CAREER: Structural and Accountable Behavior Understandings and Human-centered AI Designs with Naturalistic Micromobility Riding Data

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Micromobility systems are small, lightweight vehicles that usually operate below 15 mph. Examples include regular and electric bicycles, stand-up electric scooters, and 3-wheeled scooters equipped with seats. Understanding how micromobility riders should behave to minimize conflicts with other constituents of urban traffic is essential. Using this understanding in the design of micromobility platforms and their interactions with the rider will improve safety and comfort in urban transportation and enhance public acceptance. Rider behavior is structured, consisting of maneuvers such as acceleration, turns, and dismounts and macroscopic behaviors such as the selection of locations to visit and paths to take. Building structured models of rider behavior is necessary as the existing models are difficult to use for interpreting rider accountability, and whether behaviors are acceptable to the public. To address these challenges, this project proposes to design a novel Structural and Accountable micromobility Rider Behavior Understanding System (SARBUS). SARBUS will provide the micromobility rider behavior modeling through human-centered artificial intelligence. For concrete insights and prototype development, the project will focus on the increasingly popular stand-up electric scooters (e-scooters). The model development will use data from continuous multi-modal sensors collected during real-world riding scenarios. The principal investigator will recruit and train students from under-represented groups in research, and will use the project research to promote teaching and training through student mentoring, new course development, and outreach activities.This project will be composed of two interleaved modeling thrusts. Thrust A will develop a reinforcement learning technique that interactively learns and captures the rider's macroscopic location visit and paths (through GPS logs) and microscopic maneuvering behaviors (through accelerometers and gyroscopes), as well as their structural inter-dependencies. This thrust will derive a graph representation that will be used by SARBUS to illuminate and explain the riders' decision-making process. Thrust B will build models from the motion sensor readings of the maneuver behaviors (accelerometers and gyroscopes), recorded (on-board) riding videos and the human textual annotations of those videos. These data will capture the interactions of the riders with other road users and the environment. This thrust will quantify the relationships across these modalities through graph learning, and the resulting models will reveal when rider behaviors create conflicts with other traffic elements, and identify rider accountability. The principal investigator will further conduct e-scooter rider case studies with SARBUS to understand how the structural rider behavior modeling and accountability interpretation can together benefit the riders in route selection and riding behavior with enhanced travel efficiency and fewer conflicts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.