EAGER: Exploring Multi-Modal Deep Learning Systems for Sustainable Connected and Autonomous Vehicles

EAGER：探索可持续互联和自动驾驶汽车的多模态深度学习系统

• 批准号: 2132385
• 负责人: Sudeep Pasricha
• 金额: $ 29.56万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132385&HistoricalAwards=false
• 关键词: EAGER; Exploring; Multi; Modal; Deep

Autonomous vehicles have received a lot of attention in recent years, with experimental cars from Waymo, Uber, Tesla, and others being tested on roads. Such vehicles have the potential to eliminate human errors (including distracted driving) that are the cause of more than 90% of all road accidents. The benefits extend beyond safety, e.g., the adoption of self-driving vehicles on U.S. roadways is expected to reduce greenhouse emissions by 87–94% per vehicle by 2030. However, well-publicized recent fatalities and accidents involving self-driving vehicles point to key challenges that remain unaddressed. Recently, connected autonomous vehicles (CAVs) have emerged, with the potential to improve self-driving vehicle safety and fuel economy, by communicating with other vehicles and infrastructure to share information about road hazards, pedestrians, etc. But CAV safety and sustainable operation assurances remain elusive, due to their significantly greater complexity compared to the most advanced vehicles on the roads today. This EAGER proposal will perform critical early exploratory research to lay the foundations of robust sensing, communication, localization, security, and control in CAVs, to enable end-to-end guarantees for real-time safety and sustainable fuel economy. The proposed research will study the susceptibility of state-of-the-art deep machine learning algorithms for sensing, scheduling, localization, anomaly detection, and energy-optimal control to uncertainties from adversarial attacks, sensor faults, timing aberrations, and other sources. For the first time, the impact of uncertainties across individual vehicular subsystems will be quantified on the security, fuel economy, driving performance, and emergent behaviors of the overall CAV system. This exploratory analysis will allow for the realization of powerful new countermeasures to improve uncertainty robustness, predictability, and performance in the “system of deep learning systems” responsible for making decisions in a CAV.By quantifying and overcoming varied and dynamic sources of uncertainty in emerging connected and autonomous vehicles, this project will usher in more robust, safe, and sustainable self-driving vehicles. This outcome will transform ground transportation and modern society, laying the groundwork to eliminate thousands of fatalities on U.S. roads, improving fuel economy, enhancing comfort during transportation, and saving the U.S. economy billions of dollars annually in lost productivity due to accidents and traffic congestion. The research thrusts are foundational and can be applied to a broad range of applications, wherever the emphasis is on creating uncertainty-resilient multi-agent systems, e.g., swarms of unmanned aerial or underwater vehicles. All vehicle drive-cycle datasets and algorithms from the project will be open sourced to further research in the emerging interdisciplinary area of autonomous vehicle safety and sustainability. Research efforts will also be tightly integrated with outreach efforts to include women, underserved, graduate, undergraduate, and K-12 students in research that has a highly positive impact on society.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.

近年来，自动驾驶汽车受到了广泛关注，Waymo、Uber、特斯拉等公司的实验汽车正在道路上进行测试。这种车辆有可能消除人为错误（包括分心驾驶），这些错误是90%以上道路事故的原因。其益处超出了安全性，例如，到2030年，在美国道路上采用自动驾驶汽车预计将使每辆汽车的温室气体排放量减少87-94%。然而，最近广为人知的涉及自动驾驶汽车的死亡和事故表明，关键挑战仍未得到解决。最近，互联自动驾驶汽车（CAV）已经出现，通过与其他车辆和基础设施通信以共享有关道路危险，行人等的信息，有可能提高自动驾驶汽车的安全性和燃油经济性，但CAV的安全性和可持续运营保证仍然难以捉摸，因为与当今道路上最先进的车辆相比，它们的复杂性要高得多。EAGER计划将进行关键的早期探索性研究，为CAV中强大的传感、通信、定位、安全和控制奠定基础，从而实现端到端的实时安全和可持续燃油经济性保证。拟议的研究将研究用于传感、调度、定位、异常检测和能量最优控制的最先进的深度机器学习算法对来自对抗性攻击、传感器故障、定时偏差和其他来源的不确定性的敏感性。第一次，将量化单个车辆子系统的不确定性对整个CAV系统的安全性、燃油经济性、驾驶性能和紧急行为的影响。这种探索性的分析将允许实现强大的新对策，以提高负责在CAV中做出决策的“深度学习系统系统”的不确定性鲁棒性，可预测性和性能。通过量化和克服新兴互联和自动驾驶汽车中各种动态的不确定性来源，该项目将迎来更强大，安全和可持续的自动驾驶汽车。这一成果将改变地面交通和现代社会，为消除美国道路上的数千起死亡事故奠定基础，提高燃油经济性，提高运输过程中的舒适性，并为美国经济每年节省数十亿美元的事故和交通拥堵造成的生产力损失。研究的重点是基础性的，可以应用于广泛的应用，无论重点是创建不确定性弹性多智能体系统，例如，成群的无人驾驶飞行器或水下航行器。该项目的所有车辆驾驶循环数据集和算法都将开源，以进一步研究自动驾驶汽车安全和可持续性这一新兴的跨学科领域。研究工作也将与外展工作紧密结合，以包括妇女，服务不足，研究生，本科生和K-12学生的研究，对社会产生高度积极的影响。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices

• DOI: 10.23919/date54114.2022.9774611
• 发表时间: 2021-11
• 期刊: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
• 作者: Saideep Tiku;S. Pasricha

TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems

• DOI: 10.1109/asp-dac52403.2022.9712524
• 发表时间: 2021-09
• 期刊: 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)
• 作者: S. V. Thiruloga;Vipin Kumar Kukkala;S. Pasricha