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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.

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.

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.

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.

Hardware-in-the-loop (HIL) simulation is now a standard component in the vehicle development process as a method for testing electronic control unit (ECU) software. HIL simulation is used for all aspects of development, naturally including safety-relevant functions and systems. This applies to all test tasks (from function testing to release tests, testing a single ECU or an ECU network, and so on) and also to different vehicle domains: The drivetrain, vehicle dynamics, driver assistance systems, interior/comfort systems and infotainment are all tested by HIL simulation. At the same time, modern vehicles feature more and more safety-related systems such as Adaptive Cruise Control, Electronic Stability Program, Power Assisted Steering, and Integrated Chassis Management.

Hardware-in-the-loop (HIL) simulation in the development and test process of vehicle dynamics controllers requires a real-time tractor-trailer simulation model. The hitch coupling must be numerically stable to ensure real-time simulation for various driving maneuvers, particularly at the vehicle's handling limits. This paper presents a robust implementation of tractor-trailer coupling. The equation of motion is formed using a novel formulation which is a combination of Jourdain's Principle and the Articulated Body Algorithm. The paper shows that a robust model for a real-time tractor-trailer simulation can be achieved with the proposed method. Moreover, the approach presented is suitable for modular modeling, is successfully implemented and can also be used as a basis for flexible system definition with an adjustable number of trailer axles.

Model-based development and autocoding have become common practice in the automotive industry over the past few years. The industry is using these methods to tackle a situation in which complexity is constantly growing and development times are constantly decreasing, while the safety requirements for the software stay the same or even increase. The debate is no longer whether these methods are useful, but rather on the conditions for achieving optimum results with them. From the experiences made during the last decade this paper shows some of the key factors helping to achieve success when introducing or extending the deployment of automatic code generation in a model-based design process.

The importance of electronics, especially software, has greatly increased over the last few years. Efforts to maintain a high level of software quality have made testing an important part of the development process. With the advent of model-based development, testing methods can be used not only on code level, but also on model level. Next to test execution itself, test development is seen as the most time- and cost-intensive part of the testing process. This paper outlines and classifies current approaches to model-based test development, with the aim of providing guidelines for test developers for choosing the method best suited to the type of system under test and the test objective.

The number of electrical and electronic components in modern vehicles is constantly growing. Increasingly, functionalities are being distributed across several electronic control units (ECUs). While suppliers themselves are responsible for ensuring that individual ECUs function properly, only the OEM can test distributed functions. Moreover, with the volume of testing steadily growing, automated sequences are absolutely essential. To test electronic networks in the vehicle, Ford Europe is using platform-based hardware-in-the-loop simulation with integrated failure insertion. The company is setting up a uniform, project-independent procedure, from standardized test definition to automated test sequences on a virtual vehicle, right through to structured evaluation.

Not only is the number of electronic control units (ECUs) in modern vehicles constantly increasing, the software of the ECUs is also becoming more complex. Both make testing a central task within the development of automotive electronics. Testing ECUs in real vehicles is time-consuming and costly, and comes very late in the automotive development process. It is therefore increasingly being replaced by laboratory tests using hardware-in-the-loop (HIL) simulation. While new software functions are still being developed or optimized, other functions are already undergoing certain tests, mostly on module level but also on system and integration level. To achieve the highest quality, testing must be done as early as possible within the development process. This paper describes the various test phases during the development of automotive electronics (from single function testing to network testing of all the ECUs of a vehicle).

This paper describes a new production code generator that meets both the requirements of code developers for efficient and reliable production code, as well as the desire of system engineers to establish a control design process based on simulation models that double as executable specifications for the ECU software. The production code generator supports automatic scaling, generates optimized fixed-point C code for microcontrollers like the Motorola 683xx, Siemens C16x, and Hitachi SH-2, and produces ASAP2 [1] calibration information. Benchmark results show that the autogenerated code can match or even exceed the efficiency of typical handwritten production code. Code quality is assured by design and by systematic, automatic, and extremely comprehensive test procedures.