Keynote: Assisted and Automated Driving at Porsche

9:40–10:10

Since the introduction of Park Distance Control and adaptive cruise control in the mid-2000s, Porsche has integrated driver assistance and automated driving technologies into its product lines. This does not contradict the philosophy of a sports car—customers who like to drive themselves in appropriate traffic conditions expect driving to be made significantly easier in stressful, time-consuming situations such as traffic jams or busy parking spaces.

Although the general discussion focuses on the higher levels of automation from SAE Level 3 to Level 4, Level 1 and 2 systems will play a significant role for the next decade, at least, and represent the technological state of the art for most vehicles. In this environment, Porsche is focusing on increasing the performance and functionality of Level 1 and 2 driver assistance systems while developing programs that enable automated driving at Level 3 and 4. This offers the opportunity to build the necessary expertise in sensor technology, sensor fusion, planning and control, as well as the processes, methods, and tools required for the development, approval, and release of higher-level automated driving systems.

Systems engineering must be combined with approaches to processing very large amounts of data, while traditional random testing on the road must be transformed into a combination of virtual and systematic testing. Finally, a new end-to-end electronic architecture is required to ensure the seamless integration of the vehicle with the IT infrastructure.

Keynote: Process, Methods, and Tools: The Key Enablers for Software-Defined Vehicles

10:10–10:40

The notion of added value in the automotive industry is undergoing a shift. In a sector where hardware has been the main generator of profit for more than a century, a new rule now applies. Going forward, hardware will primarily form the platform on which software is later going to drive the vehicle.

Like smartphones, cars are increasingly turning into a technical platform where software is brought into play. To this end, the necessary requirements must be met: having fewer but more powerful ECUs, smart actuators, connectivity, and a service-oriented architecture where hardware is decoupled from software and driving functions are partitioned.

All these changes mean new requirements for the development of automotive software.

The tool landscape must be modern as well as up to date. Process, methods, and tools must be closely aligned and become a key enabler for the development of software-defined vehicles.

Keynote: Future of Engineering Design in the Age of AI

10:40–11:10

This keynote explores the transformative impact of AI on engineering design, highlighting its role in fostering innovation beyond traditional bounds. AI's capacity for generating novel solutions is not only enhancing efficiency but also redefining creativity and problem-solving in engineering. We are at a pivotal moment where the essence of design creativity intersects with the need for technological precision, marking a fundamental shift in the design process, the designer's role, and the tools used in engineering. This presentation will focus on three key transformations: the evolution of the design loop, the changing role of designers in an AI-driven world, and the advancement of engineering design tools. Together, these shifts represent a new era in engineering design, where AI acts as both a catalyst for innovation and a bridge between imagination and realization.

Navigating the Shift to Centralized E/E Architectures in SDVs

11:40–12:05

The automotive industry's move toward software-defined vehicles (SDVs) prompts a reevaluation of conventional E/E architectures. This presentation analyzes the transition to SDVs, focusing on the impact on the development and integration of advanced features on cross-domain target platforms. The discussion centers on the shift from distributed to centralized E/E architectures, which is essential for supporting ongoing feature enhancements and updates.

The FEV demonstrator project serves as a case study illustrating the application of a cross-domain central controller within a software-centric architecture. This project demonstrates the utility of service-oriented architectures and Model-Based Design workflows in creating scalable, adaptable vehicle systems that accommodate software updates post-production.

Participants will hear a technical perspective on current trends in SDV architecture, offering insights into how these developments are managed to maintain a competitive edge in the industry.

How the Software-Defined Vehicle Trend Is Transforming Development Processes

12:05–12:30

A large part of the future value of vehicles is going to be defined by software. While the automotive industry is adopting many development methodologies from the internet and mobile phone industry, it is important to consider safety and security as crucial requirements. Highly automated build and test systems—software factories—are a common practice, but what is necessary to make them ready for automotive? Frequent over-the-air updates are key to being able to react to security threats, but what is necessary to ensure that the safety of the vehicle is not compromised as a side effect? In this presentation, see how simulation and Model-Based Design can help in achieving the software quality and security that customers are expecting and integrating with automated build systems to enable the development speed and agility required to fully deliver the promise of software-defined vehicles.

Cybersecurity: Addressing ISO/SAE 21434 and UNECE with Model-Based Design

12:30–12:55

In the rapidly evolving landscape of the automotive industry, cybersecurity has transitioned from an optional enhancement to an absolute necessity. The intricate nature of securing automotive systems presents a multifaceted challenge, characterized by uncertainty and the need for continuous iteration under stringent time constraints. This is primarily due to the relentless occurrence of new cyber threats, necessitating persistent vigilance and recurring updates to the embedded systems.

