Capabilities must be in place to handle data rates of 100 MB/s for radar sensors and up to 1 GB/s (for example for an 8-megapixel camera). In ADAS projects, the focus is on raw data and internal data on the sensor level. This allows them to measure all the required data from the vehicle ECU in the course of test-drives or Hardware-in-the-Loop (HiL) tests. In-vehicle DAQ in the development phaseĪs soon as function developers have access to preproduction prototypes of the ECU and sensor hardware, they can make use of integrated measurement technology such as the ETAS ETK or GETK. Standard software tools and frameworks can also be connected – for example the robot operating system (ROS) and the automotive data and time-triggered framework (ADTF). Intuitive handling is supported by various modules for control and configuration such as the RALO (rapid logging) Manager and the RALO Recorder. The virtual GETK is managed via the ETAS SW Framework, in which the measurement data can also be visualized. In this way, the measurement data reaches a data logger directly or via the Ethernet network. This offers the advantage of using the high-performance Ethernet interfaces of industrial PCs for data output. The V-GETK is integrated into the ECU prototypes as software. To fill this gap and enable data measurements to begin from the prototyping phase, ETAS has developed a virtual GETK, the V-GETK (Fig. These simulated ECUs provide different interfaces and properties than hardware intended for production, preventing the usage of legacy ETAS hardware-oriented measurement technology such as the emulator test probe (ETK). To allow earlier development work to proceed, simulated ECUs implemented on industrial PCs are typically utilized. Yet, particularly in the prototyping phase, such data acquisition has previously been unsuccessful due to the fact that, as a rule, production-ready ECU and sensor hardware is not yet available. This in-vehicle DAQ is extremely important because it supplies the database that can be used to validate the virtualized development of automated driving functions. In-vehicle data acquisition from the prototyping phase These solutions provide optimum support for in-vehicle data acquisition (DAQ) in every phase. ETAS offers a modular portfolio of scalable solutions specifically designed to meet these requirements. It is important to note that configurations vary, so adequate data volumes may range from just a few megabytes (MB) to several gigabytes (GB) per second. Whatever the phase, developers need solutions for acquiring measurement data and accessing this data. Moreover, such a development environment must be capable of being integrated into the vehicle development processes that are split between OEMs and suppliers and supporting their various maturity levels – from prototyping and the various phases of development and function extensions right through to fleet management after start of production. This requires reliable, flexible tools for efficient data acquisition as well as the possibility of accessing data via a cloud or back end (Fig. Yet this approach relies on adequate validation of the models with real environment data. Wherever possible, virtualization is replacing experiments based on real hardware. To develop these complicated control strategies efficiently, development of the software functions is shifting to the lab. These must be capable of processing huge quantities of data and deriving driving strategies from this data within a matter of milliseconds. Powerful electronic control units (ECUs) are used to coordinate the complex sensor networks. Instead of using our eyes and ears, the vehicle’s surroundings will then be monitored by radar, lidar, video, and ultrasonic sensors. The hope is that these systems will make automated driving possible within a few years. But, because people get tired and distracted and are sometimes slow to react, there is a growing move toward the use of advanced driver assistance systems (ADAS). New modular measurement technology from ETAS is helping to acquire and deliver this data.Įyes, ears, experience, and a healthy dose of intuition – that is all you currently need to drive a car. To design these systems and check their function at all stages of development, system developers need comprehensive access to measurement data. To enable automated driving, sensor systems must replace human sensory perception.
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