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The development of perception functions for tomorrow’s automated vehicles is driven by enormous amounts of data: often exceeding a gigabyte per second and reaching into the terabytes per hour. Data is typically gathered by a fleet of dozens of mule vehicles which multiply the data generated into the hundreds of petabytes per year. Traditional methods for fueling data-driven development would record every bit of every second of a data logging drive on solid-state drives located on a PC in the vehicle. Recorded data must then be exported from these drives using an upload station which pushes to the data lake after arriving back at the garage. This paper considers different techniques for curating logged data.

Given the current proliferation of active safety features on new vehicles, especially for Advanced Driver Assistance Systems (ADAS) and Highly Automated Driving (HAD) technologies, it is evident that there is a need for testing methods beyond a vehicle level physical test. This paper will discuss the current state of the art in the industry for simulation-based verification and validation (V&V) testing methods. These will include, but are not limited to, "Hardware-in-the-Loop (HIL)", “Software-in-the-Loop (SIL)”, “Model-in-the-Loop (MIL)”, “Driver-in-the-Loop (DIL)”, and any other suitable combinations of the aforementioned (XIL). Aspects of the test processes and needed components for simulation will be addressed, detailing the scope of work needed for various types of testing. The paper will provide an overview of standardized test aspects, active safety software validation methods, recommended practices and standards.

This paper explores how to enhance your autonomous system (AS) testing capabilities and quality assurance using a completely automated hardware-in-the-loop (HIL) test environment that interfaces to or simulates autonomous sensor technology, such as cameras, radar, LIDAR, and other key technologies, such as GNSS/maps and V2X communication. The key to performing such real-time testing is the ability to stimulate the various electronic control units (ECUs)/sensors through closed-loop simulation of the vehicle, its environment, traffic, surroundings, etc., along with playback of captured sensor data and its synchronization with key vehicle bus and application data. The latest technologies are introduced, which allow for direct sensor data injection to ECUs/line replaceable units (LRUs) for test interaction and stimulus, in addition to dynamic, on-the-fly modification of sensor data streams. It will be shown how these techniques are integrated with current HIL systems.

This paper presents a demonstrator developed in the European CleanSky2 project MISSION (Modelling and Simulation Tools for Systems Integration on Aircraft). Its scope is the development towards a seamless integrated, interconnected toolchain enabling more efficient processes with less rework time in todays, highly collaborative aerospace domain design applications. The demonstration described here, consists of an open, modular and multitool platform implementation, using specific techniques to achieve fully traceable (early stage) requirements verification by virtual testing. The most promising approach is a model based integration along the whole process from requirements definition to the verified, integrated (and certified) system. Extending previous publications in this series, the paper introduces the motivation and briefly describes the technical background and a potential implementation of a workflow suitable for that target.

In the aerospace industry, methods for virtual testing cover an increasing range of test executions carried out during the development and test process of avionics systems. Over the last years, most companies have focused on questions regarding the evaluation and implementation of methods for virtual testing. However, it has become more and more important to seamlessly integrate virtual testing into the overall development process. For instance, a company’s test strategy might stipulate a combination of different methods, such as SIL and HIL simulation, in order to benefit from the advantages of both in the same test process. In this case, efforts concentrate on the optimization of the overall process, from test specification to test execution, as well as the test result evaluation and its alignment with methods for virtual testing.

Hybrid test systems are gaining more and more significance in the aerospace industry. At the heart of these systems is a standardized communication infrastructure. There are many challenges when designing the communication infrastructure. For example, it requires very specific knowledge to boot a hybrid system, manage its configuration process, and start and stop the execution of applications, such as simulations, panels or recorders. Likewise, when testers use a heterogeneous test environment, they cannot commit themselves too much to every single test means and its special characteristics. Nevertheless, testers must always be able to monitor and control every test system. This means, they must be able to determine the current overall system status and the current status of its components and parts. Examples for this are hardware components, such as real-time processors and I/O boards, as well as software applications, such as real-time simulations models on the test system.

Multi-physics interactions between structural, electrical, thermal, or hydraulic components and the high level of system integration, characteristic of new aircraft designs, is increasing the complexity of both design and verification processes. Therefore the availability of tools, supporting integrated modelling, simulation, optimization and testing across all stages of aircraft design remains a critical challenge. This paper presents some results of the project MISSION (Modelling and Simulation Tools for Systems Integration on Aircraft). It is a collaborative task being developed under the European Union Clean Sky 2 Program, which is a public-private partnership bringing together aeronautics industrial leaders and public research organizations based in Europe. The first levels of integration of different models and tools proposed in the MISSION framework will be presented, along with simulation results.

