Deep Q-network (DQN) for autonomous driving
The Deep Q-Network (DQN) is a pivotal reinforcement learning architecture applied in autonomous driving, enabling vehicles to make decisions based on visual inputs.
autonomous driving
BEVDet
BEVDet is a deep learning framework designed for 3D object detection using bird’s-eye view representations. It is primarily used in autonomous driving systems to improve the perception of surrounding environments from multi-camera setups.
OccNet (occupancy network for driving)
OccNet (occupancy network for driving) is a deep learning framework designed to model and predict the spatial occupancy of dynamic environments for autonomous driving. It uses 3D occupancy representations to enhance perception and decision-making in self-driving systems.
Imitation learning for autonomous driving
Imitation learning for autonomous driving is a machine learning approach where vehicles learn driving behaviors by mimicking human drivers. It aims to enable autonomous systems to replicate expert driving decisions through observation and data-driven modeling.
A2D2 (Audi autonomous driving dataset)
The A2D2 dataset is a comprehensive collection of data for autonomous driving, developed by Audi to aid in the research and development of self-driving technologies.