A Flexible Modeling Approach for Robust Multi-Lane Road Estimation
This addresses a critical perception challenge for Advanced Driver Assistance Systems and self-driving vehicles, but it appears incremental as it builds on existing methods with modular enhancements.
The paper tackles the problem of robustly estimating road course and traffic lanes for autonomous vehicles by presenting a flexible, real-time modeling method that uses an iterative expectation-maximization approach with features from sensors. It demonstrates robustness and precision up to 120 meters in simulated and real-world scenarios, though no specific numerical gains are provided.
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.