The complexity of automobile powertrains grows continuously. At the same time, development time and budget are limited. Shifting development tasks to earlier phases (frontloading) increases the efficiency by utilizing test benches instead of prototype vehicles (road-to-rig approach). Early system verification of powertrain components requires a closed-loop coupling to real-time simulation models, comparable to hardware-in-the-loop testing (HiL). The international research project Advanced Co-Simulation Open System Architecture (ACOSAR) has the goal to develop a non-proprietary communication architecture between real-time and non-real-time systems in order to speed up the commissioning process and to decrease the monetary effort for testing and validation. One major outcome will be a generic interface for coupling different simulation tools and real-time systems (e.g. HiL simulators or test benches).

Increasing diagnosis capabilities in modern engine electronic control units (ECUs), especially in the exhaust path, in terms of emission and engine aftertreatment control utilize on-board NOx prediction models. Nowadays it is an established approach at hardware-in-theloop (HIL) test benches to replicate the engine's steady-state NOx emissions on the basis of stationary engine data. However, this method might be unsuitable for internal ECU plausibility checks and ECU test conditions based on dynamic engine operations. Examples of proven methods for modeling the engine behavior in HIL system applications are so-called mean value engine models (MVEMs) and crank-angle-synchronous (in-cylinder) models. Of these two, only the in-cylinder model replicates the engine’s inner combustion process at each time step and can therefore be used for chemical-based emission simulation, because the formation of the relevant gas species is caused by the inner combustion states.

The application of a communication infrastructure for hybrid test systems is currently a topic in the aerospace industry, as also in other industries. One main reason is flexibility. Future laboratory tests means (LTMs) need to be easier to exchange and reuse than they are today. They may originate from different suppliers and parts of them may need to fulfill special requirements and thus be based on dedicated technologies. The desired exchangeability needs to be achieved although suppliers employ different technologies with regard to specific needs. To achieve interoperability, a standardized transport mechanism between test systems is required. Designing such a mechanism poses a challenge as there are several different types of data that have to be exchanged. Simulation data is a prominent example. It has to be handled differently than control data, for example. No one technique or technology fits perfectly for all types of data.

This paper presents an overview of a project called “Modelling and Simulation Tools for Systems Integration on Aircraft (MISSION)”. This is a collaborative project being developed under the European Union Clean Sky 2 Program, a public-private partnership bringing together aeronautics industrial leaders and public research organizations based in Europe. The provision of integrated modeling, simulation, and optimization tools to effectively support all stages of aircraft design remains a critical challenge in the Aerospace industry. In particular the high level of system integration that is characteristic of new aircraft designs is dramatically increasing the complexity of both design and verification. Simultaneously, the multi-physics interactions between structural, electrical, thermal, and hydraulic components have become more significant as the systems become increasingly interconnected.

Increasing productivity along the development and verification process of safety-related projects is an important aspect in today’s technological developments, which need to be ever more efficient. The increase of productivity can be achieved by improving the usability of software tools and decreasing the effort of qualifying the software tool for a safety-related project. For safety-critical systems, the output of software tools has to be verified in order to ensure the tools’ suitability for safety-relevant applications. Verification is particularly important for test automation tools that are used to run hardware-in-the-loop (HIL) tests of safety-related software automatically 24/7. This qualification of software tools requires advanced knowledge and effort. This problem can be solved if a tool is suitable for developing safety-related software. This paper explains how this can be achieved for a COTS test automation tool.

Advanced driver assistance systems (ADAS) are becoming increasingly important for today's commercial vehicles. It is therefore crucial that different ADAS functionalities interact seamlessly with existing electronic control unit (ECU) networks. For example, autonomous emergency braking (AEB) systems directly influence the brake ECU and engine control. It has already become impossible to reliably validate this growing interconnectedness of control interventions in vehicle behavior with prototype vehicles alone. The relevant tests must be brought into the lab at an earlier development stage to evaluate ECU interaction automatically. This paper presents an approach for using hardware-in-the-loop (HIL) simulation to validate ECU networks for extremely diverse ADAS scenarios, while taking into account real sensor data. In a laboratory environment, the sensor systems based on radars, cameras, and maps are stimulated realistically with a combination of simulation and animation.

