CPS: Medium: Learning-Enabled Assistive Driving: Formal Assurances during Operation and Training

尽管大众媒体声称，目前的“自动驾驶”和高级驾驶辅助系统（ADAS），基于纯粹的数据驱动，机器学习算法仍然可能遭受灾难性的故障。这种“理论上的统计准确性”但“在实践中表现出的脆弱性”的趋势，使得当前的深度学习算法不适合在反馈回路中用于安全关键型网络物理应用，如交通中的辅助或无监督自动驾驶汽车。尽管存在这些缺点，但可以肯定的是，自动化和自动驾驶将在未来的移动解决方案中发挥至关重要的作用，无论是个人拥有的还是共享的移动车辆；无论自动化程度如何，至少在可预见的未来，司机都应该参与其中。目前，无论是从个人体验角度，还是从安全角度，都需要量化人类驾驶员在自动驾驶回路中的影响。此外，下一代“自动驾驶”或“驾驶员辅助”系统应该能够感知、学习和预测驾驶员的习惯和技能，并相应地进行调整，从而使驾驶更加直观和安全。如何在不牺牲安全性的前提下，以透明的方式将驾驶员的学习目标和偏好最好地融合在一起，增强“驾驶体验”，还需要进一步的研究。本研究的主要目的是利用强化学习和形式化方法中的技术和模型来开发下一代ADAS，以适应驾驶员的偏好和习惯以及安全约束。其目的是提高深度神经网络架构在包括驱动程序在内的反馈回路中的性能和安全保证：a)使用混合无模型和基于模型的处理管道的冗余架构；b)在培训和执行过程中，通过利用安全关键应用程序的正式方法的最新进展，增加安全保证。具体来说，该技术包括学习一个状态预测模型，使用一种新的神经网络架构来估计驾驶员的内部奖励函数，伴随着一种联合的终身学习方法来识别异质驾驶员的偏好和目标。所提出的方法将通过将神经网络架构与可微分信号时序逻辑（STL）框架相结合来进一步增加安全性和鲁棒性层，以满足时间安全约束，并将使用运行时保证（RTA）机制来满足额外的安全层，该机制将可达性分析与监控方法相结合，以确保系统不会转向不安全的条件。拟议的框架将分两个阶段进行验证和测试。第一阶段将涉及使用高保真驾驶仿真平台（如CARLA）对几个重要问题进行模拟和实验。第二阶段将使用佐治亚理工学院开发的驾驶模拟器进行人在环路实验。研究将涉及研究生和本科生。这项研究的结果将通过期刊和会议出版物、组织应邀讲习班和研讨会发言以及有针对性地向大众传播媒介传播（新闻稿、采访）的方式向社区传播。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。

Despite claims in popular media, current “self-driving” and advanced driver assist systems (ADAS), based on purely data-driven, machine learning algorithms may still suffer from catastrophic failures. This tendency of “theoretical statistical accuracy” but “demonstrated fragility in practice” makes current deep learning algorithms unsuitable for use within feedback loops for safety-critical, cyber-physical applications such as assisted or unsupervised self-driving cars in traffic. Regardless of these shortcomings, it is certain that automation and autonomy will play a crucial role in future mobility solutions, either for personally owned or shared-mobility vehicles; and regardless of the level of automation, at least in the foreseeable future, the driver should be in the loop. There is currently a need to quantify the impact of the human driver within the autonomy loop, both from an individual experiential perspective, as well as in terms of safety. In addition, the next generation of “self-driving” or “driver-assist” systems should be able to sense, learn and anticipate driver’s habits, skills and adapt accordingly, thus making driving more intuitive and safer at the same time. How to best integrate the driver’s learning goals and preferences in a transparent manner to enhance the “driving experience” without sacrificing safety requires further work, however.The main objective of this research is to utilize techniques and models from reinforcement learning and formal methods to develop the next generation of ADAS that can accommodate the driver preferences and habits with safety constraints. The aim is to increase the performance and safety guarantees of deep neural network architectures operating within a feedback loop that includes the driver by: a) using redundant architectures that blend model-free and model-based processing pipelines; and b) adding safety guarantees both during training and during execution by leveraging recent advances of formal methods for safety-critical applications. Specifically, the technique consists of learning a state prediction model to estimate the internal reward function of the driver using a novel neural network architecture, accompanied by a federated, lifelong learning approach to identify heterogeneous driver preferences and goals. The proposed approach will further add a layer of safety and robustness by incorporating the neural network architecture with a differentiable Signal Temporal Logic (STL) framework to meet temporal safety constraints, and will meet with an additional safety layer using a run-time assurance (RTA) mechanism that combines reachability analysis with a monitoring approach to ensure that system cannot be steered to unsafe conditions. The proposed framework will be validated and tested in two stages. The first stage will involve simulations and experiments on several non-trivial problems using high-fidelity driving simulation platforms such as CARLA. The second stage will conduct human-in-the-loop experiments using a driving simulator developed at Georgia Tech. The research will involve both graduate and undergraduate students. The results of this research will be disseminated to the community by journal and conference publications, organization of invited workshops and seminar presentations, and by targeted exposure (press releases, interviews) to popular media.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.