Autonomous Driving Stack

An autonomous driving stack comprises interconnected modules for perception, prediction, planning, and control, aiming to enable safe and reliable self-driving vehicles. Current research emphasizes improving the robustness and security of these stacks, focusing on techniques like vulnerability-adaptive protection, deep learning models (including attention mechanisms, generative adversarial networks, and reinforcement learning), and efficient real-time algorithms for tasks such as object detection and trajectory prediction. This work is crucial for addressing safety concerns and accelerating the development of commercially viable autonomous driving systems, impacting both the scientific understanding of complex AI systems and the practical deployment of self-driving technology.