With the fusion of new technologies applied to electric motors and autonomous driving, the automobile industry will face many more changes in the next decade than it has for the last 100+ years since its inception. This is comparable to the historical moment when most people relied on horses, while vehicles powered by internal combustion engines were about to revolutionize transportation. We are at the tipping point of a new era with a safer, greener, and smarter transportation system.
However, hardly any industry can adopt so many technological innovations simultaneously even with billions of dollars of investment and tremendous influx of talents. One exception is the IT industry where astonishing progress has been made in part due to wide range collaboration through open source infrastructures. In order to make the trend towards electric and autonomous vehicles become more dependable and reliable, there is still much for the automobile industry to learn. Indeed, departing from the proprietary architectures, designs, and supply chains, future vehicles are like a complicated embedded system on wheels. Moreover, the majority of future innovations will be based on software and artificial intelligence. On the other hand, the automobile industry is different in that it is highly regulated due to safety and reliability concerns. Thus, the open source-based innovations are essential but will need more organized efforts to integrate the standards from the automobile industry with computer software and hardware into all the products.
In response to the above challenges, we have established a Technical Committee on Electric and Autonomous Vehicles (TC-EAV) under the IEEE Reliability Society (RS). TC-EAV aims to foster bring researchers and practitioners together as the promising future of the automobile industry heavily relies on interdisciplinary collaborations among academia, industry, and government agencies, including both private and public sectors, in the areas such as software engineering, communications and networking, computer visions, artificial intelligence and machine learning, cyber-physical systems, testing, validation, and formal verification.