Hardware-in-the-loop (HIL) testing is indispensable in the software development process for control units and has been an integral part of the software development process for years. Large HIL systems for integration tests are used to test the correct behavior of distributed functions and the communication between the control units. The vast development programs that are involved require building duplicates of such test systems or parts of them, due to the fact that the tasks are distributed between different companies or different departments within a company. However, there is an alternative to duplicating a test system. Instead of using a cloned system, coupling HIL systems over large distances is an alternate approach. This paper presents what requirements this coupling must fulfill and and describes a path-breaking method to fulfill them. In addition, results of an implementation are shown.

To make the development of complex aircraft systems manageable and economical, tests must be performed as early as possible in the development process. The test goals are already set in advance before the first hardware for the ECUs exists, to be able to make statements about the system functions or possible malfunctions. This paper describes the requirements on and solutions for test systems for ECUs that arise from these goals. It especially focuses on how a seamless workflow and consistent use of test systems and necessary software tools can be achieved, from the virtual test of ECUs, which exist only as models, up to the test of real hardware. This will be shown in connection with a scalable, fully software-configurable hardware-in-the-loop (HIL) technology. The paper also covers the seamless use of software tools that are required for HIL testing throughout the different test phases, enabling the reuse of work products throughout the test phases.

In the last few years, we have seen a tremendous increase in the rise in product complexity due to advances in technology and aircraft system functionality enhancement. The Model-based Design (MBD) process has helped manage the complexity of these systems while making product development faster by bringing more effective tools and methods to the entire process. Developing software using MBD has required extensive, sophisticated tool-chains that allow for efficient rapid controls prototyping, automatic code generation, and advanced validation and verification techniques using model-in-the-loop (MIL), software-in-the-loop (SIL), and hardware-in-the-loop (HIL) for both component testing and integration testing. However, the MBD process leads to generation of large volumes of data artifacts and work-products throughout the V-Cycle.

Model-based development is the established way of developing embedded control algorithms, especially for safety-critical applications. The aim is to improve development efficiency and safety by developing the software at a high abstraction level (the model) and by generating the implementation (the C code) automatically from the model. Although model-based development focuses on the models themselves, downstream artifacts such as source code or executable object code have to be considered in the verification stage. Safety standards such as ISO 26262 require upper bounds to be determined for the required storage space or the execution time of real-time tasks, and the absence of run-time errors to be demonstrated. Static analysis tools are available which work at the code level and can prove the absence of such errors. However, the connection to the model level has to be explicitly established.

Embedded software in the car is becoming increasingly complex due to the growing number of software-based controller functions and the increasing complexity of the software itself. Model-based development with Simulink combined with TargetLink for automatic code generation helps significantly to improve the quality of the embedded software. The development of large-scale Simulink models in distributed teams is a challenging task, especially when developing safety-critical software that must fulfill requirements stated in the ISO 26262 [1] safety standard. In practice, many questions on how to avoid the pitfalls of distributed model-based development remain open, such as how to define an appropriate model architecture, handle model complexity, and achieve compliance with ISO 26262. The intent of this paper is threefold. Firstly, we summarize those requirements of ISO 26262 that are relevant for developing complex software in a distributed environment.

It is not news anymore when somebody talks about increasing software content in today's vehicles, transportation systems and machinery. The software content and complexity has grown so tremendously and rapidly that even the most advanced product/software development techniques leave more to desire in view of evolving product life-cycles, feature content and need for development efficiency. Model-Based Design (MBD) techniques and V-Cycle based development processes address the significant need for managing complexity, and to some extent, efficiency in product development. Further efficiency in the development process can be achieved by enabling virtual validation of software components. The virtual validation environment for software not only has the ability to run the software component as a standalone unit for performance validation, but is also extended to the validation of the performance of the entire embedded software of an ECU, multiple ECUs and the entire system.

The Model-Based Development (MBD) process has been the key enabler of technical advancement. MBD helps manage complexity, while making product development faster by bringing clarity and transparency to the entire product development process, specifically software components. Developing software using MBD has required extensive, sophisticated toolchains, like the ones provided by dSPACE, that allow for efficient rapid controls prototyping, automatic code generation, and advanced validation and verification techniques with hardware-in-the-loop (HIL) test systems. MBD is an efficient iterative process that allows engineers to improve quality and deliver on demanding needs of product variants in the current competitive environment. However, the MBD process described commonly using the ‘V-Cycle’ diagram leads to the generation of large volumes of data artifacts and work products. The iterative process, variants and versions of these artifacts lead to even larger amounts of data.