The Convergence of AI, ML, and IoT in Automotive Systems: A Future Perspective on Edge Computing

Edge computing, where sensing, control, and intelligent processing occur near where data is acquired, is poised to be a fundamental enabler of several imminent disruptive future computing paradigms for emerging applications such as CPS, IoT, and more sophisticated AI-driven services. In this context, we posit the convergence of AI, ML, and IoT in automotive systems, the infrastructure required to enable it, and where edge computing will play a pivotal role in the real-world deployment of this ecosystem. We also review a few digital infrastructure technologies that can vastly enhance these next-generation digital automotive systems. This is examined through the investigation of real-world scenarios provided by our partner companies, the prominent Consumer Electronics Show (CES), and other sources. First, it is demonstrated through several industrial benchmarks that the proposed digital infrastructure technologies provide significant alleviation in terms of application accuracy, and at times even take the benefits beyond even 1x equivalent DNN accelerator-based systems in resource-constrained edge computing environments. After this, the challenges of designing and deploying them in real-world automotive systems are outlined. The paper concludes with the verifiable thesis that edge computing technologies need to play a significant role in the next-generation digital automotive system development so that ML-driven AI systems of the future are designed and deployed successfully in the field and can deliver their intent of providing superior user experience, enhanced safety, and convenience.