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

这项网络物理系统（CPS）拨款将研究踏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安全顺畅地互动。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，能够跟踪道路上所有附近车辆的轨迹。如果检测到滑板车碰撞的危险，则自动向汽车驾驶员提供视听警报，使他们意识到滑板车的存在。该系统还监控踏板车驾驶员的行为，提供实时反馈，以提高驾驶员对交通信号和人行道规则的遵守程度，并将这些信息记录为驾驶员安全记录的一部分。该系统最吸引人的特点是价格低廉（500美元），在今天的道路上立即可用，而不需要为道路上的车辆配备额外的技术，并且具有商业化的潜力。该项目通过改善微型交通系统的安全性，通过本科生研究活动扩大计算机的参与，促进工程、计算机科学和人为因素等学科的跨学科合作，为社会做出贡献。该项目将研究开发两种新型车辆跟踪技术。这两种技术，一种基于使用低成本的单光束激光传感器，另一种基于低成本的低密度激光雷达传感器，可以应用于保护脆弱的交通用户，如骑自行车的人、骑摩托车的人、踏板车的人、发展中国家的用户，也可以应用于其他网络物理系统，如室内机器人。计算机视觉系统将处理雨、雪和低光照，这些都会破坏正常的图像数据。新的基于深度学习的鲁棒识别技术将被开发出来，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颖的非线性建模来实现，并允许混合损坏类型，不同的严重程度和训练数据本身可能的损坏。为了确保人在环鲁棒性，该项目利用人体受试者研究来评估各种音频和视觉机制的有效性，以提醒驾驶者和踏板车骑手，包括提供踏板车生物运动的视觉线索等创新，以提高驾驶者对踏板车的定位。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。

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.