Tiago Rodrigues

From single-core to multicore — but you don’t have to start from scratch. 🚗⚙️ In automotive embedded systems, the shift to multicore is inevitable. But with safety-critical legacy code and real-time constraints, migrating from single-core isn’t just plug-and-play. That’s where our AUTOSAR expertise comes in. We contributed to the introduction of multicore into AUTOSAR with release 4.0.1 in 2009 and simultaneously developed the ETAS RTA-CAR BSW stack: a robust, field-proven solution that fully supports any customer use case. Built by engineers, for engineers —RTA-CAR at a glance: 🔧 Compact & efficient - Optimized for resource-constrained, low-latency real-time environments. 🛡️ Safety & security - Complies with industry standards like ISO26262 and ISO/SAE21434, along with MISRA-C & CERT-C guidelines. 🛠️ Versatile compatibility - Suitable for all ECU types, from smart sensors to vehicle computers, and adaptable for single and multi-OEM projects. ⚡Wide device support - Compatible with various target devices and integrates seamlessly with third-party MCALs and security solutions. 🚀 Proven success - Deployed on over 4 billion ECUs globally. We’ve summed up more exclusive insights and real-world cases to help you navigate the transition in a white paper. Download it now: https://brnw.ch/21wSQG3 #YourAmbitionIsOurMission #AUTOSARClassic

53

The Critical Role Of Safety Microcontrollers In ADAS And AD Applications. New Webinar: https://lnkd.in/d4bqBN-E Join us for an in-depth exploration of the critical role Safety Microcontrollers (MCUs) play in Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) applications. This webinar, presented by Infineon Technologies, aims to provide a comprehensive understanding of the functionalities and indispensability of Safety MCUs, particularly in contrast to the nascent Safety Islands within System on Chips (SoCs). Discover why OEMs continue to rely on external Safety MCUs, such as Infineon's AURIX™, despite the common availability of Safety Islands in modern automotive SoCs. Key takeaways: ✅ Essential Role of Safety MCUs: Safety MCUs are a cornerstone of a proper FUSA concept for ADAS/AD applications, such as domain controllers, smart cameras, LiDARs, and radars. ✅ Critical Functions of Safety MCUs: Safety MCUs supervise actuator commands, provide emergency response, monitor the health of all critical components, offer gateway functionality, and support an independent real-time software environment. ✅ Advantages of AURIX™ Safety MCUs: AURIX™ is the technically proven solution due to its best-in-class scalability, extensive AUTOSAR and safety software portfolio, and ability to handle fail-operational use cases. ✅ Limitations of Safety Islands: Safety Islands in SoCs lack the comprehensive feature set and hardware separation that dedicated Safety MCUs offer; hence, they cannot replace them.

165

2 comentários

Why ISO 26262 Is Crucial in Automotive Development As vehicles become smarter, more connected, and increasingly autonomous, ensuring functional safety is not just important — it’s essential. That’s where ISO 26262 comes in. ISO 26262 is an international standard specifically designed for the functional safety of electrical and electronic systems in production automobiles. It provides a structured framework to identify and mitigate risks related to system failures, ensuring that vehicles operate safely even in the face of unexpected faults. Why is it needed? Complexity is growing: Modern vehicles have hundreds of ECUs and millions of lines of code. ISO 26262 helps manage this complexity with systematic risk assessment and development processes. Safety is non-negotiable: From ADAS to electric powertrains, safety must be built in — not added on. Global trust: Compliance with ISO 26262 builds credibility with OEMs, regulators, and end users alike. Whether you're a developer, engineer, or product leader, integrating ISO 26262 early in your V-model or agile development cycle is a proactive step toward safer mobility. Safety isn’t just a feature — it’s a responsibility. #Automotive #ISO26262 #FunctionalSafety #AutonomousVehicles #AutomotiveDevelopment #ASIL #SafetyFirst

