A Flexible Modeling Approach for Robust Multi-Lane Road Estimation

TL;DR

This paper introduces a flexible, real-time multi-lane road modeling method using an iterative expectation-maximization approach, enhancing robustness and precision for autonomous vehicle environment perception.

Contribution

The paper presents a modular lane modeling framework that integrates multiple data sources and constraints, adaptable to various geometric representations and real-time applications.

Findings

Robust lane estimation up to 120 meters distance

High precision demonstrated in simulated and real vehicle data

Flexible approach adaptable to different lane geometries

Abstract

A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles.