Liquid Dino: A Multi-Task Neural Network towards Autonomous Driving

When discussing advanced driver assistance systems, it is essential to understand the driver’s emotions, behaviors and surroundings in order to improve the safety and overall driving experience of the driver. This work investigates the performance of various neural network architectures across four distinct classification tasks, namely, Emotion Recognition, Driver Behavior Recognition, Scene-Centric Context Recognition and Vehicle-Based Context Recognition, all of which incorporate visual information captured through cameras. By utilizing camera based data, we aim to evaluate how different neural architectures handle visual inputs in these diverse contexts, thereby exploring the robustness and generalization of each model to different real-world scenarios. We compare the performance of several state-of-the-art models and introduce a novel contribution that significantly improves classification accuracies in all areas. Our results show that the proposed Liquid Dino architecture achieves an overall average accuracy of 83.79%, outperforming other models in the literature.