  This paper aims to enhance the ability to predict nighttime driving behavior
by identifying taillights of both human-driven and autonomous vehicles. The
proposed model incorporates a customized detector designed to accurately detect
front-vehicle taillights on the road. At the beginning of the detector, a
learnable pre-processing block is implemented, which extracts deep features
from input images and calculates the data rarity for each feature. In the next
step, drawing inspiration from soft attention, a weighted binary mask is
designed that guides the model to focus more on predetermined regions. This
research utilizes Convolutional Neural Networks (CNNs) to extract
distinguishing characteristics from these areas, then reduces dimensions using
Principal Component Analysis (PCA). Finally, the Support Vector Machine (SVM)
is used to predict the behavior of the vehicles. To train and evaluate the
model, a large-scale dataset is collected from two types of dash-cams and
Insta360 cameras from the rear view of Ford Motor Company vehicles. This
dataset includes over 12k frames captured during both daytime and nighttime
hours. To address the limited nighttime data, a unique pixel-wise image
processing technique is implemented to convert daytime images into realistic
night images. The findings from the experiments demonstrate that the proposed
methodology can accurately categorize vehicle behavior with 92.14% accuracy,
97.38% specificity, 92.09% sensitivity, 92.10% F1-measure, and 0.895 Cohen's
Kappa Statistic. Further details are available at
https://github.com/DeepCar/Taillight_Recognition.
