I-Corps: A Self-driving Autonomous Electric Scooter

这个I-Corps项目更广泛的影响/商业潜力是开发自动驾驶电动踏板车。 电动滑板车（e-scooters）是新的城市交通模式的重要组成部分，与汽车和公共汽车相比，它在短距离内提高了可持续性、可达性和公平性。美国环保署估计，减少一半不到一英里的美国汽车旅行将每年节省5.75亿美元的燃料成本。2018年，美国有3850万人次乘坐电动滑板车，是2017年的两倍。到2030年，美国的微型移动市场预计将达到2000 - 3000亿美元。在全球范围内，自2015年以来，投资者已经将对微型移动初创企业的投资增加了57亿美元。也就是说，大型摩托车车队维护成本高昂，并且不能保证骑手容易进入。在没有骑手的情况下智能地重新定位自身的电动滑板车可以通过改善骑手体验、增加电动滑板车使用、降低服务成本以及帮助监管过程来为滑板车操作者提供价值。这个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，这些解决方案可以很容易地改装到电动滑板车和其他微移动平台上，以实现自动驾驶。所提出的解决方案能够实现电动踏板车的部署、再充电和单元利用优化。 该技术将使电动滑板车能够在没有骑手的情况下安全智能地操纵，以实现骑行召唤，自动停车和自适应重新定位以增加使用量。AI系统的机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。车外组件包括用于预测乘车需求空间分布的机器学习算法。到目前为止，已经开发出基于电动滑板车动力学新模型的自动驾驶解决方案，包括基于感知的转向算法。 此外，还进行了电动滑板车地理空间乘坐需求分析（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NSF的法定使命，并通过使用基金会的智力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.