Max Mauro Dias Santos is an internationally recognized researcher, academic leader, and engineer with over 29 years of experience bridging electrical and embedded systems research with the real-world deployment of intelligent, safe, and sustainable mobility technologies. His work focuses on the design, validation, security, and responsible integration of autonomous, electrified, and connected systems, guided by the principle that intelligence in safety-critical environments must be explainable, trustworthy, and deployable. He is currently an Associate Professor and Department Chair of Electronics at UTFPR–Ponta Grossa, where he leads multidisciplinary research programs and directs the Automotive Systems Group (GSA) and the Real-Time Systems Laboratory (LTR). Throughout his career, he has led large-scale projects funded with over USD 4.3 million and collaborated with global OEMs and Tier-1 suppliers, including Volvo, Renault, Stellantis, DAF Trucks, Bosch, and Vector Informatik, delivering deployable engineering solutions and advancing DA/AD validation and cybersecurity. His background also includes senior engineering roles at Volvo, General Motors, and IBM, and his scholarly output comprises numerous journal and conference publications, books, and patents. With international research appointments in Canada, France, and Portugal, including as a Visiting Professor at the University of Toronto, his vision is to build globally connected research ecosystems in embedded AI, cyber-physical security, and intelligent mobility, aligned with clean energy, public safety, and long-term societal impact.
AI-Driven Intelligent Transportation Systems
January 7th, 2026
Artificial Intelligence–Driven Intelligent Transportation Systems (AI-Driven ITS) represent the convergence of advanced sensing, data analytics, and intelligent decision-making to transform how mobility systems are designed, operated, and experienced. At their core, AI-Driven ITS integrate technologies such as computer vision, machine learning, deep learning, sensor fusion, edge and cloud computing, vehicle-to-everything (V2X) communication, and increasingly large and small language models to enable perception, prediction, optimization, and explainable decision-making across transportation networks. These systems support applications ranging from advanced driver assistance and autonomous vehicles to smart traffic management, logistics optimization, predictive maintenance, and safety monitoring for vulnerable road users. Looking ahead, the perspective for AI-Driven ITS points toward more connected, autonomous, and sustainable mobility ecosystems, where intelligence is distributed from the edge to the cloud, decisions are context-aware and human-aligned, and safety, cybersecurity, and ethics are embedded by design. As these technologies mature, AI-Driven ITS will play a central role in reducing accidents, improving efficiency, lowering environmental impact, and enabling smarter, more inclusive transportation systems for cities and societies worldwide.
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