I-Corps: A Self-driving Autonomous Electric Scooter

I-Corps项目更广泛的影响/商业潜力是开发自动驾驶电动滑板车。电动滑板车（e-scooters）是新型城市交通模式的重要组成部分，在短距离内提高了汽车和公共汽车的可持续性、可达性和公平性。美国环保署估计，如果减少一半的美国汽车行驶里程不足1英里，每年将节省5.75亿美元的燃料成本。2018年，美国电动滑板车的出行次数为3850万次，是2017年的两倍。到2030年，美国微型交通工具市场预计将达到2000 - 3000亿美元。自2015年以来，全球投资者已将对微型移动初创企业的投资增加了57亿美元。也就是说，大型滑板车车队的维护成本很高，而且不能保证乘客可以轻松进入。电动滑板车可以在无人驾驶的情况下智能地重新定位，通过改善乘客体验、增加电动滑板车使用率、降低服务成本和帮助监管程序，为滑板车运营商提供了价值。I-Corps项目的基础是开发人工智能（AI）物流、运营和管理解决方案，这些解决方案可以轻松改造为电动滑板车和其他微型移动平台，从而实现自动驾驶。提出的解决方案可以实现电动滑板车的部署、充电和单元利用率优化。该技术将使电动滑板车能够在无人驾驶的情况下安全智能地行驶，以实现乘车召唤、自动停车和自适应重新定位等目的，以增加使用量。人工智能系统的车载组件包括用于感知和定位的传感器模块、用于智能决策的规划模块和通过传感器反馈进行动力驱动的电机模块。车载组件包括用于预测乘车需求空间分布的机器学习算法。迄今为止，一种基于新型电动滑板车动力学模型的自动驾驶解决方案已经开发出来，其中包括一种基于感知的转向算法。此外，还进行了电动滑板车地理空间出行需求分析（数据由马里兰州交通研究所提供），并设计了人体实验，以了解电动滑板车的安全性和人体工程学。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a self-driving electric scooter. Electric scooters (e-scooters) are a vital part of a new urban mobility model, improving sustainability, accessibility, and equity over cars and buses for short distances. The EPA estimates that eliminating half of US car trips less than one mile would save $575M/year in fuel costs. In 2018, 38.5 million trips were taken on e-scooters in the US, which is twice as many as in 2017. The US micromobility market is predicted to be worth between $200–300B by 2030. Worldwide, investors already have increased investments in micro-mobility start-ups by $5.7B into since 2015. This said, large scooter fleets are expensive to maintain and do not guarantee easy access for riders. E-scooters that intelligently re-position themselves without a rider may provide value to scooter operators by improving the rider experience, increasing e-scooter usage, decreasing servicing costs, and aiding the regulatory process. This I-Corps project is based on the development of artificial intelligence (AI) logistics, operations, and management solutions easily retrofitted to electric-scooters and other micromobility platforms to enable self-driving. The proposed solution enables deployment, recharging, and unit utilization optimization of an electric scooter. The technology will enable the e-scooter to safely and intelligently maneuver without a rider for the purposes of ride summoning, automatic parking, and adaptive re-positioning for increased usage. The onboard components of the AI system consist of a sensor module for perception and localization, a planning module for intelligent decision-making, and a motor module for powered actuation via sensor feedback. Offboard components include machine-learning algorithms for predicting the spatial distribution of ride demand. To date, an autonomous driving solution based on novel models of e-scooter dynamics has been developed, including a perception-based steering algorithm. In addition, an e-scooter geospatial ride-demand analysis (with data provided by the Maryland Transportation Institute) has been performed as well as designed human-subject experiments to understand e-scooter safety and ergonomics.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.