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.

翻译：本文旨在通过识别人类驾驶与自动驾驶车辆尾灯信号，提升夜间驾驶行为预测能力。所提模型集成定制化检测器，可精准检测道路上前方车辆尾灯。检测器前端部署可学习预处理模块，从输入图像提取深度特征并计算各特征数据稀有度。借鉴软注意力机制设计加权二值掩码，引导模型聚焦预定义区域。本研究应用卷积神经网络提取目标区域鉴别性特征，并通过主成分分析降维，最后采用支持向量机实现车辆行为预测。为训练评估模型，收集福特汽车公司后视视角下两类行车记录仪与Insta360摄像头的大规模数据集，涵盖昼夜超1.2万帧图像。针对夜间数据不足问题，创新性提出像素级图像处理技术，将日间图像转化为逼真夜间图像。实验结果表明，该方法可实现92.14%准确率、97.38%特异性、92.09%敏感性、92.10%F1值及0.895 Cohen's Kappa系数的精准车辆行为分类。更多细节见https://github.com/DeepCar/Taillight_Recognition。