On October 21, Professor Joseph Sifakis, a Turing Award laureate and Chief Scientific Advisor of the CaoCao Mobility AI Innovation Research Center, visited the center for in-depth discussions with CaoCao’s technology team. On the same day, CaoCao launched a research project on Trustworthy Decision-Making and Multi-Agent Coordination for Robotaxi Curbside Operations. Professor Sifakis witnessed the launch and offered valuable recommendations based on his extensive research experience.
Professor Sifakis is one of the pioneers of model checking. He received the 2007 Turing Award for his contributions to the development of model-checking theory and its applications. He has long been engaged in research on complex intelligent systems and Trustworthy Autonomous Systems, areas closely related to the safety and trustworthiness challenges facing Robotaxi operations at scale.
In June this year, Professor Sifakis was formally invited to serve as Chief Scientific Advisor of the CaoCao Mobility AI Innovation Research Center to provide academic guidance for CaoCao’s AI transformation and the development of its autonomous driving technologies. At the 2026 World Artificial Intelligence Conference, he outlined three fundamental principles for trustworthy Robotaxis: Safe by Design, System Coordination, and Runtime Assurance. He also noted that CaoCao’s RoboX ecosystem closely aligns with the systems-engineering principles underlying Trustworthy Autonomous Systems.
Passenger pick-up and drop-off are critical to the Robotaxi experience. This newly launched research project focuses on safety and trustworthiness in this everyday mobility scenario, exploring how the theory of Trustworthy Autonomous Systems can be translated into verifiable engineering methods.
Drawing on 11 years of real-world operating data and experience from CaoCao’s mobility services, the project will examine the conditions under which curbside stopping locations can be used, passenger boarding and alighting paths, vehicle stopping behavior, and system coordination. By exploring methods for cross-validating results from simulation and real-vehicle testing, the project aims to build a body of reusable, scenario-based evidence to support safety validation for the trustworthy transfer of Robotaxi operations across cities and their deployment worldwide.