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Research Paper | Information Technology | United States of America | Volume 12 Issue 10, October 2023 | Rating: 5 / 10
Optimizing ADAS and Autonomous Driving Systems with Advanced Ethernet Protocols and Machine Learning
Ravi Aravind
Abstract: We will review how Ethernet and open standard AVB/TSN are evolving for automotive and how they offer real implementation benefits from both a hardware and software level. We discuss how AVB/TSN IP can be deployed at the application level, making it easier to develop, test, and optimize use cases with Ethernet as the network backbone. These can range from in-vehicle multi-resolution GUI and multiple safety-critical ADAS to high-performance multi-camera sensing engine features. We also look at the potential for machine learning-based implementations inside switched-Ethernet ECUs, running software that manages congestion and competes for time-critical services with the more traditional automotive traffic. Companies developing in the in-vehicle network solution space can learn where to appropriately position themselves in the increasingly software-designed ecosystem-driven future of automotive electronics. We show how HW & SW developed AVB/TSN implementation, reducing the complexity of E/E architectures, resulting in a more effective ADAS and improving road safety. It also allows OEMs, car manufacturers, and Tier 1's to rapidly deploy that system features most important to their customers' requirements at launch. The automotive ADAS features deployment race is about to shift up a gear, enabling them to jointly deliver vehicles with the highest driver/user acceptance and confidence in the latest ADAS features.
Keywords: Optimizing ADAS and Autonomous Driving Systems with Advanced Ethernet Protocols and Machine Learning, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science, Vehicle, Vehicle Reliability
Edition: Volume 12 Issue 10, October 2023,
Pages: 2147 - 2155