Sensors

The sensor fusion for tomorrow's urban driving functions

25th November 2021
Kiera Sowery

BASELABS has introduced BASELABS Dynamic Grid, an algorithm that generates a consistent environment model from high-resolution raw sensor data.

Dynamic Grid accelerates the development of data fusion systems for automated driving functions, especially in challenging urban environments. While skipping time-consuming algorithm training, automotive developers can develop driver assistance systems such as parking functions or traffic jam pilots with better performance than traditional tracking and grid methods.

Automated driving functions for urban areas set high requirements on the environment model used. On the sensor side, the industry is preparing to use high-resolution sensors to acquire the required data with a sufficient level of detail.

Traditional algorithmic methods of sensor fusion reach their limits in such a context. BASELABS Dynamic Grid, on the other hand, can process the high-resolution sensor data from, e.g. radars or laser scanners at the raw data level. It is also possible to use cameras with semantic segmentation.

As a result, Dynamic Grid provides a self-consistent environment model that detects dynamic and static objects in the vehicle environment with high accuracy and robustness. In addition, it estimates free space to identify drivable areas or parking spaces. The algorithm runs on automotive CPUs in real-time and is implemented according to ISO26262.

Dynamic Grid is particularly suitable for driving functions for automation level 2 and above, including highly automated driving. Typical application areas are automated parking functions such as trained or valet parking, emergency braking functions with automatic avoidance, or traffic jam pilots. Furthermore, the algorithm is suitable for use in radar subsystems.

"With Dynamic Grid, we present a superior alternative to the combined use of traditional tracking methods and a static occupancy grid. By processing the data in an integrated manner in a self-contained algorithm, we avoid inconsistencies that the combination of two different methods in the traditional approach often entails. Dynamic Grid can show its strengths especially in scenarios with many objects and different directions of motion in the vehicle's environment. In addition, the algorithm can detect and track objects of any shape without extensive training," said Norman Mattern, Head of Product Development at BASELABS.

"By purchasing Dynamic Grid as a software library, our customers gain very fast and cost-effective access to a sensor fusion technology for tomorrow's driving functions that is suitable for series production," said Robin Schubert, Managing Director of BASELABS.

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