Software-Defined Vehicles Ready To Roll


Software-defined vehicles are driving a swell of activity across the automotive ecosystem, including new methodologies and technology approaches that could significantly reduce costs and shorten time to market for advanced features. The SDV approach encompasses more than a single concept. It helps to think of it more as a modeling approach that connects EVs, driver assistance technology, and... » read more

Dealing With Noise In Image Sensors


The expanding use and importance of image sensors in safety-critical applications such as automotive and medical devices has transformed noise from an annoyance into a life-threatening problem that requires a real-time solution. In consumer cameras, noise typically results in grainy images, often associated with poor lighting, the speed at which an image is captured, or a faulty sensor. Typi... » read more

Auto Network Speeds Rise As Carmakers Prep For Autonomy


In-vehicle networks are starting to migrate from domain architectures to zonal architectures, an approach that will simplify and speed up communication in a vehicle using fewer protocols, less wiring, and ultimately lower cost. Zonal architectures will partition vehicles into zones that are more manageable and flexible, but getting there will take time. There is so much legacy technology in ... » read more

Autonomous Vehicles: Not Ready Yet


The swirl of activity around L4 and L5 vehicles has yet to result in a successful demonstration of an autonomous vehicle that can navigate the streets of a city or highway without incident, and there is a growing body of real-world data showing that much work still needs to be done. Robo-taxi trials in big cities such as San Francisco, Los Angeles, and soon San Diego, are proving that autono... » read more

Automotive Safety Island


The promise of autonomous vehicles is driving profound changes in the design and testing of automotive semiconductor parts. Automotive ICs, once deployed for simple functions like controlling windows, are now performing complex functions related to advanced driver-assist systems (ADAS) and autonomous driving applications. The processing power required results in very large and complex ICs that ... » read more

Gearing Up For Level 4 Vehicles


More autonomous features are being added into high-end vehicles, but getting to full autonomy will likely take years more effort, a slew of new technologies — some of which are not in use today, and some of which involve infrastructure outside the vehicle — along with sufficient volume to bring the cost of these combined capabilities down to an affordable price point. In the meantime, ma... » read more

Securing Automotive Ethernet With MACsec Silicon IP


In today’s cars, the Ethernet standard is the go-to solution for connecting zonal gateways to the central compute units that handle ADAS functionality. However, in-vehicle networks are vulnerable to a number of security threats, including eavesdropping, denial-of-service attacks, man-in-the middle attacks, and unauthorized access. This white paper explores how MACsec provides an effective sol... » read more

Why The SOAFEE Project Is Integral For The Design Of Connected Vehicles


The Scalable Open Architecture for Embedded Edge (SOAFEE) project, of which Synopsys is a voting member, is defined by automakers, semiconductor suppliers, open-source and independent software vendors, and cloud technology leaders. The effort builds on technologies that define standard boot and security requirements for the Arm architecture, while adding a cloud-native development and deployme... » read more

Virtual Development Of Perception Sensor Systems


By Ron Martin and Christoph Sohrmann Over the past few years, there has been a marked expansion in research and development activities related to driver assistance systems as well as highly automated and connected driving systems. However, this has yet to translate into a higher degree of automation in the average production vehicle – especially for SAE Level 3 and above. The next step in ... » read more

AI Transformer Models Enable Machine Vision Object Detection


The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the models and simplify their development. Over the years, many AI models have been introduced, including YOLO, Faster R-CNN, Mask R-CNN, RetinaNet, and others, to detect images or video signals, interp... » read more

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