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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛/商业影响是提高了汽车运输的安全性、可用性和公众公平性，从而减少了美国每年因恶劣天气发生的撞车事故造成的5,300人死亡、418,000人受伤和数十亿美元的损失。该技术在具有挑战性的驾驶条件下，如拥堵的十字路口和桥梁，黑暗的隧道，以及阳光刺眼和活跃的降雪期间，使用摄像头数据处理来识别行驶车道。解决这些问题还有助于美国在全球汽车市场的技术竞争力、与国防和能效应用相关的技术开发、现有大学课程的扩展以及代表不足的社区的创业参与。拟议研究的基础是利用专门用于雪地导航的相机和全球定位数据，使用实时机器学习方法，而不过度依赖深度学习。这项技术可以在现有车辆上实施，产生广泛的商业影响，并成为发展可行业务的有力手段，该业务正在产生税收收入，并为当地社区提供技术工作机会。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。该系统在不过度依赖深度学习的情况下，提供对人类观察到的地面真实情况的成功性能验证，因此汽车公司可以使用标准实践成功验证该系统。这一创新使目前的驾驶辅助产品在最需要的时候仍能发挥作用：在低能见度、低牵引力的情况下。这项研究旨在验证在双车道交叉路口、桥梁、隧道、在阳光刺眼的条件下、在100英里的活跃降雪以及在误导性环境信息的情况下的创新。将收集这些实例的数据，并将修改和改进现有技术。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.