The safety and reliability of autonomous driving technology have become pivotal challenges within the modern automotive industry. Ensuring the stable operation of autonomous vehicles in unpredictable situations is essential for safety. Rigorous testing and validation across various scenarios are imperative. In this light, adherence to the SOTIF (Safety Of The Intended Functionality) standard, which aims to minimize risks and secure safety in autonomous driving technology, is increasingly crucial. This session will explore the advanced features of MORAI's autonomous driving simulation platform and will provide insights into strategies for complying with SOTIF

What the audience will learn

• Grasp the crucial role of simulation in bolstering the safety and efficiency of autonomous driving systems.
• Acknowledge the importance of verifying the safety of autonomous driving systems, along with current trends in global safety standards.
• Delve into the SOTIF (Safety Of The Intended Functionality) standard and how it impacts autonomous driving technologies.
• Learn how thorough testing and validation can ensure consistent operation of autonomous vehicles in unforeseen situations.
• Discover practical strategies for meeting and implementing the SOTIF standard during the development and testing phases of autonomous vehicles.

The OTX standard (ISO 13209) is gaining ground in the automotive field. As test sequences in OTX are often developed generically for different control units or even model series, they must also be tested accordingly using regression tests. The use of a simulator for vehicle diagnostics can greatly increase the efficiency of development and quality assurance in this area. In the talk, we will discuss the usage and benefits of diagnostic simulator for development of OTX based diagnostic sequences on the concrete examples.

What the audience will learn

• Vehicle diagnostics
• OTX based diagnostic tester
• Diagnostic simulators

Digital twins have emerged as an innovative and powerful approach in engineering processes across various industries. A major challenge is to provide the operational and execution infrastructure for digital twins on the system level, as they comprise different types of analysis and control components. This presentation addresses the challenges and solutions for creating and operating digital twins for automotive driving functions in practice. The validation platform FERAL is introduced, which enables the construction of digital twins through virtual prototypes and virtual test scenarios by coupling simulation models and tools, existing code and virtual hardware platforms. Results from practical applications are shown.

What the audience will learn

• The benefits and challenges of introducing virtual validation for automotive driving functions
• Technical assets and tools that address the challenges and obstacles for virtual validation in automotive projects
• How digital twins are constructed by coupling simulation models and tools, existing code and virtual hardware platforms
• How traceability between engineering assets (e.g. requirements, models, validation scenarios and results) is achieved
• Examples and results from projects with automotive companies

Due to the high natural frequencies of electric motors, it is necessary for the bases of test equipment to be very rigid so that resonance frequencies do not distort the recording of test results. Cast iron and welded steel constructions reach their limits here. The presentation describes the use of a solid, cement-bonded mineral casting (ultra-high-performance concrete or UHPC) with a modulus of elasticity of 80,000MPa for testing tables and benches.

What the audience will learn

• Use of UHPC for test benches
• Design of machine elements with UHPC
• Analyses of machine elements with UHPC

In the context of e-mobility applications trending rapidly towards more and more powerful drive systems, two main issues can be identified regarding appropriate facilities to measure, test and record drive systems: - The boundary conditions of installation space for testing of complete e-axles is getting more and more difficult because of high integration of e-drives. - Integration of measurement technology (torque cells) has to be adopted to high speeds. At a certain point you can either use specific measurement devices or - much easier - use the right couplings. - Examples of integrated central shaft cooling are shown.

What the audience will learn

• Testing of most powerful e-drives and e-axles
• Modular test rigs and retrofit of existing facilities
• Intelligent integration of measurement systems
• Newly developped high-speed joint shaft for test rig applications

The value of scenario data in autonomous driving development is vital. The source of traditional scenario data mainly relies on real vehicle data recording. Data costs remain high and scenario coverage is very limited. Now we can use digital twin technology to transform real road data into virtual scenario models, furthermore effectively generalize the behavior of traffic participants to achieve exponential growth in scenario data generation. The high-precision data acquisition, digital twin, simulation and back-injection tool chain launched by ZD Automotive could create high-coverage, high quality data at low cost, and facilitate autonomous driving to efficiently closed the data loop.

