Press Releases 1 to 3 of 3
05.07.2022 10:00 BASELABS Dynamic Grid enables highly automated parking
Chemnitz (Germany), July 05, 2022 - BASELABS, the specialist for sensor fusion, adds dedicated functionality for automated parking to their algorithm for processing high-resolution sensor data. Baselabs Dynamic Grid now detects parking spaces from semantically processed camera data. In combination with the reliable free space estimation, the vehicle is enabled to automatically approach parking spaces and park independently. This functionality matches perfectly with the integrated detection of dynamic and static objects in the Dynamic Grid. The algorithm runs on automotive CPUs in real-time and is implemented according to ISO26262.
08.03.2022 11:00 BASELABS Create Embedded 8 now with Multiple-Models Support for Higher Reliability in Sensor Fusion
Chemnitz (Germany), March 8, 2022 - BASELABS, the specialist for sensor fusion, introduces a new release of its modular, ISO 26262-certified software library for the development of data fusion systems for automated driving functions: BASELABS Create Embedded 8 now supports distinct models for different types of dynamic objects in its software development kit (SDK). Developers of ADAS control units thus obtain, e.g., a constant velocity model for pedestrians and a constant curvature model for vehicles. If an object is not yet assigned to a class, for example during initialization, multiple hypotheses with different models can be created. With the new multiple-models support, the reliability of predicting movements of different object classes increases. As a result, the measurement association and the overall performance of sensor fusion are significantly improved.
24.11.2021 14:00 The sensor fusion for tomorrow's urban driving functions BASELABS Dynamic Grid
Chemnitz (Germany), November 24, 2021. BASELABS, the specialist for sensor fusion, presents 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.
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