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

尽管流行媒体声称，目前基于纯粹数据驱动的机器学习算法的自动驾驶和高级驾驶员辅助系统(ADAS)可能仍会遭遇灾难性的故障。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安全关键的网络物理应用(如交通中的辅助或无人驾驶汽车)的反馈循环中使用。尽管存在这些缺点，但可以肯定的是，自动化和自主化将在未来的移动解决方案中发挥关键作用，无论是个人拥有的还是共享移动的车辆；而且无论自动化水平如何，至少在可预见的未来，驾驶员都应该参与其中。目前有必要从个人经验的角度以及从安全的角度来量化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、技能，并做出相应的适应，从而使驾驶更直观、更安全。然而，如何以透明的方式将驾驶员的学习目标和偏好最好地结合起来，在不牺牲安全性的情况下提高驾驶体验，还需要进一步的工作。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能够适应安全约束的驾驶员偏好和习惯的下一代ADAS。其目的是通过以下方式提高在包括驱动程序的反馈环路内运行的深度神经网络体系结构的性能和安全保证：a)使用融合了无模型和基于模型的处理流水线的冗余体系结构；以及b)通过利用安全关键型应用程序的形式方法的最新进展，在训练和执行期间增加安全保证。具体地说，该技术包括使用一种新的神经网络结构学习状态预测模型以估计驾驶员的内部奖励函数，并伴随着一种联合的终身学习方法来识别不同的驾驶员偏好和目标。通过将神经网络结构与可微信号时态逻辑(STL)框架相结合来满足时间安全约束，所提出的方法将进一步增加一层安全和健壮性，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，以确保系统不会被引导到不安全的条件。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模拟平台(如CALA)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。这项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、组织受邀的研讨会和研讨会演讲，以及有针对性地向大众媒体曝光(新闻稿、采访)向社会传播。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.