Enhancing autonomous driving vision: deep learning approaches to data synthesis, 3D representation and model efficiency

In the autonomous driving domain, effectively perceiving and interpreting the environment is crucial for safe and efficient navigation. The field has been revolutionized by deep learning models that enable the vehicle to detect, recognize and segment objects in the surrounding environment. However, several challenges persist, including the time-consuming process of data collection and labeling, inconsistencies in the quality of input data, and the constraint of real-time processing. To address these issues, we present three approaches: the augmentation of training datasets, the refinement of input data during the inference stage, and the transfer of knowledge from complex to lightweight models via a process known as knowledge distillation. We show that generative adversarial networks and attention-based architectures can be leveraged, respectively, to generate realistic samples from a learned data distribution and to increase the input resolution. These methods can improve the generalization capabilities of detection, segmentation, and reconstruction algorithms. Furthermore, a significant contribution of our research is the integration of knowledge distillation, aligning the performance of simpler models with their more complex counterparts. This ensures the perception systems are not only accurate but also versatile in real-time scenarios, a critical aspect for the successful deployment of safe autonomous vehicles.