This presentation showcases Model-Based Design as a way to alleviate the complexities inherent in automotive cybersecurity certification. Model-Based Design serves to maintain the integrity, completeness, and consistency of certification artifacts within a singular, cohesive engineering environment. Learn how to use System Composer™ and Simulink Fault Analyzer™ to conduct integrated security risk assessments, how Requirements Toolbox™ provides end-to-end traceability of security goals, how to use Control Systems Toolbox™ to implement countermeasures that can detect replay attacks, and how to efficiently create and qualify your security patches by leveraging change impact analysis, Embedded Coder^®, and static analysis.

End-to-End Framework from Cloud to SoC for SDV Development

12:55–13:20

With the split of hardware from software in automotive architectures, there will be a longer cadence for new hardware architecture, while software is released on much shorter cadence and able to cope with many generations of hardware in parallel. At system definition, one important aspect is evaluating whether a new software feature can be deployed and executed on an existing or new System On Chip (SoC).

For that reason, Elektrobit offers our customers a framework based on MATLAB^® and Simulink^® that allows them to:

• Analyze the feasibility of running a given use case on abstracted hardware
• Run a high-level simulation to derive functional and nonfunctional requirements
• Estimate and characterize system parameters like optimized sample rate of functions, MIPS, memory footprint, and power consumption
• Define the interfaces, including bandwidth requirement and integration of third-party software
• Perform an early integration of interfaces and building blocks
• Provide a test harness to verify production software

Furthermore, any framework must natively and seamlessly close the gap between simulation and virtual validation as well as real-world testing in a fully automated cloud environment. Therefore, we enable a shift left of the development through virtualization of the system design, covering the test of functional, nonfunctional, and integration requirements.

This presentation demonstrates the complete development flow enabling an efficient virtual design of automotive application, starting from the cloud down to testing on real hardware.

This framework complements Elektrobit’s end-to-end closed-loop environment in software and hardware for model-based application development. We streamline software-defined vehicle development by using MATLAB and Simulink for the exploration of innovative ideas and development of software functions, integration, and verification from cloud to SoC.

Accelerating Development of VCU Software in Iveco eDaily with Model-Based Design

14:50–15:10

The eDaily is Iveco Group's new electric light commercial vehicle, launched in 2023. At its core, the vehicle control unit (VCU) represents its brain, working as the center of a sophisticated star-based control system. The VCU oversees various aspects of the vehicle such as traction control, thermal systems, recharging processes, the entire HV domain, HMI components, and more. To keep full control over the vehicle software, Iveco developed the VCU application software internally, establishing a dedicated team of software engineers and defining related processes, methods, and tools.

This presentation illustrates how Iveco Group choose an approach based on Model-Based Design leveraging MATLAB^® products. By making use of libraries, data dictionaries, and referenced models, we designed a completely new software architecture to create a clear separation between the application software and the basic software, and to make the application software easily reusable over different projects. We reserved part of the software for safety monitors to achieve compliance with ISO 26262.

Safety-related software was qualified with Simulink Test™, Simulink Report Generator™, and Polyspace^®. By integrating SVN into our Simulink^® project, we designed a development process where multiple software engineers can work in parallel in an agile manner, achieving near-daily software releases. We created a virtual vehicle where our whole VCU application software can be integrated and tested. By using model variants, we made the virtual vehicle effortlessly convertible in the emulation engine of the hardware-in-the-loop testbench for our VCU. Finally, many of the actions needed to build and test the software have been automated via scripts to obtain an effective CI/CT pipeline.

Fault Injection Testing and Simulation-Based FMEA

15:10–15:30

This talk will demonstrate an approach to fault effect and safety analysis conducted through simulation. The focus will be on a methodology that allows for the injection of faults into a system model without necessitating any changes to the initial design. This technique is applicable to a variety of modeling environments and is particularly adept at handling faults that are either timed or conditionally triggered by the system's behavior.

Participants will gain insights into how to examine the impact of faults using simulation inspection tools to assess the robustness of their systems. The talk will also address the execution of comprehensive safety analyses, including the industry-standard failure mode and effects analysis (FMEA), by leveraging the detailed insights that simulation provides.

The session will further reveal strategies for establishing clear and formalized connections between system faults, associated hazards, and the logic for fault detection and mitigation. These strategies are crucial for creating a thorough safety analysis framework that can be integrated into the overall system design process.

How to Simulate and Test Heterogeneous AUTOSAR Software Compositions in Simulink

15:30–15:50

In our software at Continental Automotive, we have nearly 1,000 AUTOSAR software components (SWCs). These are a combination of handwritten C-code SWCs and Model-Based Design SWCs.

As we decided to use Model-Based Design as the standard development method for all AUTOSAR SWCs, we set up a project called Modular Software. The goal is to use one unified toolchain to develop and test both types of AUTOSAR SWCs.

