Virtuelle Instrumente in der Praxis VIP 2017

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Behind the Test Challenges of Automotive Radar Systems

David A. HallNational Instruments Corporation, Austin, USA

KurzfassungVor mehr als 50 Jahren, nämlich bereits 1959, verbaute man im Konzeptauto CadillacCyclone XP-74 zwei abgewandelte und urspünglich aus der Luftfahrt stammende Radarsys-teme, die den Fahrer vor Verkehrsaufkommen warnen sollten. Heute ist ein Radar im Fahr-zeug kleiner als ein Eishockey-Puck und wurde vom Konzept zur Realität.

Einfachere Systeme zur Vermeidung von Auffahrunfällen und unerwünschten Spurwech-seln werden mittlerweile durch Fahrerassistenzsysteme (Advanced Driver Assistance Sys-tems, ADAS) ersetzt, wodurch sich auch neue Herausforderungen bei der Entwicklung unddem Test der Systeme ergeben. Moderne ADAS-Architekturen kombinieren komplexe Sen-sorik, Verarbeitungs- und algorithmische Technologien miteinander und bilden das, wasim Endeffekt das Innenleben der autonomen Fahrzeuge ausmacht. Für den Verbraucherbedeutet der gesteigerte Einsatz von ADAS-Technologien ein Plus an Komfort und Bequem-lichkeit. Für Ingenieure jedoch bringt die Evolution von ADAS zum einen sichere Arbeits-plätze mit sich, zum anderen aber auch unterschiedlichste Herausforderungen in System-entwicklung und -test.

AbstractMore than 50 years ago in 1959 the Cadillac Cyclone XP-74 concept car featured two mod-ified aircraft radars that were designed to alert the driver for the presence of oncoming traf-fic. Today, an automotive radar sensor is smaller than a hockey puck and has transitionedfrom concept to reality (fig. 1).

Figure 1: Nose cones on the front of a Cadillac Cyclone concept car housed modified aircraft radars.

Behind the Test Challenges of Automotive Radar Systems

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As primitive systems for collision and lane change avoidance are being replaced with ad-vanced driver assistance systems (ADAS) – they introduce new design and test challenges.Modern ADAS architectures combine complex sensing, processing, and algorithmic tech-nologies into the what will ultimately become the guts of autonomous vehicles. As consum-ers, the growth in ADAS technology provides comfort and convenience. As engineers, theevolution of ADAS provides a combination of job security mixed with very different designand test challenges.

State of ADAS TechnologyAs ADAS systems evolve from simple collision avoidance systems to level 5 autonomousvehicles, they require increasingly complex sensing and computing technologies. Forexample, consider the sensing technology on the Tesla Model S, which combines informa-tion from 8 cameras and 12 ultrasonic sensors as part of its Autopilot technology. In addi-tion, numerous reports have pointed to the 2018 Audi A8 as the first vehicle capable ofLevel 3 autonomy using radar, camera, and LIDAR sensors.

Adding to the complexity, autonomous vehicles like these are increasingly using sensorfusion to combine multiple sensor inputs from radar, camera, ultrasound, LIDAR to betterinterpret objects and obstacles. This approach takes advantage of the benefits of radar forrange detection and the benefits image processing algorithms for identifying what thoseobjects are. Although sensor fusion can significantly improve the accuracy with which anautonomous vehicle detects obstacles – it has significant implications on the processingcapabilities of autonomous vehicles.

Figure 2: Sensor fusion utilizes decision-making using inputs from multiple types of sensors.

As a result, autonomous vehicles utilize significantly more complex processing technolo-gies and generate more data than ever before (fig. 2). As an example, the Tesla Model Sactually contains 62 microprocessors – more than three times the number of moving partsin the vehicle. In addition, Intel recently estimated that tomorrow’s autonomous vehicleswill produce 4 Terabytes of data every second. Making sense of all of this data is a signifi-cant challenge – and engineers have experimented with everything from simple PID loopsto deep neural networks to improve autonomous navigation.

David A. Hall

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Importance of System-Level TestThe combination of increasingly complex ADAS technology – from sensor fusion to artifi-cial intelligence algorithms – demands a new approach to system design and test. Forexample, one can imagine the challenges of testing the performance of an unpredictablealgorithm like a deep neural network.

Engineers testing automotive radar systems are no longer answering the question of “doesthe radar tell the right range?”. Instead, they are asking “can my sensor fusion algorithm tellthe difference between a pedestrian and a road sign?” Therefore it is no longer enough justto test the physical signal (transmit/receive RADAR signal). Instead, test techniques likeobject simulation are hard requirements. Answering such questions are essential for thedevelopment of autonomous driving – and uncovering those answers requires more than asimple voltage measurement or reading a CAN packet. Instead, it requires engineers to re-create the embedded algorithm’s physical environment with hardware-in-the-loop (HIL) testsystems.

