I-Corps: A Self-driving Autonomous Electric Scooter

I-Corps项目更广泛的影响/商业潜力是开发一款自动驾驶电动滑板车。电动滑板车是新的城市机动性模式的重要组成部分，可以在短距离内提高可持续性、可达性和相对于汽车和公交车的公平性。美国环保署估计，将美国一半的汽车出行减少到一英里以下，每年将节省5.75亿美元的燃料成本。2018年，美国电动滑板车出行3850万次，是2017年的两倍。预计到2030年，美国微型移动市场的价值将在2000至3000亿美元之间。在全球范围内，自2015年以来，投资者对微型移动初创企业的投资已经增加了57亿美元。这就是说，大型摩托车车队的维护成本很高，而且不能保证骑车人很容易到达。在没有骑手的情况下智能地重新定位自己的电动滑板车可以通过改善骑手体验、增加电子滑板车的使用、降低维修成本和帮助监管过程为滑板车运营商提供价值。这个i-Corps项目基于人工智能(AI)物流、运营和管理解决方案的开发，这些解决方案可以轻松改装到电动滑板车和其他微移动平台，以实现自动驾驶。建议的解决方案使电动滑板车的部署、充电和单元利用率得到优化。这项技术将使电动滑板车能够在没有骑手的情况下安全而智能地进行机动，以实现叫车、自动停车和自适应重新定位的目的，从而增加使用量。AI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，以及用于通过传感器反馈进行动力驱动的电机模块。车外部件包括机器学习算法，用于预测乘车需求的空间分布。到目前为止，已经开发出一种基于电动滑板车动力学新模型的自动驾驶解决方案，其中包括基于感知的转向算法。此外，还进行了电动滑板车的地理空间骑行需求分析(使用马里兰交通研究所提供的数据)，并设计了以人为对象的实验，以了解电动滑板车的安全性和人体工程学。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.