Abstract
For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle.
Highlights
The vehicle has been a most widely used form of transportation in people’s lives over the last few decades, and various studies were proposed for road safety
We can assume that every car will have similar behavior while entering the road segment, and be able to use the information of previous vehicles that have passed through the road segment to estimate the future position of a vehicle, which is currently on that segment
Since our algorithm can predict the future position of a vehicle by finding the most proper model from the database where the data is collected from a particular road segment, such as the highway or intersection, it can be applied to every possible case
Summary
The vehicle has been a most widely used form of transportation in people’s lives over the last few decades, and various studies were proposed for road safety. In [1], the authors proposed a form of collision-avoidance architecture based on computer vision, machine learning, vehicle dynamics, and the predictive algorithm. The authors designed a linear algorithm based on the output of an existing real-time object detection project, YOLOv3 [15,16], to predict the future position of a vehicle. Note that their algorithm can cooperate with different real-time object detection projects, YOLOv3 being one of the most feasible methods.
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