Advanced Lane Detection Model for the Virtual Development of Highly Automated Functions

TL;DR

This paper introduces an advanced lane detection model optimized for virtual development environments, enabling efficient and realistic perception of lanes for highly automated driving functions in CarMaker.

Contribution

The paper presents a novel lane detection model (ALDM) that improves calculation efficiency for vehicle guidance in virtual simulation environments.

Findings

ALDM reduces computation time compared to previous models.

ALDM enhances the accuracy of lane detection in CarMaker simulations.

The model supports both lateral and longitudinal vehicle guidance.

Abstract

Virtual development and prototyping has already become an integral part in the field of automated driving systems (ADS). There are plenty of software tools that are used for the virtual development of ADS. One such tool is CarMaker from IPG Automotive, which is widely used in the scientific community and in the automotive industry. It offers a broad spectrum of implementation and modelling possibilities of the vehicle, driver behavior, control, sensors, and environmental models. Focusing on the virtual development of highly automated driving functions on the vehicle guidance level, it is essential to perceive the environment in a realistic manner. For the longitudinal and lateral path guidance line detection sensors are necessary for the determination of the relevant perceiving vehicle and for the planning of trajectories. For this purpose, a lane sensor model was developed in order to efficiently detect lanes in the simulation environment of CarMaker. The so-called advanced lane detection model (ALDM) is optimized regarding the calculation time and is for the lateral and longitudinal vehicle guidance in CarMaker.