38

2 comentários

🔊 Meet IAV SonicSeek – Smarter Acoustic Diagnostics IAV SonicSeek uses AI to detect and classify abnormal sounds in real time — helping prevent damage, reduce maintenance costs, and improve quality assurance across industries. From automotive infotainment systems to production machinery and wind turbines, SonicSeek captures live acoustic data and analyzes it via cloud or edge computing to pinpoint issues before they escalate. 🧠 Developed with TU Braunschweig, SonicSeek combines advanced neural networks with a scalable, cloud-based pipeline — adaptable for automotive, energy, aerospace, and beyond. ✅ Fast fault detection ✅ Easy system integration ✅ Scalable, modular design 🔍 Experience a new standard in intelligent diagnostics. 👉 https://lnkd.in/gSi4Rh6r

24

1 comentário

For the validation of ADAS (Advanced Driver Assistance System) driving functions, developers need as much measurement data as possible from test drives – because this is the only way to ensure that the system has encountered many edge cases such as construction sites or rare traffic signs. But the measuring equipment for this is expensive, therefore making it cost-intensive to scale for large vehicle fleets. Validation is also generally associated with a large amount of work, as the example of traffic sign recognition shows: a developer in the passenger seat compares whether the ADAS system has correctly recognised a traffic sign during road tests. In the event of an error, they note the discrepancy manually. In addition, the corresponding measurement data from the vehicle is recorded on a hard drive. Porsche Engineering aims to reduce the manual work effort required for the validation of ADAS functions. To do so, it is employing smartphones equipped with software for traffic sign recognition from the Berlin-based start-up Peregrine.ai. Learn more: https://lnkd.in/e7Pzp8zZ Macan Turbo Electric (WLTP, preliminary values): Electrical consumption combined: 20.7 – 18.9 kWh/100 km; CO₂ emissions combined: 0 g/km; CO₂ class: A; Status 04/2025

431

16 comentários

💡 Think flashing your software to the ECU means your runnable logic runs immediately? Not quite. In automotive systems, it's not that simple — and for good reason. We’re not just pushing code to a basic device. We’re dealing with complex embedded systems in vehicles, where timing, scheduling, and execution order are absolutely critical. 🚗 Understanding AUTOSAR Execution Flow – From Alarms to Runnables 🔧 If you're working in automotive software, understanding how runnables are triggered in AUTOSAR is critical for building real-time, deterministic systems. Let’s walk through the execution flow, from ECU startup all the way to runnable execution — with examples: 🔹 1. ECU Startup It all begins when the car powers up: ✅ Hardware boots up ✅ Operating System (OS) initializes ✅ RTE (Run-Time Environment) starts This bootstraps the environment for software components (SWCs) to run. 🔹 2. Counters & Alarms The OS uses hardware timers to increment counters. These are monitored by alarms, which can be configured to: 🔁 Trigger cyclically (e.g., every 10ms) 🔔 Activate tasks 🧩 Set events to wake up tasks 🧠 Invoke callbacks 📌 Example: A 20ms alarm sets an event for a periodic task that handles sensor data. 🔹 3. Tasks & Events AUTOSAR typically uses extended tasks, which can wait for events. When the OS detects an event (e.g., from an alarm), it: ✅ Wakes the task ✅ Moves it to the ready/running state 📌 Example: A task waiting on a "NewSensorData" event gets activated every 20ms. 🔹 4. RTE Dispatching Runnables Once the task runs, the RTE dispatches the appropriate runnables: Runnables are code units inside SW-Cs They're mapped to tasks based on timing or event triggers 📌 Example: Runnable_ProcessSensorData is triggered when Task A is activated by a 20ms alarm. 🧩 Why It Matters This chain — from timers → alarms → events → tasks → runnables — ensures: ✅ Real-time behavior ✅ Deterministic execution ✅ Scalable modular design across ECUs 🔍 Whether you're debugging a timing issue or designing a new application in AUTOSAR Classic Platform, having a clear mental model of this flow will save hours of guesswork. 💬 Have you worked on optimizing task-to-runnable mappings or alarm configurations? Let’s connect and exchange tips! #AUTOSAR #AutomotiveSoftware #EmbeddedSystems #RealTimeSystems #RTE #AutomotiveEngineering

