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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响是提高汽车运输安全性、可用性和公众公平性，从而减少美国每年因恶劣天气事故造成的 5,300 人死亡、418,000 人受伤以及数十亿美元的损失。该技术在具有挑战性的驾驶条件下（例如拥挤的十字路口和桥梁、黑暗的隧道以及阳光刺眼和积雪期间），使用摄像头数据处理来识别行驶车道。解决这些问题还可以提高美国在全球汽车市场的技术竞争力，开发与国防和能源效率应用相关的技术，扩展现有大学课程，以及代表性不足的社区的创业参与。所提出的研究的基础是利用相机和全球定位数据，专门用于使用实时机器学习方法在雪中导航，而不过度依赖深度学习。这项技术可以在现有车辆中实施，从而产生广泛的商业影响，并成为发展可行业务的有力手段，从而产生税收并为当地社区提供技术工作。这项工作的强大技术创新是使用弹性工程方法、单独调整的分类、相机和 GPS 融合以及快速处理机器学习构建的分层计算机视觉系统。该系统根据人类观察到的真实情况提供成功性能验证，而无需过度依赖深度学习，因此汽车公司可以使用标准实践对其进行成功验证。这项创新使当前的驾驶辅助产品能够在最需要的时候保持功能：在低能见度、低牵引力的情况下。这项研究旨在验证双车道交叉口、桥梁、隧道、太阳眩光条件下、100 多英里的主动降雪以及误导性环境信息情况下的创新。将收集这些实例的数据，并对现有技术进行修改和改进。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。