SBIR Phase I: Scientific Discovery Translation of Snow-Covered Road Perception Software to a Lane Detection in Snow (LDIS) Product

SBIR 第一阶段：将雪地道路感知软件科学发现转化为雪地车道检测 (LDIS) 产品

• 批准号: 2304352
• 负责人: Zachary Asher
• 金额: $ 27.5万
• 依托单位: REVISION AUTONOMY INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304352&HistoricalAwards=false
• 关键词: SBIR; Phase; Scientific; Discovery; Translation

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is improved automotive transportation safety, usability, and equity for the general public which reduces the annual 5,300 fatalities, 418,000 injuries, and billion-dollar losses from inclement weather crashes in the United States. The technology identifies the driving lane using camera data processing in challenging driving conditions such as congested intersections and bridges, dark tunnels, and during sun glare and active snowfall. Addressing these problems also enables U.S. technology competitiveness in the global automotive market, development of technologies relevant to national defense and energy efficiency applications, expansions of existing university courses, and entrepreneurial engagement from underrepresented communities. The foundation for the proposed research is the utilization of camera and global positioning data specifically for navigation in snow using real-time machine learning methods without an overreliance on deep learning. This technology can be implemented in current vehicles, enabling a widespread commercial impact and a strong means to grow a viable business that is generating tax revenue and offering technology jobs to the local community.The strong technical innovation of this work is a hierarchical computer vision system built using a resilience engineering methodology, individually tuned classifications, camera and GPS fusion, and fast processing machine learning. This system provides verification of successful performance with respect to human-observed ground truth without an overreliance on deep learning so that it can be successfully validated by automotive companies using standard practices. This innovation allows current driving assistance products to remain functional when they are needed most: in low visibility, low traction situations. This research aims to verify the innovation in two-lane intersections, bridges, tunnels, under sun glare conditions, in 100+ miles of active snowfall, and in instances of misleading environmental information. Data for these instances will be collected and the existing technology will be modified and improved.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.

这项小型企业创新研究（SBIR）I期项目的更广泛/商业影响改善了公众的汽车运输安全性，可用性和股权，减少了美国的每年5,300人死亡，418,000次伤害，数十亿美元的伤害，并降低了美国美国的损失。该技术在充满挑战的驾驶条件（例如拥挤的十字路口和桥梁，深色隧道）以及阳光眩光和主动降雪期间使用摄像头数据处理来识别驾驶道。解决这些问题还可以使美国技术在全球汽车市场，与国防和能源效率应用相关的技术的发展，现有大学课程的扩展以及从代表性不足的社区中的企业家参与。拟议研究的基础是使用实时机器学习方法专门用于雪地导航的相机和全球定位数据，而无需过分依赖深度学习。这项技术可以在当前的车辆中实施，从而实现广泛的商业影响，并有强大的方法来发展可行的业务，该业务正在为当地社区产生税收收入，并向当地社区提供技术工作。这项工作的强大技术创新是使用Resilience Engineering方法构建的层次计算机视觉系统，该方法是使用Resilience Engineering方法来构建的，单独调整了分类和GPS融合和快速处理的计算机学习和快速处理。该系统提供了有关人类观察的基础真理的成功绩效的验证，而没有过度依赖深度学习，以便使用标准实践可以通过汽车公司成功验证它。这项创新允许当前的驾驶援助产品在最需要时保持功能：在低知名度下，牵引力低。这项研究旨在验证两车道的交叉点，桥梁，隧道，在阳光下，有100英里以上的主动降雪以及误导环境信息的情况下。这些实例的数据将被收集，并将修改和改进现有技术。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。