Affordance Learning for Detection of Drivable Area
Affordance learning for drivable areas focuses on leveraging deep learning
techniques to predict
actionable cues that determine navigable regions for autonomous vehicles. This involves training
neural networks on multi-modal sensor data, such as LiDAR, camera imagery, and radar, to map
low-level perceptual inputs to high-level affordances like lane boundaries, free space, and
obstacle
proximity. State-of-the-art models integrate spatial and temporal features using convolutional
and
recurrent architectures to enhance real-time decision-making. The learning process often employs
supervised or self-supervised methods, emphasizing generalization across diverse road
environments while maintaining robustness to occlusions and dynamic objects.