TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection

TL;DR

This paper introduces TEDNet, a twin encoder-decoder neural network that combines camera and LiDAR data for accurate, real-time road surface detection in autonomous vehicles, demonstrating competitive performance on the Kitti-Road dataset.

Contribution

The novel TEDNet architecture effectively fuses camera and LiDAR features for improved road detection, with an ablation study exploring different encoding strategies.

Findings

Performs comparably to state-of-the-art methods

Operates at real-time frame rates

Effective fusion of camera and LiDAR data

Abstract

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.