###### Abstract
As the IoV is an emerging research area under development, the subset of features required for developing a collision alarm system for vehicle collision detection in the IoV are not fully understood as research is still active. Feature selection has the advantage of isolating noisy, irrelevant or redundant data, resulting in less algorithm convergence time and improved data quality. The model accuracy can be improved and resources can be saved in subsequent data collection [4]. Generally, feature selection methods can be classified into one of three categories: filter, wrapper and hybrid/embedded. In the filter method, the evaluation of subset features is independent of the model. The wrapper method evaluates the subset features by computing feature score while maximizing accuracy. On the other hand, the hybrid/embedded method combines both the wrapper and the filter methods [6]. In the embedded feature selection method, the training process typically involves the selection of features to be trained for the modeling algorithm to create a classifier [6]. In machine learning, feature selection is a challenging task [3]. Attempts were made by researchers to develop classifiers for vehicle collision detection in the IoV. The focus of this paper is on the features that influence vehicle collision in the IoV. Rigorous studies on selecting features that influence vehicle collision are limited in the literature, and the few studies that conducted feature selection mainly depended on the correlation coefficient for selecting features influencing vehicle collision in the IoV. [PERSON] and [PERSON] [7] proposed deep learning for the development of vehicle collision detection systems for deployment in multi-access edge computing in the IoV. The study used deep recurrent for the modelling to predict vehicle collision in the IoV. The work was limited to the recurrent network prediction of collisions without discussing the features influencing collisions in the IoV and feature selection was absent in the study. Similarly, [PERSON] et al. [8] used YOLOv4 for the development of a vehicle avoidance system in the IoV by predicting future vehicle positions and similar to [PERSON] and [PERSON] [7], no feature selection was conducted and a discussion about the features influencing collision in the IoV was out of the scope of the study. [PERSON] et al. [9] developed collision detection alarm system for IoV, features were extracted based on the inbuilt feature extraction mechanism in Squeeze-and-excitation network. However, feature selection was not covered. [PERSON] et al. [10] developed an IoV-based model for the detection of collisions in the IoV. Like [PERSON] and [PERSON] [7]; in [PERSON] et al. [8]; in [PERSON] et al. (2019) [9] study on vehicle collision feature selection was not included in the scope of the studies. On the other hand, [PERSON] et al. [10] proposed an IoV-based vehicle collision detection system. The features influencing vehicle collision were collected and discussedin the study. The correlation coefficient was used to select the subset features used for modeling the algorithm to detect vehicle collision. In another study, the features influencing vehicle collision in the IoV were selected using the correlation coefficient to reduce the number of available features generated from the IoV before developing the classifier for collision detection [11]. Similarly, [PERSON] et al. (2023) [12] adopted the features used in the study of [PERSON] et al. [13] to generate the vehicle collision data used for the development of classifiers to detect vehicle collision in the IoV. From the literature, it is established that the research community in the domain of IoV heavily relied on the correlation coefficient, a traditional technique for the selection of subset features influencing vehicle collision in the IoV environment.