## 2 Proposed Method
### Vehicle Type and Color Classification  
In this step, the detected vehicle bounding boxes are continuously tracked across the input frames, while classifying their types and colors. To ensure accurate vehicle detection in the next frame, the position of the vehicle bounding box detected in the previous frame is predicted in the next frame using Kalman filtering, as applied in previous research [4, 8]. A pre-trained Resnet-50 CNN is used to classify the type and color of the vehicle. A deep learning based classification network has been developed to categorize vehicles by types and colors. The network learns from labeled datasets and predicts the type and color of vehicles based on bounding boxes using a pre-trained model. The proposed method uses the network shown in Figure 7 for vehicle type and color classification. The pre-trained Resnet-50 CNN is used, and during the retraining step, the fully connected layer, softmax, and classification layer are modified to create a network that can classify into the respective class. This paper presents a study on the classification of vehicle color and type using deep learning networks. Images of the rear of front-driving vehicles on highways and general roads were obtained from the vehicle's DashCam camera. The learning dataset was classified into four classes: sedan, bus, truck, and SUV. Buses were defined as vehicles with more than 25 passengers, while trucks were defined as vehicles capable of carrying more than 1 ton of cargo. The objective of this paper is to determine the optimal lane for the presence of large buses and trucks in adjacent lanes during a lane change maneuver. Due to the challenge of obtaining a significant amount of training data for scenarios involving buses and trucks, a data augmentation process is performed to obtain additional learning data. This process aims to improve the model's ability to generalize new and unseen data. The training data is randomly augmented with four variations to increase its diversity: HSI  
Figure 6: Results of the activation channel with the maximum value in the 40 th ReLU layer of Resnet-50 CNN for feature extraction from road images, (**a**) input road image, (**b**) result of overlaying the maximum activation channel of the 40 th ReLU layer on input image (**a,c**) 3D model of the maximum activation channel. color change, horizontal flip, pixel movement, and addition of Gaussian noise, as shown in Figure 8. The HSI color space normalizes hue, saturation, and brightness values to a range of [0, 1]. Augmented data is generated by using the hue value to 0.1, and the saturation and brightness values to 0.3. Shift augmented data is generated by randomly shifting [5,10] pixels in four directions. Additionally, augmented data containing Gaussian white noise with a variance of 0.01 and a mean of 0 is also generated. The hue, saturation, brightness, and contrast values in the HSI color space of the detected vehicle bounding boxes are modified to generate augmented data. This augmented data is then used to train the color classification model. To generate augmented data for learning vehicle color classification, the HSI color space is first normalized to a range of [0, 1]. Next, the hue color space is then normalized to a range of [0.01, 0.6], the saturation color space to a range of [\(-\)0.4, \(-\)0.1], and the brightness space is normalized to a range of [\(-\)0.9, \(-\)0.1]. A random value is then selected from each of these ranges and used to augment the data. Additionally, contrast-enhanced data is generated by calculating the average brightness value (M) of the brightness (B) and then applying the formula (B-M) \(\times\) scale + M. The value of the scale is randomly selected from the range [1.1, 3.9]. The dataset used to classify vehicle type and color was learned from images captured by DashCam cameras installed in moving vehicles in different road environments.