In this talk, we describe how to simulate and test complex AUTOSAR software compositions that contain Model-Based Design and handwritten SWCs using one unified, ASPICE-conformal tooling.

We bring handwritten AUTOSAR SWCs and software compositions together into the Simulink^® environment, simulate it using software-in-the-loop mode and test the system using Simulink Test™.

As we bring the handwritten code into Simulink and set up the software testing using Simulink Test, we facilitate the transition from handwritten code to Model-Based Design, identify software integration issues, and enable software testing early in the process, reducing time and costs.

From Electrode to Pack: Simulate and Tune Fast Charge Profiles

16:35–16:55

The charging time of battery electric vehicles (BEVs) is crucial for consumer appeal, reducing range anxiety, and enhancing usability. However, fast charging can lead to faster battery wear and reduce the battery lifetime.

To hinder cell wear while obtaining optimal charging rates, various factors at the microscopic cell level must be considered. Furthermore, system level considerations like charger limits, thermal management design, control strategies, and manufacturing differences must be taken into account. All these requirements can be considered by calculating the fast charging profile, which ensures optimal charging time while reducing the risk of cell wear.

In this presentation, learn how to use Simscape Battery™ to set up an electrochemical model of a lithium-ion cell. Subsequently, you will discover how to extend this model to a parallel assembly and then a module while considering the thermal, electrical, and chemical behavior of the cells. The models will be employed to derive safe fast charge profiles for different charging scenarios.

How to Accelerate EV Software Release Cycles with AVL E-Motor Emulation Testbeds

16:55–17:15

The powertrain industry has traditionally relied on dynamometer testing for robust software releases. With the adoption of electric vehicles, it is essential to have short software release cycles including electrical safety and interoperability. While testing powertrains using internal combustion engines requires a rotating dynamometer, it is possible to test inverters for EV powertrains and the onboard charger at full power by using only electrical emulation of the electric motor and the battery. Such an approach allows all operating conditions to be tested, including driving scenarios, charging, electric faults, and even the interactions with other control units. Due to the need for high testing efficiency, frequent software updates can be carried out in a very short amount of time. The motor inverter testbed from AVL SET replaces the need for electric motors, dynamometers, and battery packs by using power electronics to emulate the electrical behavior of electric motors and battery packs with exceptional fidelity. As powertrain engineers typically use Model-Based Design to develop control designs and generate certified software code from Simulink^®, AVL SET has also developed an interface to Speedgoat test systems to provide a seamless real-time interface to MATLAB^® and Simulink. The model interface has a very low latency, which is crucial to emulate electrical systems as any delay would significantly disturb the model fidelity. As a result, engineers can use battery pack models from Simscape Battery™ or vehicle models from Powertrain Blockset™ when testing their motor inverter and controllers. In this session, you will learn about the E-Motor Emulator testbed and how to test an automotive inverter at full power for typical operation and fault conditions. We will later combine with Speedgoat test systems to emulate a battery pack using models from Simscape Battery when testing software in a fully powered inverter.

Design and Analyze a Battery Electric Vehicle with Thermal Management

17:15–17:40

In recent years, the shift towards sustainable mobility has become a pivotal focus within the automotive sector, largely in response to the stringent emission standards set by European legislation. Among the solutions explored, Battery Electric Vehicles (BEVs) stand out as a viable option for achieving these sustainability goals since they do not cause any local emissions. However, the adoption of BEVs is hampered by concerns over their limited range compared to traditional combustion engine vehicles, making range estimation a critical aspect of BEV development.

In this presentation you will learn how to build a virtual vehicle model of a BEV designed to estimate range and consumption. The virtual vehicle comprises models for powertrain, driveline, and thermal management. Following a brief presentation of the model, it will be used to highlight the impact of different parameters on vehicle consumption and range.

Developing Next-Generation Lidar with Model-Based Design

14:50–15:10

Model-Based Design has become a cornerstone of Valeo's development of automotive lidar systems, an essential component of its advanced driver assistance systems portfolio. This strategic approach is driven by the imperative to shorten market delivery times, expedite development cycles, meet escalating performance demands, and achieve cost-effective designs. This presentation highlights the effective application of Model-Based Design in the development of Valeo's lidar sensor motor-driven laser-shooting system. The process of generating a sensor's point cloud starts with laser shots driven by a motor, positioning the design as a critical juncture for the development teams. Among the technical challenges faced are ensuring azimuth precision, maintaining accuracy, and addressing missing shots in the point cloud. Additionally, adherence to ISO 26262 standards is required.

Attendees will gain insights into how MATLAB^® and Simulink^® streamline concept evaluation, algorithm design, and plant modeling. The algorithms developed include precise control, offline calibration, online corrections. Area and timing-optimized code is generated using HDL Coder™. Model testing is conducted with Simulink Test™, while HDL Verifier™ is employed to automatically repurpose model testbenches for verifying HDL code, ensuring a comprehensive and efficient development cycle.

