Abstract
It is not practical to assume that all vehicles must have the communication ability for the purpose of realizing the IoV-based collision avoidance architecture. Hence, we need some extra designs to make up for the deficiency. In the literature, some researches use a camera mounted on the infrastructure at an intersection to implement the collision detection. Particularly, they utilize the real-time object detection and dynamic prediction to predict the future positions of vehicles for the collision avoidance. In this paper, we propose a new method to predict the future positions of vehicles as well, which is based on a well-known real-time object detection project, YOLOv4. By incorporating the concept of vehicle dynamics with our machine learning architecture, our design can estimate the further future vehicular position more accurately and stably. Lastly, the experimental results show the performance of our algorithm for predicting the future vehicular positions and realizing the collision avoidance architecture.
Highlights
During the last decades, vehicles have been the most used transportation for people
Nowadays due to the increasing use of electronics in the automobile industry, many active/advanced safety techniques have been developed for various scenarios, e.g., Lane Departure Warning system (LDW) [3], Forward Collision Warning system (FCW) [4], Blind Spot Warning system (BSW) [5], Parking Assistant System (PAS) [6]–[8], and navigation [9], [10]
WORKS In this paper, we propose novel method to predict the future vehicular position based on a well-known real-time object detection project, YOLOv4
Summary
Vehicles have been the most used transportation for people. various researches are proposed for road safety, e.g., passive and active safety technologies. In [1], [2], the authors have proposed a novel collision avoidance architecture, which is based on computer vision with deep learning, vehicle dynamics, and corresponding predictive algorithms. In [1], the authors design a linear algorithm based on the output of an existing real-time object detection project, YOLOv3 [16], [17], to predict the future position of a vehicle.
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