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Algorithms for ADAS

Vector’s vADASdeveloper provides an infrastructure for developing algorithms for ADAS and automated driving. The tool reduces the workload when developing sensor data fusion applications.

Version 2.0 of vADASdeveloper gives access to CAN signals (Photo: Vector)

The runtime environment acquires sensor data, logs it, and can replay the data for stimulation. The sensor data and the results of algorithms are displayed in a bird’s eye perspective and in the video image. Additional functions of the recently released version 2.0 optimize ADAS (advanced driver assistance systems) applications from algorithm development to data logging for multiple sensors, stimulation, and visualization of processing results.

The tool gives access to network signals (CAN, LIN, Flexray, and others) with Vector interfaces and it enables a combination of signal-oriented automotive measurement technology with object-oriented data fusion development. It also gives access to ECU-internal information using XCP and VX1000. With it, high-precision time synchronization of input signals with XCP 1.3 mechanisms and Vector hardware synchronization can be achieved.

The tool replays MDF files and videos in AVI format for algorithm development with previously recorded test drive data, for example from CANape. It also lets the user configure input data objects for data fusion from individual signals.

Vector bought the multi-sensor development tool from Baselabs about one year ago. By adopting the tool from Baselabs into its own tool chain, Vector supports the entire development process for ADAS systems. While the primary focus of vADASdeveloper is on system development, the CANape “Driver Asistance” option is used for the validation of object recognition algorithms. While the algorithm is running in the ECU, CANape can be used to calibrate and validate the ECU.

The company offers webinars for its vADASdeveloper infrastructure.


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