These systems run vehicle models in real time and provide “fake” signals to one or more ofa vehicle’s electronic control modules (ECM) in order to convince the ECM that it is operat-ing under real-world conditions. Typical HIL systems use automotive networks such asCAN, FlexRay, and BroadR-Reach as well as electrical signals with built-in fault injection tosimulate a vehicle’s interaction with the ECM.

As the impetus to “apply the brakes” shifts from a person pushing a physical pedal to acomputer sorting through terabytes of data – HIL test systems must also evolve. Today, HILtest systems must accommodate technologies like ADAS and V2X communication and mustre-create new environmental scenarios like the electronic signature of a pedestrian crossingthe street.

Figure 3: Typical radar test patterns

Additionally, multiple sub-systems must now be tested together to ensure that the entiresystem functions properly. It is no longer enough to merely test the radar system, camerasystem, and brake system separately. Instead, engineers are increasingly required to test theentire chain to make sure the vehicle will actually stop when an object is detected. As aresult, HIL test solutions are evolving in a manner that allows them to simulate multiplesensors types using tightly synchronized instrumentation, bus interface, and data acquisi-tion modules (fig. 3).

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NI’s Platform-Based Approach to HILTo address these challenges, NI offers a platform-based approach to hardware-in-the-looptesting. Using HIL test techniques, engineers are able to rapidly develop and test highlycomplex control systems by simulating the physical environment. Key features of NI’s HILtest approach is the tight synchronization between a wide range of PXI instruments – whichenables engineers to simulate many unique driving scenarios and sensors. Particularlyimportant in ADAS and autonomous driving applications, NI’s FPGA technology enablesengineers to design HIL test systems with extremely fast loop rates in order to test quickdecision making.

One recent example of an HIL test system that utilizes NI’s platform-based approach tosensor fusion testing was demonstrated by the ADAS Innovations in Test (http://www.adas-iit.com/) consortium at NIWeek 2017 in Austin, Texas. This group is a joint col-laboration between NI alliance partners S.E.T., Konrad Technologies, measX, and S.E.A. AtNIWeek, the group demonstrated a comprehensive ADAS test solution which is able to syn-chronously simulate radar, LIDAR, communication, and camera signals for an ADAS sensor.In Figure 4, observe that the solution was able to simulate a virtual test drive using the IPGCarMaker and NI VeriStand software (fig. 4).

Figure 4: Simulating a virtual test drive using IPG CarMaker Software, NI VeriStand Software, and PXI modular hardware

In parallel with simulating the physical signals that correspond to the simulation, modularsystems such as PXI simulate many of the physical signals – effectively recreating the phys-ical environment of the ADAS sensor or ECU. Note in Figure 5 that synchronization is acritical requirement of the PXI modules – because all of these signals have to be preciselysimulated in parallel, as the ECU needs the radar, V2X and camera signal to arrive at thesame time in a real-world vehicle to process and understand the scenario and act accord-ingly.

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Figure 5: Test Configuration for ADAS ECM

Using HIL test techniques based on a platform, engineers are able to simulate a virtuallyunlimited duration of “driving time” and achieve greater test coverage to better understandhow the embedded software performs in a wide range of situations. As a result of simulat-ing driving conditions in the lab environment, engineers are able to identify critical designflaws much earlier in the design process. For example, at Audi AG, the radar team recentlyadopted a PXI-based system radar simulation. According to project lead Niels Koch, radarhardware-in-the-loop simulation enabled them to “simulate ten years of sensor environ-ments within few weeks.” Going forward, as sensors continue to escalate in complexity – HILtesting is an absolute requirement as a method to validate virtually unlimited driving timevery quickly and therefore reduce time to market (fig. 5).

SummaryFifty years ago, a front-mounted radar system that notified the driver of oncoming trafficwas a gimmick. Five years from now, it will be the difference between life and death forpassengers in autonomous vehicles. Given the impending safety and regulatory considera-tions of this technology, engineers will utilize HIL test techniques to literally simulate bil-lions of miles of driving.

Advanced systems like autonomous vehicles are quickly re-writing the rules for how testand measurement equipment vendors must design instrumentation. In the past, test soft-ware was merely a mechanism to communicate a measurement result or measure a voltage.Going forward, test software is the technology that allows engineers to construct increas-ingly complex measurement systems capable of characterizing everything from the simplestRF component to comprehensive autonomous vehicle simulation. As a result, softwareremains a key investment area for test equipment vendors – and the ability to differentiateproducts with software will ultimately define the winners and losers in the industry.