LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations

TL;DR

LMT-Net is a neural network architecture that automates the generation of high-definition lane maps from sparse vehicle observations, reducing reliance on dense sensors and manual annotations in autonomous driving.

Contribution

The paper introduces LMT-Net, a novel transformer-based neural network for predicting lane models from sparse vehicle data, enhancing scalability and automation in HD map creation.

Findings

LMT-Net outperforms baseline methods on internal datasets.

The approach effectively predicts lane connectivity and structure.

Promising results on highway and non-highway domains.

Abstract

In autonomous driving, High Definition (HD) maps provide a complete lane model that is not limited by sensor range and occlusions. However, the generation and upkeep of HD maps involves periodic data collection and human annotations, limiting scalability. To address this, we investigate automating the lane model generation and the use of sparse vehicle observations instead of dense sensor measurements. For our approach, a pre-processing step generates polylines by aligning and aggregating observed lane boundaries. Aligned driven traces are used as starting points for predicting lane pairs defined by the left and right boundary points. We propose Lane Model Transformer Network (LMT-Net), an encoder-decoder neural network architecture that performs polyline encoding and predicts lane pairs and their connectivity. A lane graph is formed by using predicted lane pairs as nodes and predicted lane connectivity as edges. We evaluate the performance of LMT-Net on an internal dataset that consists of multiple vehicle observations as well as human annotations as Ground Truth (GT). The evaluation shows promising results and demonstrates superior performance compared to the implemented baseline on both highway and non-highway Operational Design Domain (ODD).