创新是未来的引擎

Test execution planning (TAP) and fault pattern detection (FER)

TAPFER研究项目于2020年4月正式启动，目前，我们和慕尼黑工业大学（Technical University of Munich）正在一起研究开发一种通过测试结果分析和动态调整测试执行计划来检测故障模式的方法。

赞助商

Federal Ministry for Economic Affairs and Energy (BMWi)

项目合作伙伴

• TraceTronic GmbH
• Technical University of Munich
• BMW (associate)
• BOSCH (associate)

项目时间

April 2020 to April 2023

详情

As part of the project, the time-consuming, manual fault identification and diagnosis is supported by a method of independent learning that can recognize fault patterns and gradually learn how to deal with underlying errors from users. Test result reports and data recorded at runtime are used to train a system to automatically analyze incorrectly executed test scripts. The manual execution planning of test scripts on test resources can at times be time-consuming and often suboptimal due to lack of time or lack of information. This is replaced by dynamic test execution planning. In addition to the already available information, it also refers to knowledge gained during fault pattern detection at runtime in order to minimize downtimes and thus increase the productivity of test resources. Finally, test execution planning also includes the distribution of test orders to the available test resources taking the given boundary conditions into account.

网络化和自动化驾驶功能的通用开发和安全方法

高度自动化的驾驶功能（HAD）不能再用当前已有的功能许可概念和方法进行测试，或批准用于道路交通中。在本项目中，我们开发了方法和软件，以提高从测试场模拟到公共交通领域不同测试级别之间的耦合度。

赞助商

BMVI (Federal Ministry of Transport and Digital Infrastructure)

项目合作伙伴

• TraceTronic GmbH
• HTW Dresden
• IAV GmbH

项目时间

2017 年 7 月至 2020 年 6 月

详情

At the core of the project is a close coupling between different levels of simulations as well as the inclusion of as many measurements as possible from real test drives designed for the assessment and approval of highly automated driving functions. The aim of this new simulation approach is not only to describe in detail and test the driving maneuvers under investigation, but also to realize a description of the driving task at the most general level possible in the digital environment and validate it on the basis of real test drives. Both the knock-on effects ensuing and the transferability of the simulation results thus achieved will enable any desired number of simulated safety drives for highly automated driving functions. For this purpose, tools and methods will be developed within the framework of this project and tested for their viability on selected maneuvers.

As the consortium brings together the expertise of different topics, this will allow extensive measuring campaigns to be effectively conducted in order to evaluate the concept. This will lay the foundation for a continual extension to all areas of highly automated driving.

基于智能知识的高度自动化驾驶辅助系统

传统的测试概念和方法不足以考虑到更高水平的自动驾驶功能的迅速发展。IWAHAF 项目开发了基于知识的方法和工具来支持和自动化汽车领域的复杂安全活动。

赞助商

BMBF (Federal Ministry for Education and Research)

项目合作伙伴

• TraceTronic GmbH
• TU Dresden
• AUDI (associated)
• BMW (associated)
• BOSCH (associated)

项目时间

2017 年 1 月至 2020 年 6 月

详情

The automotive industry is currently dominated by the trends towards digitalization, electrification and networking – and will remain so in the future. Systems for highly automated driving are already reality today and – according to current projections – their further development will result in fully automated driving by 2030. The autonomous actions of vehicles acting according to the situation will result in entirely new operational and error scenarios and present functional safety with major challenges. Current expert opinions conclude that current testing concepts are no longer sufficient to safeguard the new system of autonomous driving. To counter this development, there is an urgent demand for sophisticated methods, which would enable testing activities to be adapted to the new requirements and to undergo further automation.

In order to support developers and testers in the planning, configuration, conducting and evaluation of tests and analyses, various semantic technologies will be used. For the purpose of further increasing data and process quality, formalized knowledge will be used to automate core processes, such as data collection and test configurations.

The planned results obtained by the project partners will complement each other to form an innovative, application-specific, knowledge-based assistance system, which will be integrated via various interfaces into existing tools of the project partners as well as tools of other users, and support complex safeguarding activities. The focus of the application lies primarily on the data analysis for the "Interactive testing on the vehicle" pilot application in the course of safeguarding systems for highly automated driving.

