Detecting Abnormal Driving Behavior Using Modified DenseNet

Abstract

Car accidents have serious consequences, including depletion of resources, harm to human health and well-being, and social problems. The three primary factors contributing to car accidents are driver error, external factors, and vehicle-related factors. The main objective of this paper is to address the issue of car accidents caused by driver error. To achieve this goal, a solution is proposed in the form of a modified version of the Dense model, called the 1Dimention-DenseNet, specifically designed to detect abnormal driving behavior. The model incorporates adapted dense blocks and transition layers, which enable it to accurately identify patterns indicative of abnormal driving behavior. This paper compares the performance of the 1D-DenseNet model to the original DenseNet model in detecting abnormal driving behavior in the Kaggle distracted driver behavior dataset. Results show that the 1D-DenseNet model outperforms the original DenseNet model in classification and validation accuracies, loss, and overhead. The 1D-DenseNet, after 100 epochs of training using Keras on top of TensorFlow, the 1D-DenseNet achieved a categorical cross-entropy loss of 0.19 on the validation set, with classification and validation accuracies of 99.80% and 99.96%, respectively. These findings demonstrate the effectiveness of the 1D-DenseNet model in improving the detection of abnormal driving behavior.

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

“…Te use cases for digital picture segmentation are numerous, as are the types of images that can be segmented. In this research, we propose an improved version of DenseNet [13] (TLDenseNet)-one of the best CNN models-by integrating the transfer learning mechanism to better handle pictures with varying attributes and segmentation needs. Figure 5 depicts the basic idea behind this model-based approach to digital picture segmentation.…”

Application of Driving Behavior Control System Using Artificial Neural Network to Improve Driving Comfort by Adjusting Air-to-Fuel Ratio

“…Another study also observed and assessed driver control and safe practices during the process of driving a vehicle using risky driving behavior as the research variable [16]. Moreover, Mafeni [17] and Takashi Bando [18] evaluated driving behavior errors with a focus on abnormal braking and vehicle speed operation when traveling at very high speeds. It was also observed that even though the studies have different objectives, none focused on energy consumption in evaluating driving behavior.…”