<![CDATA[Driver distraction represents a major cause of traffic accidents, posing a serious threat to human life. In this review, we present the latest research findings of driver distraction detection based on convolutional neural networks (CNNs). In general, the analysis of driver behavior while driving is represented by either detecting driver drowsiness or attention diversion from driving by other activities, all of which fall under the definition of driver distraction. Facial features are often the basis for detecting driver drowsiness. In most papers, it is typically done by eye blinking, yawning, and head movement. As for the driver attention diversion, it is through the position of the hand and face. It involves many activities, text messages, making phone calls, adjusting the radio, consuming beverages, reaching for objects behind the driver, applying makeup, interacting with passengers, and other similar distractions. However, suggesting new methodologies in driver distraction detection and choosing appropriate CNN-based techniques is a big challenge given the wide variety experiments and studies in this field. Therefore, previous papers should be revisited to produce new methods by taking advantage of the techniques used. As a result, this paper reviews research approaches and reveals the effectiveness of CNN in detecting driver distraction. Finally, the article lists techniques that can be used as benchmarks in this context.]]>
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