Collaborative Research: CPS: Small: An Integrated Reactive and Proactive Adversarial Learning for Cyber-Physical-Human Systems

自动驾驶汽车的逐步部署将不可避免地导致一类新的重要的网络-物理-人类系统的出现，自动驾驶汽车通过车载传感器或车对车通信与人类驾驶的车辆进行交互。与控制理论相结合的强化学习可以帮助满足自动驾驶汽车实时决策和5级自动驾驶的安全要求。然而，众所周知，传统的强化学习策略容易受到对抗性或非对抗性扰动对其观察结果的影响，类似于分类器和/或学习的奖励（包）下降的对抗性示例。随着使用开放式通信和控制平台实现自动驾驶变得至关重要，以及行业继续投资于此类系统，对解决弹性问题的担忧加剧了这些问题。决策制定机制旨在将敏捷性与强化学习结合起来，允许自适应、自修复和自优化。这项研究将贡献和统一几个不同领域的知识体系，包括强化学习、安全、自动控制和运输，以实现人类在环中的弹性自治。在这个项目中，为了对抗行动和观察操纵以及奖励下降，主要研究人员将利用主动切换策略，旨在(i)为闭环强化学习机制中的对抗性输入和奖励下降提供鲁棒性，（ii）增加欺骗操作的成本，（iii）限制易受攻击的行为和观察的暴露，以及（iv）提供稳定性，最优性和鲁棒性保证。最终，研究人员将对上述每个领域作出基本贡献，并将这些领域合并起来，以提供一个独特的综合框架。该项目的成果将为减少事故提供基础，从伦理角度提高对自动驾驶技术的信心。所提出的框架可以扩展到全球经济的其他关键推动因素，包括智能和互联城市、医疗保健和智能系统的网络化行动，同时减少环境污染并最大限度地减少对人类健康的不利环境影响。该项目将培养来自不同层次、年龄和文化背景的下一代学生，通过良好协调、适当水平的参与研究和教育活动，同时为学生提供一个独特的机会，让他们欣赏高效、自主和低成本的设计。该项目还将为未来的工程课程做出贡献，追求研究和教育的实质性整合，并为来自代表性不足的群体的学生提供机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The gradual deployment of self-driving cars will inevitably lead to the emergence of a new important class of cyber-physical-human systems where autonomous vehicles interact with human-driven vehicles via on-board sensors or vehicle-to-vehicle communications. Reinforcement learning along with control theory can help meet the safety requirements for real-time decision making and Level 5 autonomy in self-driving vehicles. However, it is widely known that conventional reinforcement learning policies are vulnerable to adversarial or non-adversarial perturbations to their observations, similar to adversarial examples for classifiers and/or reward (packet) drops of the learning. Such issues are exacerbated by concerns of addressing resiliency as the use of open communication and control platforms for autonomy becomes essential, and as the industry continues to invest in such systems. Decision making mechanisms, designed to incorporate agility with the help of reinforcement learning, allow self-adaptation, self-healing, and self-optimization. This research will contribute and unify the body of knowledge of several diverse fields including reinforcement learning, security, automatic control, and transportation for resilient autonomy with humans-in-the-loop.In this project, to counter action and observation manipulation as well as reward drops, the principal investigators will leverage proactive switching policies that aim (i) to provide robustness to adversarial inputs and reward drops in the closed-loop reinforcement learning mechanisms, (ii) to increase the cost of manipulation by deception, (iii) to limit the exposure of vulnerable actions and observations, and (iv) to provide stability, optimality, and robustness guarantees. Ultimately, the investigators will develop fundamental contributions to each of the above-mentioned fields and amalgamate these fields to provide a unique synthesis framework. The outcomes of this project will increase levels of confidence in autonomous technologies from ethical perspectives by providing an underpinning for curtailing accidents. The proposed framework can be extended to other key enablers of the global economy, including smart and connected cities, healthcare, and networked actions of smart systems while decreasing environmental pollution and minimizing the adverse environmental impacts on human health. The project will train the next generation of students from various levels, ages, and cultures through well-coordinated, level appropriate involvement in research and educational activities while providing a unique opportunity for the students to appreciate efficient, autonomous, and low-cost designs. This project will also contribute to future engineering curricula, pursue a substantial integration of research and education, and provide opportunities to engage students from the underrepresented group.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.