BASELABS Dynamic Grid provides sensor fusion of dynamic and static objects, including free space detection. It also supports high-resolution and semantic sensors. The software library allows the development of challenging use cases in urban and highway environments, such as valet parking, L2+ driver assistance, and highly automated driving.
The Dynamic Grid is the right solution for your project, if you...
The dynamic grid is an advanced approach to detect static and dynamic objects and to estimate free space in an integrated algorithm. This ensures consistent information about objects and free-space free of contradictions. ARM- and x64-based processors are ideally suited to run the algorithm in real-time. The dynamic grid makes optimal use of its performance. Compared to state-of-the-art AI approaches, the dynamic grid can fuse different sensor modalities like radar and camera in an integrated manner.
Parking, assisted and automated driving in urban and highway environments
Exemplary use cases for the dynamic grid are:
From a system setup perspective, the Dynamic Grid is well suited for radar or lidar sub-systems and provides the required low-level fusion. A typical example is a radar sub-system with four corner radars which generates a unified output from all sensors of this modality.
"The dynamic grid provided by BASELABS is a promising algorithm to significantly improve the environment perception in challenging environments."
Dr. Steen Kristensen
Senior Expert and Teamleader Comprehensive Environment Model
The algorithm divides the environment into small areas, so-called cells. For each cell, the algorithm determines whether it is free or occupied. If it is occupied by an object, its velocity and driving direction are also calculated. Finally, static and dynamic objects are clearly separated from each other and provided together with the free space, e.g. for maneuver decisions and path planning.
Urban environments add new challenges for automated driving functions and the required environment models. Highway-like scenarios mainly contain objects that can be well modeled and detected using classical data fusion and tracking methods. The objects in city, however are more diverse, more complex to model, and partially unforeseeable. To address urban environments, high-resolution lidar and radar sensors are becoming more and more popular. However, classical algorithms like the occupancy grid have severe shortcomings in processing point clouds in scenarios that contain both stationary and moving objects. The Dynamic Grid is a new approach that overcomes these shortcomings and determines free space as well as static and dynamic objects in an integrated algorithm. To provide an even more comprehensive environmental model, semantic information from cameras can be incorporated as well.
Get in touch with Eric Richter, Director Innovation Technology of BASELABS.
A library for the development of embedded data fusion systems for automated driving functions.
BASELABS is executing on the vision of "safe automated driving for all vehicles". We provide software products to make the development of sensor fusion efficient and scalable.
