Beyond "Taming Electric Scooters": Disentangling Understandings of Micromobility Naturalistic Riding

Electric(e)-scooters have emerged as a popular, ubiquitous, and first/last-mile micromobility transportation option within and across many cities worldwide. With the increasing situation-awareness and on-board computational capability, such intelligent micromobility has become a critical means of understanding the rider's interactions with other traffic constituents (called Rider-to-X Interactions, RXIs), such as pedestrians, cars, and other micromobility vehicles, as well as road environments, including curbs, road infrastructures, and traffic signs. How to interpret these complex, dynamic, and context-dependent RXIs, particularly for the rider-centric understandings across different data modalities --- such as visual, behavioral, and textual data --- is essential for enabling safer and more comfortable micromobility riding experience and the greater good of urban transportation networks. Under a naturalistic riding setting (i.e., without any unnatural constraint on rider's decision-making and maneuvering), we have designed, implemented, and evaluated a pilot Cross-modality E-scooter Naturalistic Riding Understanding System, namely CENRUS, from a human-centered AI perspective. We have conducted an extensive study with CENRUS in sensing, analyzing, and understanding the behavioral, visual, and textual annotation data of RXIs during naturalistic riding. We have also designed a novel, efficient, and usable disentanglement mechanism to conceptualize and understand the e-scooter naturalistic riding processes, and conducted extensive human-centered AI model studies. We have performed multiple downstream tasks enabled by the core model within CENRUS to derive the human-centered AI understandings and insights of complex RXIs, showcasing such downstream tasks as efficient information retrieval and scene understanding. CENRUS can serve as a foundational system for safe and easy-to-use micromobility rider assistance as well as accountable use of micromobility vehicles.