YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless Automated Diving
This addresses the need for real-time, cost-effective perception in automated driving by reducing reliance on expensive and outdated maps, though it is incremental as it builds on existing YOLO methods.
The paper tackles the problem of detecting lane boundaries and other 1D structures in images for mapless automated driving, proposing a neural network based on YOLO that achieves detection of lane centerlines, borders, and markings with inherent classification into types like dashed or solid lines.
In automated driving, highly accurate maps are commonly used to support and complement perception. These maps are costly to create and quickly become outdated as the traffic world is permanently changing. In order to support or replace the map of an automated system with detections from sensor data, a perception module must be able to detect the map features. We propose a neural network that follows the one shot philosophy of YOLO but is designed for detection of 1D structures in images, such as lane boundaries. We extend previous ideas by a midpoint based line representation and anchor definitions. This representation can be used to describe lane borders, markings, but also implicit features such as centerlines of lanes. The broad applicability of the approach is shown with the detection performance on lane centerlines, lane borders as well as the markings both on highways and in urban areas. Versatile lane boundaries are detected and can be inherently classified as dashed or solid lines, curb, road boundaries, or implicit delimitation.