AD/ADAS Country-Based Virtual Validation Using Real-World Data

15:10–15:30

Accurate 3D simulation models that can represent country-specific features are key to accelerating the development and virtual validation of innovative ADAS/AD functions. This presentation from IAV illustrates how to enhance 3D models with real-world test data by applying AI methods with MATLAB^®, Simulink^®, and RoadRunner. The presentation will cover how to:

• Make simulation models accurate under different conditions and countries. This can be done using realistic camera/radar/lidar modeling approaches, IAV competencies on perception modeling, virtual world creation, and generation of road networks and realistic assets for country-based validation.
• Create realistic scenes and scenarios from real-world test—including data augmentation, data analysis, AI-based generation of 3D models—both manually and programmatically. The outlook is to include multi-agent simulation.
• Combine all-new methods to create a complete solution package.

Automated Driving in the Urban Environment with RoadRunner Scenario

15:30–15:50

This talk presents concepts that can be used to expedite the development of advanced driver assistance and automated driving systems by leveraging the power of simulation. It also provides solutions to augment your simulation environment with vehicle-to-everything (V2X) communication, which will become relevant in the future. Learn how to:

• Create a complex urban scene consisting of intersections with traffic lights in RoadRunner.
• Generate a V2X MAP from RoadRunner HD Map and a V2X SPAT (signal phase and timing) from the RoadRunner traffic signalization.
• Implement a mission planner to search the shortest path for a given start and destination position using an A-star planner.
• Design a behavioral planner to follow traffic lights in the urban intersections using V2X MAP and SPAT in RoadRunner Scenario.

AI with Model-Based Design: Reduced-Order Modeling

16:35–16:55

During vehicle development, high-fidelity models such as those based on finite element analysis, computer-aided engineering, and computational fluid dynamics are created for a variety of components. However, these high-fidelity models are not suitable for all stages of the development process. For example, a finite element analysis model that is useful for detailed component design will be too slow to include in system-level simulations for verifying your control system or to perform system analyses that require many simulation runs. Similarly, a high-fidelity model for the thermal behavior of a battery will be too slow to run in real time on your embedded system.

Does this mean you have to start from scratch to create faster approximations of your high-fidelity models? This is where reduced-order modeling (ROM) comes to the rescue. ROM is a set of computational techniques that helps you reuse your high-fidelity models to create faster-running, lower-fidelity approximations.

This talk focuses on AI-based ROM techniques and methods and how they can be leveraged for Model-Based Design. Discover how to leverage the Simulink^® add-on for reduced-order modeling to set up design of experiments, generate input-output data, and train and evaluate suitable reduced-order models using preconfigured templates that cover various ROM techniques. Learn how to integrate these AI models into your Simulink simulations, whether for hardware-in-the-loop testing or deployment to embedded systems for virtual sensor applications. Explore the pros and cons of different ROM approaches to help you choose the best one for your next project.

Deploy AI-Based Functions into Rapid Prototyping for Real-Time Applications

16:55–17:15

New functions based on machine learning models are being developed across the automotive industry for multiple applications with the goal of improving vehicle performance and customer satisfaction. To study the viability and perform the calibration of such functions, they can be deployed into rapid prototyping units to execute in-vehicle validations. However, the deployment of models coming from newer development frameworks into real-time applications can bring some challenges using the classical toolchain approach.

This presentation shows how Hyundai Motor Europe (in cooperation with MathWorks) uses Simulink^® code generation and the target library for third parties to deploy different machine learning models into a defined toolchain for rapid control prototyping and ECU calibration. Together, we have established a workflow including different solutions to run models designed and trained within MATLAB^® or PyTorch^® frameworks. This gives the team great flexibility to extend its research and development capabilities across different environments.

AI-Driven Software Design and Development for AURIX TC4x in MATLAB and Simulink

17:15–17:40

The traditional approach to embedded software development is burdened with many potential pitfalls, such as vulnerability to errors and time-consuming development, due to manual peripherals configuration and interprocess communication. This presentation introduces a model-driven approach for embedded software development based on Simulink^® models to overcome these disadvantages. Based on an automotive-based use case—trajectory control—we will demonstrate a best practice example of end-to-end development and deployment of AI-enhanced embedded applications. Using the SoC Blockset™ support package for Infineon AURIX™ TC4x, all necessary hardware components like CPUs, hardware accelerators, peripherals, memory units, and IPC components can be easily configured and simulated from the level of GUI. This example application can be easily distributed on several CPUs and parallel processing units, enabling efficient execution of the neural network. Learn how Simulink enables you to generate an optimized code and build the final software for the Infineon AURIX TC4x platform.