caused by two-wheelers,
Large Good Vehicles and road users, respectively. In the same vein, the
in-vehicle lane detection system shows a good performance of the saACO-based
lane detection system and gives a better performance in comparison with the
standard ACO method.