Advanced Lane Detection Model for the Virtual Development of Highly Automated Functions
This work addresses the problem of efficient lane detection for virtual prototyping in automated driving systems, but it is incremental as it builds on existing simulation tools and focuses on optimization within a specific platform.
The paper tackles the need for realistic environment perception in virtual development of highly automated driving functions by developing an advanced lane detection model (ALDM) optimized for calculation time in the CarMaker simulation tool, enabling efficient lane detection for lateral and longitudinal vehicle guidance.
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.