Lane Detection and Classification using Cascaded CNNs
This work addresses the need for reliable lane boundary identification and classification to improve positioning and path planning in autonomous vehicles, representing an incremental advancement in the field.
The authors tackled the problem of lane detection and classification for autonomous vehicles by developing an end-to-end system using two cascaded CNNs, which runs in real-time and was trained on 14,336 labeled lane boundary instances from the TuSimple dataset.
Lane detection is extremely important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. As many other computer vision based tasks, convolutional neural networks (CNNs) represent the state-of-the-art technology to indentify lane boundaries. However, the position of the lane boundaries w.r.t. the vehicle may not suffice for a reliable positioning, as for path planning or localization information regarding lane types may also be needed. In this work, we present an end-to-end system for lane boundary identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. To build the system, 14336 lane boundaries instances of the TuSimple dataset for lane detection have been labelled using 8 different classes. Our dataset and the code for inference are available online.