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，自动驾驶汽车引起了很多关注，Waymo，Uber，Tesla和其他人在道路上进行了测试。此类车辆有可能消除人类错误（包括分心的驾驶），这是所有道路事故中90％以上的原因。这些收益范围超出了安全性，例如，预计到2030年，在美国道路上采用自动驾驶车辆将使每辆车的温室排放量减少87-94％。但是，最近宣布的最近的死亡人数和事故涉及自动驾驶汽车指出，自动驾驶汽车指出的是主要的挑战，这些挑战尚未得到解决。最近，通过与其他车辆和基础设施进行沟通，与今天的公路上最高级的汽车相比，它们仍然存在有关道路危害，行人和可持续运营保证的信息，从而有可能通过与其他车辆和基础设施进行通信，以共享有关道路危害，行人和可持续运营保证的信息，从而有可能提高自动驾驶车辆安全性和燃油经济性的潜力。这项渴望的建议将进行重要的早期探索性研究，以在骑士中奠定强大的感应，沟通，本地化，安全和控制的基础，以使实时安全和可持续的燃油经济性端到端保证。拟议的研究将研究最先进的深度机器学习算法对感应，调度，定位，异常检测和能量最佳控制对对抗性攻击，传感器缺陷，时机畸变和其他来源的不确定性的敏感性。首次将对各个车辆子系统的不确定性的影响将在整个CAV系统的安全性，燃油经济性，驱动性能和新兴行为上进行量化。这种探索性分析将允许实现强大的新对策，以改善不确定性的鲁棒性，可预测性和性能，负责在骑士中做出决策的“深度学习系统”。通过量化和克服各种不确定性的不确定性来源，紧密相互连接的和自动驾驶汽车的不确定性来源，该项目将在更强大的自动稳固，安全，安全的自我自我自动化和维持自动化和维持生命的自动驾驶中。这一结果将改变地面运输和现代社会，为消除美国道路上的数千人死亡，改善燃油经济性，增强运输过程中的舒适性以及每年由于事故和交通拥堵而每年浪费数十亿美元的舒适性，为消除数以千计的死亡人数，提高燃油经济性，提高燃油经济性，增强舒适度，为消除数千人的损失。研究推力是基础的，可以应用于广泛的应用，无论重点是创建不确定性抗性的多机构系统，例如，无人驾驶空中或水下车辆群。该项目的所有车辆驱动器周期数据集和算法将被开放，以进一步研究自动驾驶汽车安全和可持续性的新兴跨学科领域。研究工作还将与推广工作紧密融合，以包括女性，服务不足，研究生，本科生和K-12学生的研究，这对社会产生了高度积极的影响。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来支持的。

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