CPS: Medium: Smart Tracking Systems for Safe and Smooth Interactions Between Scooters and Road Vehicles

CPS：中：智能跟踪系统可实现踏板车和道路车辆之间安全平稳的交互

• 批准号: 2038403
• 负责人: Rajesh Rajamani
• 金额: $ 120万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038403&HistoricalAwards=false
• 关键词: CPS; Medium; Smart; Tracking; Systems

This Cyber-Physical Systems (CPS) grant will study smart tracking systems on scooters for ensuring safe and smooth interaction with other vehicles and pedestrians on the road. The smart system consists of inexpensive sensors, active sensing based estimation algorithms, and deep learning based robust image processing to enable trajectory tracking of all nearby vehicles on the road. If the danger of a scooter-vehicle collision is detected, an audio-visual alert is automatically provided to the car driver to make them aware of the presence of the scooter. The system also monitors the scooter rider’s behavior, provides real-time feedback to improve rider compliance with traffic signals and sidewalk rules, and documents the information as a part of the rider’s safety record. The key attractive features of the system are that it is inexpensive ( $500), is immediately useful on today’s roads without requiring the vehicles on the road to be equipped with additional technology, and is potentially commercializable. The project contributes to the society by improving safety of micro-transportation systems, and broadens participation in computing via undergraduate research activities and promoting significant cross-disciplinary collaboration between faculty in engineering, computer science and human factors.The project will conduct research to develop two novel vehicle tracking technologies. The two technologies, one based on use of a low-cost single-beam laser sensor and another based on a low-cost low-density Lidar sensor, can have applications in protecting vulnerable transportation users such as bicyclists, motorcyclists, scooter riders, users in developing countries and also in other cyber-physical systems such as indoor robots. The computer vision system will handle rain, snow and low lighting which pose a major challenge by corrupting normal image data. New robust deep-learning-based recognition techniques will be developed that can effectively deal with corrupted image data sets. This will be achieved by novel nonlinear modeling of the low-complexity structures in both the clean data and in the image corruption using deep learning and allowing for mixed corruption types, varying severity and possible corruptions in the training data itself. To ensure human-in-the-loop robustness, the project utilizes human subject studies to evaluate the effectiveness of a variety of audio and visual mechanisms for alerting the motorist and the scooter rider, including innovations such as providing visual cues of biological motion on the scooter to improve localization of the scooter by motorists.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.

这项网络物理系统（CPS）拨款将用于研究滑板车上的智能跟踪系统，以确保与道路上的其他车辆和行人进行安全顺畅的互动。该智能系统由廉价的传感器、基于主动感知的估计算法和基于深度学习的鲁棒图像处理组成，可以跟踪道路上所有附近车辆的轨迹。如果检测到踏板车与车辆碰撞的危险，则自动向汽车驾驶员提供视听警报，使他们意识到踏板车的存在。该系统还监控滑板车骑手的行为，提供实时反馈以改善骑手对交通信号和人行道规则的遵守，并将信息记录为骑手安全记录的一部分。该系统的主要吸引人的特点是它价格低廉（500美元），在当今的道路上立即可用，而不需要道路上的车辆配备额外的技术，并且具有商业化的潜力。该项目通过提高微型交通系统的安全性来为社会做出贡献，并通过本科生的研究活动扩大对计算的参与，促进工程、计算机科学和人为因素之间的重要跨学科合作。该项目将开展研究，开发两种新的车辆跟踪技术。这两种技术，一种基于使用低成本单光束激光传感器，另一种基于低成本低密度激光雷达传感器，可用于保护易受伤害的交通用户，如骑自行车者，摩托车者，踏板车骑手，发展中国家的用户以及室内机器人等其他网络物理系统。计算机视觉系统将处理雨，雪和低光照，这是一个重大挑战，破坏正常的图像数据。将开发新的强大的基于深度学习的识别技术，可以有效地处理损坏的图像数据集。这将通过使用深度学习对干净数据和图像损坏中的低复杂性结构进行新型非线性建模来实现，并允许混合损坏类型，不同的严重性和训练数据本身可能的损坏。为了确保人在回路中的鲁棒性，该项目利用人类受试者研究来评估各种音频和视频机制的有效性，以提醒驾驶员和踏板车骑手，包括创新，如在踏板车上提供生物运动的视觉提示，以提高驾驶者对踏板车的定位。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Self-Validation: Early Stopping for Single-Instance Deep Generative Priors

• 发表时间: 2021-10
• 期刊: Journal of water process engineering
• 影响因子: 7
• 作者: Taihui Li;Zhong Zhuang;Hengyue Liang;L. Peng;Hengkang Wang;Ju Sun

On Challenges in Coordinate Transformation for Using a High-Gain Multi-Output Nonlinear Observer *

关于使用高增益多输出非线性观测器的坐标变换的挑战*

• DOI: 10.23919/acc55779.2023.10156518
• 发表时间: 2023
• 期刊: IEEE
• 作者: Alai, Hamidreza;Zemouche, Ali;Rajamani, Rajesh
• 通讯作者: Rajamani, Rajesh

A Survey of Electric-Scooter Riders’ Route Choice, Safety Perception, and Helmet Use

电动滑板车骑手的路线选择、安全认知和头盔使用调查

• DOI: 10.3390/su15086609
• 发表时间: 2023
• 期刊: Sustainability
• 影响因子: 3.9
• 作者: Sievert, Kelsey;Roen, Madeleine;Craig, Curtis M.;Morris, Nichole L.
• 通讯作者: Morris, Nichole L.

Low-cost camera and 2-D LIDAR fusion for target vehicle corner detection and tracking: Applications to micromobility devices

• DOI: 10.1016/j.ymssp.2023.110891
• 发表时间: 2024-01
• 期刊: Mechanical Systems and Signal Processing
• 影响因子: 8.4
• 作者: Hamidreza Alai;R. Rajamani