SBIR Phase II: Energy-Efficient Perception for Autonomous Road Vehicles

SBIR 第二阶段：自动驾驶道路车辆的节能感知

• 批准号: 1758546
• 负责人: Forrest Iandola
• 金额: $ 75万
• 依托单位: DeepScale, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1758546&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Energy; Efficient

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to allow more consumers to make use of assisted and autonomous driving systems in automobiles. Fully Autonomous Vehicles (AV) will reduce traffic collisions and enable humans to spend less time driving and more time on productive activities. Commercially deploying AVs requires a number of key technologies including sensing, perceptual systems, motion planning, and actuation. Our discussions with leaders and decisionmakers at automotive companies have shown that the development of robust, accurate, and energy-efficient perception systems is a major technical obstacle to creating mass-producible autonomous road vehicles. Of particular interest to automakers is scaling down the computational requirements of perceptual systems while preserving high levels of accuracy and robustness.This Small Business Innovation Research (SBIR) Phase II project will use deep learning to create perception systems that are (a) scalable across different computational platforms and (b) scalable across smaller or larger sensor sets. Specifically, the company will develop scalable systems from small compute platforms (used for Highly Automated Driving) to somewhat larger compute platforms (used for Fully Automated Driving). Further, the company will develop perceptual systems that scale from few sensors to many sensors. The goal is to "do more with less," advancing the pareto-optimal frontier of efficiency-accuracy and price-accuracy tradeoffs. The company has already engaged with automotive OEMs and suppliers to develop partnerships and to define metrics for success in this endeavor.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）II期项目的更广泛的影响/商业潜力将是允许更多的消费者利用汽车中的辅助和自主驾驶系统。完全自动驾驶的车辆（AV）将减少交通碰撞，使人类花费更少的时间开车，而花更多的时间进行生产活动。商业部署AVS需要许多关键技术，包括传感，感知系统，运动计划和驱动。我们与汽车公司的领导者和决策者的讨论表明，强大，准确和节能的感知系统的发展是创建质量可实现的自动驾驶汽车的主要技术障碍。汽车制造商特别感兴趣的是缩减感知系统的计算要求，同时保持高水平的准确性和鲁棒性。本小型企业创新研究（SBIR）II期项目将使用深度学习来创建（a）在不同的计算平台上可扩展的感知系统，以及（b）在较小或较大的传感器集中可扩展的范围。具体来说，该公司将开发从小型计算平台（用于高度自动化驾驶）到更大的计算平台（用于全自动驾驶）的可扩展系统。此外，该公司将开发从几个传感器扩展到许多传感器的感知系统。目的是“做更多的事情”，促进效率 - 准确性和价格准确性权衡的帕累托最佳前沿。该公司已经与汽车OEM和供应商互动，以建立合作伙伴关系并定义这项努力成功指标。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。