TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection
This work addresses road detection for autonomous ground vehicles, but it is incremental as it builds on existing sensor fusion approaches with minor architectural variations.
The authors tackled road surface detection for autonomous vehicles by proposing TEDNet, a twin encoder-decoder neural network that processes camera and LiDAR data, achieving performance comparable to state-of-the-art methods on the Kitti-Road dataset while operating at real-time frame rates.
Robust road surface estimation is required for autonomous ground vehicles to navigate safely. Despite it becoming one of the main targets for autonomous mobility researchers in recent years, it is still an open problem in which cameras and LiDAR sensors have demonstrated to be adequate to predict the position, size and shape of the road a vehicle is driving on in different environments. In this work, a novel Convolutional Neural Network model is proposed for the accurate estimation of the roadway surface. Furthermore, an ablation study has been conducted to investigate how different encoding strategies affect model performance, testing 6 slightly different neural network architectures. Our model is based on the use of a Twin Encoder-Decoder Neural Network (TEDNet) for independent camera and LiDAR feature extraction, and has been trained and evaluated on the Kitti-Road dataset. Bird's Eye View projections of the camera and LiDAR data are used in this model to perform semantic segmentation on whether each pixel belongs to the road surface. The proposed method performs among other state-of-the-art methods and operates at the same frame-rate as the LiDAR and cameras, so it is adequate for its use in real-time applications.