To extend the TMVA4D architecture to handle a larger number of classes, such as different types of vehicles and obstacles, in addition to humans, several modifications can be implemented. One approach is to increase the number of output channels in the final layer of the architecture to accommodate the additional classes. Each channel would correspond to a specific class, allowing the model to predict multiple classes simultaneously. Additionally, the training dataset would need to be expanded to include annotations for the new classes, ensuring that the model learns to differentiate between humans, vehicles, and other obstacles. Fine-tuning the hyperparameters, such as the loss function weights and learning rate, may also be necessary to optimize the model for the increased complexity of multiple classes.