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

职业：结构和负责任的行为理解以及以人为中心的人工智能设计与自然微移动骑行数据

• 批准号: 2239897
• 负责人: Suining He
• 金额: $ 50万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239897&HistoricalAwards=false
• 关键词: CAREER; Structural; Accountable; Behavior; Understandings

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.

微型操作系统是小型轻巧的车辆，通常以低于15 mph的速度运行。例如，定期和电动自行车，站立电动踏板车以及配有座椅的3轮踏板车。 了解骑手应如何将与城市交通的其他成分的冲突最小化至少是必不可少的。使用这种理解在微型效率平台的设计中及其与骑手的互动将提高城市运输方面的安全性和舒适性，并增强公众接受。骑手的行为是结构化的，由诸如加速，转弯和下马和宏观行为之类的动作组成，例如选择要访问的位置和要走的路径。 建立骑手行为的结构化模型是必要的，因为现有模型很难用来解释骑手问责制，以及行为是否可以被公众接受。为了应对这些挑战，该项目建议设计一种新颖的结构和负责任的微型骑士行为理解系统（SARBUS）。 Sarbus将通过以人工智能为中心的人工智能提供微型骑士行为。对于具体的见解和原型开发，该项目将集中于日益流行的站立电动踏板车（E型摩托车）。该模型开发将使用来自现实世界骑行场景中收集的连续多模式传感器的数据。首席研究人员将招募和培训来自代表性不足的研究小组的学生，并将利用项目研究通过学生指导，新课程开发和外展活动来促进教学和培训。该项目将由两个交织的建模推力组成。推力A将开发一种增强学习技术，该技术可以交互学习并捕获骑手的宏观位置访问和路径（通过GPS日志）和显微镜操纵行为（通过加速度计和陀螺仪）以及其结构相互依赖性。该推力将得出图表表示，Sarbus将使用该图表来照亮和解释车手的决策过程。推力B将从动作行为（加速度计和陀螺仪）的运动传感器读数中构建模型，记录（板载）骑行视频以及这些视频的人类文本注释。这些数据将捕获骑手与其他道路使用者和环境的互动。这一推力将通过图形学习量化这些模式之间的关系，并且由此产生的模型将揭示何时骑手行为与其他流量元素造成冲突，并确定骑手的问责制。首席研究者将与SARBUS进一步进行电子诉讼骑士案例研究，以了解结构性骑手行为建模和问责制解释如何使骑手在路线选择和骑行行为方面的行为和较少的旅行效率和更少的冲突中的行为有益于骑手。该奖项反映了NSF的法规任务，并认为通过基金会的知识效果和广泛的评估，这是值得通过评估来进行评估的Crositia crositia和Broadite croseria crocritia crocritia crocritia crocritia crocritia croperia croperia crocritia award section affection。

项目成果

Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature Representations

• DOI: 10.1145/3631421
• 发表时间: 2023-12
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Mahan Tabatabaie;Suining He

Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction

• DOI: 10.1145/3610904
• 发表时间: 2023-09
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Mahan Tabatabaie;Suining He;Kang G. Shin

Interaction-Aware and Hierarchically-Explainable Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation

• DOI: 10.1109/iros55552.2023.10342051
• 发表时间: 2023-10
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Mahan Tabatabaie;Suining He;Kang G. Shin