141

Engineering, to the power of AI. ⚙️ In embedded systems, a single oversight in a requirement, a design choice or a line of code can cost millions or even lives. That’s where AI makes the difference. With Expleo, engineering teams move from quality-by-testing to quality-by-design: ⏱️ Reduce Hardware-in-the-Loop testing time and costs by up to 30% 🛠️ Minimise unscheduled maintenance by up to 50% with AI-driven predictive maintenance 🛡️ Add extra safety features with ultra-light EdgeAI virtual sensors Our experts help you design smarter from the start, so you can deliver safer systems, faster testing and fewer failures. 👉 Discover how to elevate product performance, to the power of AI: https://expleo.to/3JYLQ1E #ToThePowerOfAI

53

I had the opportunity to test GLIWA T1 4.0 recently. Thanks to Cristian Garzón and Germano Brunacci for the oportunity. GLIWA T1 4.0 is not “new.” It’s just less friction. And that matters. And honestly: it’s hard to improve an already great tool without just adding noise. This feels like the opposite, same fundamentals, fewer steps, faster loops. What stood out in my quick test: - Overall it feels faster (less waiting, quicker iterations) - A new page that puts the key information in one place, no more tab switching between cont / scope / … just to keep context - A very practical one: T1.flex across all functions at once, no more manual work selecting each function, starting measurements, repeating the same setup cycle Why this matters in real projects: timing/resource closure isn’t blocked by “lack of data”, it’s blocked by workflow friction. Reducing manual steps increases cadence and makes it easier to keep integration disciplined. What’s your biggest time sink today in timing analysis: setup/selection, running measurements, or turning results into decisions?

24

1 comentário

Kinematic Equations in ADAS Advanced Driver Assistance Systems (ADAS) rely on kinematic equations to predict motion, avoid collisions, and keep drivers safe. These equations help calculate: ✔ How fast a car is moving (velocity) ✔ How quickly it can stop (deceleration) ✔ How far it travels before reacting (stopping distance) v=v₀+at: Final velocity. d=v₀t+½at²: Displacement. v²=v₀²+2ad: Velocity-displacement relationship. d=1/2(v₀+v)t: Displacement with average velocity. Where: v = final velocity v₀ = initial velocity a = acceleration t = time d = displacement Automatic Emergency Braking (AEB): Scenario: Your car is traveling at 20 m/s (v₀). A pedestrian suddenly appears 25 meters ahead (d). The AEB system needs to determine the deceleration (a) required to stop in time. Calculation: We know the final velocity (v) must be 0 m/s to stop. Using v²=v₀²+2ad, we can rearrange to solve for a: a=(v2−​vo^2)/(2d) a=(0^2−20^2)/(2∗25) −400/50 =-8m/s² This means the AEB system needs to apply a deceleration of 8 m/s² to avoid a collision. Adaptive Cruise Control (ACC): Scenario: Your car is traveling at 30 m/s (v₀). The car ahead slows down, causing your radar to detect a relative acceleration of -2 m/s² (a). The system wants to calculate the distance (d) your car will travel in the next 3 seconds (t). Calculation: Using the equation d=Vo​t+1/2​at^2 d=(30∗3)+(0.5∗−2∗3^2) d=90+(−9) d=81 meters. The system calculates your car will travel 81 meters in that time frame. This information is used to adjust your car's speed to maintain a safe following distance. Time-to-Collision (TTC): Scenario: Your car is traveling at 15 m/s, and a car ahead is traveling at 10 m/s. The distance between them is 25 meters. Assuming the car ahead maintains its velocity. How much time until a collision? Calculation: Relative velocity = 15-10 = 5 m/s. TTC = distance / relative velocity. TTC = 25 / 5 = 5 seconds. Therefore, the time to collision is 5 seconds. If the car ahead were decelerating, then the "a" variable would be used to calculate a more accurate TTC. Key ADAS Points Sensors (radar, lidar, cameras) provide the initial data (position, velocity). ADAS algorithms use kinematic equations to: Predict future object positions. Calculate safe following distances. Determine necessary braking or steering actions. The accuracy of these calculations is critical for the reliable operation of ADAS. Disclaimer: This post is intended for educational purposes from a software development perspective. It does not cover complex non-linear vehicle dynamics.

