Navigation and Autonomous Driving involves a vehicle’s ability to plan routes, make decisions based on real-time data, and safely reach its destination without human intervention. Autonomous vehicles use various sensors, including GPS, cameras, lidar, radar, and inertial measurement units, along with advanced algorithms to perceive the environment and navigate effectively.

Posts

Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts

Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird’s-eye-view (BEV) space. In contrast to prior works that require dense supervision such as semantic labels in perspective view, our method only requires human annotations for parametric attributes that are cheaper and less ambiguous to obtain. To solve this challenging task, our design is comprised of modules that incorporate inductive biases to learn occlusion-reasoning, geometric transformation and semantic abstraction, where each module may be supervised by appropriately transforming the parametric annotations. We demonstrate how our design choices and proposed deep supervision help achieve meaningful representations and accurate predictions. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state-of-the-art results with considerably less human supervision.