What the audience will learn

• Traditional Data Limits: Relies on costly real vehicle recordings with limited scenario coverage.
• Digital Twin and Generated Reality Innovation: Transforms road data into virtual models, exponentially increasing scenario data.
• Efficient Data Solution: Offers high-coverage, quality data at low cost, closing the autonomous driving data loop.

The safety and performance of high-voltage components is significantly affected by temperature. In particular, HV batteries are susceptible to temperature effects. Therefore, temperatures have to be monitored extensively as well as with high density inside HV components in the development process. The question now is, how that many temperature channels can be realized without sacrificing the components sealing and influencing their overall behavior. In this presentation Dipl.-Ing. Timo Eich discloses a revolutionary approach to precisely measure hundreds of temperatures inside HV components with pin-point accuracy utilizing a single digital, high-end temperature measurement system with minimal-invasive character.

What the audience will learn

• Why classical instrumentation with thermocouples or RTDs fails when lots of measurement points are demanded inside of HV components
• Why the verification of thermal propagation models demands high precision positioning of sensors and how to archive precise positioning
• How over 500 sensors can be acquired with only one single cable led out of the device under test
• How an accuracy of temperature measurement down to .1 Kelvin can be reached
• How signal noise and interference are eliminated by the use of a digital measurement system

In today's engineering and testing landscape, the concept of open ecosystems has emerged as a pivotal force driving innovation and digitalization across industries. Within this paradigm, dynamic data acquisition stands as a cornerstone, enabling organizations to harness the power of real-time information for unprecedented insights and agility. Delving into the interplay between open ecosystems and IIoT / Edge / Cloud Computing, we are exploring the artistry of what's possible. From revolutionizing decision-making processes to fostering collaborative innovation, join us on a journey through the transformative potential of these symbiotic elements in shaping the future of interdisciplinary engineering and testing.

What the audience will learn

• How data ecosystems may change testing processes
• The typical scope of applications
• How to become a part of an open data ecosystem

Regulators from the EU and around the world are constantly updating the legal requirements for automotive manufacturers, particularly regarding safety and the environment. The advent of electric vehicles (EVs), advanced driver assistance systems (ADAS), and autonomous vehicles are driving the development of new standards and pose new challenges for engineers. This presentation focuses on strategies for flexible performance indicators as regulations evolve. It highlights the value of edge analytics, event-based data acquisition, video, and customizable dashboards for maintaining compliance. And it includes a demonstration showing an example of how some manufacturers are using these tools and processes today.

What the audience will learn

• Evolving regulations mean that manufacturers need flexible vehicle data acquisition strategies.
• Edge analytics provides rapid information about vehicle performance and regulatory compliance.
• Event-based data acquisition prevents data overload and provides engineers with fast access to relevant information.
• Video monitoring, aided by AI, speeds up troubleshooting and ensures test coverage.
• Customizable dashboards make it easy to track compliance and test coverage, while also aiding in root-cause analysis.

The automated vehicle (AV) industry faces a significant challenge: How can it effectively identify and assess the safety risks associated with AVs? A wide range of test setups, driving scenarios, and simulation models offer valuable testing elements for validation, yet their effective application remains unclear. We discuss how artificial intelligence and machine learning methods can link the crucial testing elements purposively. Consequently, the validation of automated vehicles becomes data-driven, and tests traceably contribute to safety validation and the AV release.

What the audience will learn

• With the “multi-pillar approach”, the UN recommends combining virtual and real tests to validate the safety of automated vehicles.
• Through active learning, scalable virtual test setups provide a broad scenario coverage that can be characterized by probabilistic models.
• Based on the knowledge gained virtually, transfer learning allows real tests to be parameterized in a targeted manner.
• The findings from the real tests are in turn used to improve the models used in the virtual tests.
• Used iteratively, the data-driven linkage of virtual and real tests uncovers critical scenarios, and test results become increasingly reliable.