- Classification fusion: Sensors such as cameras often provide information on the class of a certain object. This information is highly relevant to improve the data fusion performance, e.g. to specify class-specific sensor characteristics. The Data Fusion Designer of BASELABS Create Embedded now supports to design data fusion systems that include classification information from any of the configured sensors. If multiple sensors provide a classification for an object, the built-in classification fusion resolves potential conflicts. Furthermore, the runtime visualization has been enhanced and includes the object classification.
- Out-of-sequence measurements handling: When using multiple sensors, data may arrive delayed at the data fusion, e.g. due to pre-processing or communication. To handle these so-called out-of-sequence measurements, BASELABS Create Embedded now provides a deterministic buffering approach. When using this buffering method, the measurements of the different sensors are buffered and then processed in chronological order.
- Runtime calibration: The parameters configured in the Data Fusion Designer now can be changed at runtime as well.
- Visualization of ego motion data: The velocity and yaw rate of the host vehicle are shown in the runtime visualization.
- Track statistics: For each configured sensor, the resulting track structure of the data fusion contains the information whether an object has been seen by this sensor for the last eight time steps.
- Traceability: Generated files contain a header containing information on the time of creation and the used version of BASELABS Create Embedded.
- Host vehicle Parameters: Dimensions of the host vehicle can be configured in the Data Fusion Designer and are used in the visualization during development and runtime.
- vADASdeveloper component: The classes used at the component's pins check that the maximum number of measurements is not exceeded.
- Unique error codes: The error codes contain information on their origin.
- Association: When multiple system models are configured, the measurements from all continuous models are used for the measurement to track association.
- Data fusion template: Less stack memory consumption to avoid stack overflow exceptions.
- Example projects: ROS and vADASdeveloper example project estimate the object's width and object class.
- Template dependencies: Dependencies between templates can be expressed. Missing dependencies are automatically added, when adding a new data fusion item.
- vADASdeveloper example project: The example project cannot be built when the Trait-C data fusion template is used.
- Runtime visualization: The fields of view are not correctly drawn but cover the whole visualization area.
- Runtime visualization: Predicted measurements are shown even if a sensor is not active.
- vADASdeveloper component: Large measurement data structures cannot be marshalled to C structures.
- ROS node: Extended state spaces, e.g. by an additional width space, are not supported in the track message.
- Linux: Custom modules not found when building a data fusion project on Linux machines.