We will further develop the results into a marketable knowledge-based assistance system to support the analysis of test and mass data – particularly for the safeguarding of highly automated driving, while also providing services in this sector. The TU Dresden will expand its expertise in the fields of data analysis for software-intensive embedded systems and semantic technologies and make it available in the form of publications and courses.

基于场景的自动驾驶功能检测平台

为了在整个系统生命周期中确保自动化驾驶功能的功能性和产品安全性，需要新型的测试与验证理念。SePIA 项目首次汇总了大量的真实场景、相关的事故数据、大量的车辆总线和视频数据，并用一种生成插值场景的新方法对其进行补充。

赞助商

SAB (Development Bank of Saxony)

项目合作伙伴

• TraceTronic GmbH
• FSD Fahrzeugsystemdaten GmbH
• Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI
• TU Dresden
• Verkehrsunfallforschung an der TU Dresden GmbH

项目时间

2017 年 6 月至 2020 年 5 月

详情

A large number of real driving scenarios, associated accident data and information from assessments on accident reconstruction as well as extensive vehicle bus and video data are used for the development of a platform for scenario-based testing and inspection. The data taken from various technological areas is for the first time aggregated and extended by means of a new methodology for generating supplementary, interpolated scenarios. This data can be categorized by criticality and representativeness and thereby exploited. A combining of the resulting scenarios with additional environmental information, such as traffic flow and weather, is possible via open interfaces. The platform will be tested and demonstrated in a SiL/HiL application environment. In production use, it will be able to effectively promote both the rollout process and the ongoing functional testing of automated vehicles in Germany.

We support the creation of the requirements specification as well as the development of the platform. Furthermore, we head the designing of description tools for physically realistic scenarios and cooperate in developing the SiL/HiL application environment.

为批准高度自动化的驾驶功能，建立公认的质量评定标准、工具和方法以及场景和情况的项目

PEGASUS 项目的内容是开发基于场景的高度自动化车辆测试统一的方法和工具，以应对其批准过程中所遇到的技术、法律和社会挑战。

赞助商

BMWi (Federal Ministry for Economic Affairs and Energy)

项目合作伙伴

• TraceTronic GmbH
• Adam Opel AG
• AUDI AG
• BMW AG
• Daimler AG
• Volkswagen AG
• A.D.C. Automotive Distance Control Systems GmbH
• Continental Teves AG
• Robert Bosch GmbH
• TÜV SÜD GmbH
• Forschungsgesellschaft Kraftfahrwesen mbH Aachen
• iMAR Navigation GmbH
• IPG Automotive GmbH
• QTronic GmbH
• VIRES Simulationstechnologie GmbH
• DLR e.V. – Institut für Verkehrssystemtechnik
• TU Darmstadt – Fachgebiet Fahrzeugtechnik

项目时间

2016 年 1 月至 2019 年 6 月

网站

http://www.pegasus-projekt.info/de/

详情

Automated driving is a key issue in the development of the automotive sector. The highest quality and safety requirements exist in relation to the approval of highly automated vehicle functions. Present procedure models and testing methods are not adequate for this purpose. In order to automatically check compliance with these requirements, there is a great need for appropriate quality criteria, validation methods and software tools.

The purpose of the PEGASUS project was to develop generally accepted methods and tools for testing highly automated vehicle functions. This prepares the approval of highly automated vehicles for the market. To this end, the first step is to conduct preliminary work to determine the performance level of HAD functions (e.g. the definition of an "average driver" as a benchmark), from which criteria relating to the standard and quality can be derived. Once these were in place, testing methods, testing catalogs as well as testing instruments for simulation, laboratory and test bed-based concepts were developed in the project. PEGASUS was thereby intended to provide a reference for the efficient validation of highly automated driving functions.

Within the framework of this project, we developed methods and software components for tool chains to validate highly automated driving functions. For the resulting requirements, new methods are developed for use in test automation, trace analysis and test evaluation and then realized as software demonstrators. In order to take account of the large number of variants and the high safety requirements on highly automated vehicle functions, existing trace analysis approaches are continued and new methods created for the automated evaluation of the large and complex quantities of simulation and test driving data thereby obtained.

On completion of the project, we will use the results to expand our existing product portfolio to include new features, libraries and tools for the testing and safeguarding of highly automated vehicle functions. The solutions will be integrated into the tool chains of our project partners for use there.

用于跟踪分析的语义数据仓库

SeDaWaT 项目包括了用于分析大量汽车测量数据的开发方法和软件工具。

赞助商

BMBF (Federal Ministry for Education and Research)

项目合作伙伴

• TraceTronic GmbH
• TU Dresden
• Audi (associated)
• BMW (associated)

项目时间

2014 年 1 月至 2016 年 6 月

详情

The trend to constantly new functional requirements in the automotive sector, brought about by growing customer demands, statutory provisions and new guidelines, is resulting in rapid growth in electronic components and software-intensive systems in vehicles. The increasing complexity and variety of the systems developed is also accompanied by a continual growth in the scope of analysis. The tool-based analysis of measurement data (known as trace data) is a key instrument for testing and safeguarding in all phases of the development of ECU software in the automotive sector. In order to counter this development, new methods are required, with which relevant data and information components can be systematically collected, aggregated and subjected to a highly automated analysis.

The aim of the SeDaWaT research project was to create a software engineering framework. Integrated and intelligent data and analysis management would then be used to greatly simplify the complex evaluation and verification of extremely large trace data sets in the automotive sector. The technological core consists of a new type of database – known as the semantic analytic data warehouse. This integrates a wide range of data sources and formats, thereby aggregating all the relevant data obtained from test automation and trace analysis. A scalable, intuitive operating concept and a visualization of analytical results are intended to optimally support the ability of the users and prevent human errors. In addition to the usual mechanisms, such as searching, sorting and selecting, for convenient access to data volumes, the Semantic Analytic Data Warehouse is intended to be significantly superior in functionality to classical relational databases. For this purpose, it additionally provides domain-specific mechanisms and interfaces for creating and performing complex analyses.

With the results from this project, we are planning to establish a new data warehouse solution for the evaluation of complex mass data obtained from the development of automotive ECU software and provide services in this field. The TU Dresden will expand its expertise in the fields of data analysis for software-intensive embedded systems and make it available in the form of publications and courses.

高级模型驱动的跟踪分析

emTrace研究项目包括开发用于跟踪分析的新概念、方法和软件工具。

赞助商

BMBF (Federal Ministry for Education and Research)

项目合作伙伴

• TraceTronic GmbH
• TU Dresden
• Audi (associated)
• BMW (associated)

项目时间

2011 年 6 月至 2013 年 11 月

详情

Quality assurance for networked mechatronic vehicle systems is a key topic in the automotive sector. Rising safety and comfort demands, an increasing variety of versions as well as the constantly growing complexity of the hardware and software used, open up new problems in relation to ensuring the reliability and complying with statutory provisions across the entire vehicle life cycle.

The currently used vehicle onboard networks are connected reactive realtime systems, in which complex functions are distributed across several control units. Due the lack of determinism in the overall system, it is not possible to detect or exclude all the failure mechanisms here – despite extensive automated testing and ECU-internal diagnostic functions.

The runtime monitoring of the systems with subsequent evaluation of the measurement data recorded (trace analysis) offers one possibility of increasing the safeguarding level.

The aim of the emTrace project was to significantly increase the effectiveness of trace analyses by extensive improvements in the fields of expressiveness, analysis performance as well as user interaction and support. The key concepts of "model-driven design, parallelization and distribution" as well as "visual analysis" were to be adapted and used for the first time. This would thereby enable the trace analysis to be developed into an expressive, performant and user-friendly standard tool for quality assurance.

On the completion of the project, the algorithms and methods developed were fed into existing tools of the project partners. These included the ECU-TEST test automation environment and the TRACE CHECK analysis framework.

新能源汽车通信

CECC 项目的目的是降低机动车辆的能源消耗。

赞助商

SAB (Development Bank of Saxony)

项目合作伙伴

• TraceTronic GmbH
• TU Dresden
• DMOS GmbH
• Dresden Elektronik Ingenieurtechnik GmbH
• HTW Dresden

项目时间

2010 年 10 月至 2013 年 3 月

详情

In order to reduce the energy consumption of vehicles, a radio and information system was developed to provide information on other vehicles (location, distance to the next vehicle, speed) and infrastructure (phase duration of traffic lights, roads) in an energy-efficient manner. The energetically favorable driving profiles thereby derivable were created as concept applications for driver assistance systems. This includes approaching traffic light junctions (minimization of the time stopped at the traffic lights by means of red-phase prediction as well as optimization of motor start/stop functions), speed recommendations as well as stop-and-go assistance. For this purpose, one of the things we developed was a framework for generating energy-optimized operating strategies.

测试和诊断中的扩展跟踪分析

TraceSys 项目开发了用于分析汽车测量数据的方法和软件工具。在德国联邦教育及研究部的支持下，我们于 2008 年 2 月启动了该项目。目标是定义测试框架的原型开发，该框架通过分析跟踪信息来评估整个系统生命周期中的测试和诊断数据。

赞助商

BMBF (Federal Ministry for Education and Research)

项目合作伙伴

• TraceTronic GmbH
• BMW AG (associated)
• GMT-TUD GmbH, Dresden
• FSD Fahrzeugsystemdaten GmbH (associated)
• TU Dresden

项目时间

2008 年 2 月至 2010 年 9 月

详情

In the course of testing embedded systems, a large number of time-based measurement data (known as traces) were created in the various test environments of the vehicle's lifecycle. Data was recorded for testing and diagnostic purposes from the modeling (MiL, model in the loop), via the ECU programming (SiL, software in the loop), ECU implementation as well as production and final testing (HiL, hardware in the loop) through to the general inspection. In the past, these traces had only been manually analyzed using rudimentary methods and tools. Within the framework of the TraceSys research project, we developed algorithms to automatically evaluate these series of measurement data and identify errors. These can be used across the entire lifecycle of the vehicle in order to efficiently evaluate stored data. Formal methods, which can build a first bridge to real software verification, were also used.

On completion of the project, the algorithms and methods developed were fed into the existing tools of the project partners, this including the ECU-TEST test automation environment.

自动系统的动态迁移和即时启动

OMSIS 项目的包括为工厂自动化和汽车终端测试领域的集成测试和仿真环境创建一个通畅的工具链。

赞助商

BMWi (Federal Ministry for Economic Affairs and Energy)

项目合作伙伴

• TraceTronic GmbH
• TU Dresden（项目协调方）
• Martin Luther University Halle-Wittenberg
• DUALIS GmbH IT Solution
• GWT-TUD GmbH
• Incontrol Enterprise Dynamics GmbH
• ITI - Gesellschaft für ingenieurtechnische Informationsverarbeitung mbH
• PEER Group GmbH
• Qimonda Dresden GmbH & Co. OHG
• SYSTEMA Systementwicklung Manfred Austen GmbH

项目时间

2008 年 4 月至 2011 年 1 月

详情

The migration or upgrading and renewal of existing automation systems during operation is a permanent process in modern companies. In this process, the operators are often faced with challenges, since long downtimes come with production stoppages, and the systems can therefore not be completely shut down.

In order to reduce downtimes to a minimum when upgrading, both the new hardware and the adapted software should be error-free and ready for immediate use on starting up again. This is only possible if the corresponding changes have been both reliably simulated and checked for correctness beforehand.

The aim of the OMSIS joint project was to create a comprehensive tool chain for an integrated testing and simulation environment. This tool chain was intended to improve and facilitate the simulation, monitoring, test-case generation and execution subtasks as well as diagnostics. Furthermore, it must relieve humans of the burden of routine tasks and thereby enable the efficient migration or start-up of automated systems.

We have supported this project as a specialist in the field of intuitive test-case specification and ECU verification for vehicle ECUs.