# Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

## TL;DR

This paper presents a real-time multi-lane perception method for autonomous vehicles using GraphSLAM to fuse camera and traffic data, enabling robust lane detection up to 120 meters.

## Contribution

It introduces a novel GraphSLAM-based fusion approach for multi-lane perception that improves detection range and robustness over traditional serial methods.

## Key findings

- Ego and adjacent lanes detected up to 120 meters
- Enhanced detection range compared to serial lane detection
- Robust perception of multiple lanes in highway environments

## Abstract

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles.