Establishment and Analysis of Dynamic Traffic Condition Model Based on In-Vehicle Video Images

In recent years, with the rapid development of autonomous driving technology, the demands for real- time traffic condition detection and route planning have been increasing. Map navigation applications can offer many positioning and routeplanning services, and accurate traffic condition information is crucial for travel decisions. However, traditional traffic condition detection methods, which mainly rely on driving users' trajectory information, face challenges in accuracy when there are few users or abnormal driving behaviors. So, it's important to find more efficient detection methods. This paper proposes a method for traffic condition analysis using in- vehicle video images. By analyzing these images, we can accurately observe road conditions such as the number of vehicles, road width, and traffic flow, thus enabling more accurate traffic state evaluation. We use the convolutional neural network algorithm to build a neural network model based on the collected video images and train it to judge road traffic states, improving the accuracy of traffic condition detection. When the graphic resolution is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128 \times 128$</tex>, this method can achieve certain accuracies on both the training and test sets. In conclusion, this paper constructs a convolutional neural network model for in- vehicle video images to effectively predict traffic conditions, providing a useful exploration for the development of intelligent navigation systems.