561

12 comentários

Traditional development approaches that prioritize hardware and mechanical systems are no longer sufficient in the era of software-defined vehicles. As the industry shifts its focus and the competition adapts, automakers must consider new strategies that emphasize agility and innovation. Read the “Don’t Get Outpaced” eBook to identify outdated approaches and learn new strategies to navigate the future of mobility: https://lnkd.in/eNf8hitn

41

Master the Core of Automotive Embedded Systems: Bootloaders Explained In modern vehicles, reliable firmware flashing and secure booting are non-negotiable — and bootloaders make that possible. Whether you're developing an ECU, working on OTA updates, or implementing diagnostic services using UDS, understanding bootloader types is a must. 🔍 Here's a quick breakdown: 🔹 ROM Bootloader – Pre-burned in silicon, triggers boot process 🔹 Primary Bootloader (PBL) – Initializes hardware & loads next stage 🔹 Secondary Bootloader (SBL) – Prepares OS or app-level boot 🔹 Flash Bootloader (FBL) – Enables flashing via CAN, UDS, DoIP 🔹 Bootloader Updater (BLU) – Updates bootloader safely with rollback 📘 Learn more via hands-on blogs, visuals, and tutorials at: 👉 Download Now from Google Play 🔗 https://lnkd.in/gaw5GaFq 👉 Visit for more : 🌍 https://lnkd.in/gBC-RaA6 #Bootloader #FlashBootloader #ROMBootloader #ECUDevelopment #EmbeddedSystems #AutomotiveEmbedded #UDSProtocol #FirmwareFlashing #OverTheAirUpdate #OTAUpdates #SecureBoot #MicrocontrollerProgramming #AutomotiveSecurity #CANProtocol #DoIP #CANFD #DiagnosticsOverCAN #EmbeddedSoftware #RealTimeSystems #TechLearning #PiEmbSysTech #OpenSourceLearning #SelfLearning #IoTDevices #AutomotiveElectronics #LinuxInEmbedded #SoftwareFlashing #FBL #SBL #PBL #BLU #EmbeddedInnovation #TechEducation #ADASDevelopment #FirmwareUpdate #SystemProgramming #MCUDesign #AutomotiveSoftware #DigitalMobility #PiEmbCommunity

274

14 comentários

🚗📡 Master ADAS Radar System Design with Expert Insights! Designing advanced ADAS radar systems comes with unique challenges, but we’ve got you covered! Our free technical handbook dives into:   🚗 Insights into key application trends 🧠 Design challenges and how to overcome them 💡 Nexperia’s optimized solutions for power and signal electronics   📥 Download now and optimize your radar designs with industry-leading expertise! https://lnkd.in/ga_uqXGp

76

📊 Confidence in zonal architectures is rising fast. According to the 2025 SDV Survey, more respondents than ever believe zonal E/E architectures will become the dominant automotive design approach by 2030. This shift will simplify wiring, boost flexibility, and support software-first development. Curious what’s driving the momentum? Download the SDV Survey eBook for key trends, regional insights, and industry outlook:https://lnkd.in/gNwZKWPR #SoftwareDefinedVehicle #ConnectedVehicles #MobilityInnovation #SDVSurvey

27

🔟 𝐓𝐡𝐢𝐧𝐠𝐬 𝐭𝐨 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫 𝐖𝐡𝐞𝐧 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐚 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐕𝐞𝐡𝐢𝐜𝐥𝐞 𝐌𝐨𝐝𝐞𝐥 Tip 9️⃣ - Make your model available to users, leveraging the Simulink Interface and FMU export functionality Do you need to share your models with third parties? VI-CarRealTime serves as a collaboration platform, allowing you to export the complete vehicle model - obfuscated if required - either as a Simulink component or as an FMU for any FMI-compatible simulation environment. 👉 Want to learn more? Request additional information about VI-CarRealTime: https://lnkd.in/eQ-vJA2 #vigrade #ZeroPrototypes

40

1 comentário