Sensor fusion algorithm uses raw data for automotive models -

Sensor fusion algorithm uses raw data for automotive models

BASELABS’ new Dynamic Grid algorithm takes high-resolution raw sensor data to provide a consistent, detailed environment model, and includes dynamic and static objects as well as free space estimation.


Automotive sensor fusion software company BASELABS has introduced its Dynamic Grid, an algorithm that generates a consistent environment model from high-resolution raw sensor data. The algorithm accelerates development of data fusion systems for automated driving functions, especially in challenging urban environments. It enables automotive developers to skip time-consuming algorithm training, so they 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 exceptionally 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 said Dynamic Grid addresses this challenge by processing the high-resolution sensor data from, for example, radars or laser scanners, at the raw data level. It is also possible to use cameras with semantic segmentation. As a result, the algorithm 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.

BASELABS dynamic grid
Dynamic Grid is an algorithm that generates a consistent environment model from high-resolution raw sensor data. (Source: BASELABS).

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. The algorithm is also suitable for use in radar subsystems.

The head of product development at BASELABS, Norman Mattern, said, “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.”

BASELABS provides software products to make the development of sensor fusion efficient and scalable for automotive manufacturers and suppliers. The company was founded in 2012 and is in shared ownership by its four founders and Vector Informatik, which the company said makes it strategically independent of any OEM, tier 1, or sensor supplier.

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