Optimizing Interconnect Topologies For Automotive ADAS Applications


Designing automotive Advanced Driver Assistance Systems (ADAS) applications can be incredibly complex. State-of-the-art ADAS and autonomous driving systems use ‘sensor fusion’ to combine inputs from multiple sources, typically cameras and optionally radar and lidar units to go beyond passive and active safety to automate driving. Vision processing systems combine specialized AI accelerators... » read more

Seeing with Sound: AI-Based Detection of Participants in Automotive Environment from Passive Audio


Existing ADAS solutions for car environmental awareness (cameras, LiDAR, ultrasonic, etc.) require targets to be in a clear line of sight from the sensor. The target must be illuminated by some source of energy, so systems are affected by dust, weather, lighting, and obstacles. We address those limitations using a passive acoustic solution that “listens” to the environment. It can hear pote... » read more

Semiconductor Testing Unlocks Increasing Levels Of ADAS


Today’s advanced driver assistance systems (ADAS) require unprecedented computing power – tasked with processing an incredible amount of data from sensors in real-time, making split-second decisions, and ensuring the safety and comfort of passengers. The challenge is fluid and, as vehicles ascend from one level of autonomous driving to the next, computational demands will rise exponentially... » read more

The Uncertainty Of Certifying AI For Automotive


Nearly every new vehicle sold uses AI to make some decisions, but so far there is no consistency in what is being developed, where it is being used, and whether it is compatible with other vehicles on the road. This fragmentation is partially due to the fact that AI is still a nascent technology, and cars and trucks sold today may be significantly different than those that will be sold sever... » read more

Software-Defined Vehicle Momentum Grows


Experts at the Table: The automotive ecosystem is undergoing a transformation toward software-defined vehicles, spurring new architectures with more software. Semiconductor Engineering sat down to discuss the impact of these changes with Suraj Gajendra, vice president of products and solutions in Arm's automotive line of business; Chuck Alpert, R&D automotive fellow at Cadence; Steve Spadon... » read more

Advancing Automotive Functional Safety Through Analog & Mixed-Signal Fault Simulation


The automotive industry is undergoing a major transformation, driven by the rise of electric vehicles, ADAS, connected cars, and autonomous vehicles. Due to the safety-critical nature of automotive applications, the reliability and tolerance to faults in semiconductor designs becomes paramount. This white paper delves into the role of analog fault simulation in the context of automotive functio... » read more

Powering The Automotive Revolution: Advanced Packaging For Next-Generation Vehicle Computing


Automotive processors are rapidly adopting advanced process nodes. NXP announced the development of 5 nm automotive processors in 2020 [1], Mobileye announced EyeQ Ultra using 5 nm technology during CES 2022 [2], and TSMC announced its “Auto Early” 3 nm processes in 2023 [3]. In the past, the automotive industry was slow to adopt the latest semiconductor technologies due to reliability conc... » read more

From Data To Safety On-The-Road Hardware Health Monitoring


The automotive industry is currently experiencing a transformation driven by electrification, connectivity, and autonomous driving. Vehicles require extensive computational capabilities and generate massive amounts of data. Future cars will embrace new architectures, becoming software-defined "computers on wheels" capable of hosting advanced applications, machine learning algorithms, and contin... » read more

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

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