CPS: Synergy: Collaborative Research: Adaptive Intelligence for Cyber-Physical Automotive Active Safety - System Design and Evaluation

汽车行业发现自己正处在一个十字路口。当前MEMS传感器技术的进步、嵌入式控制软件的出现、计算机技术、数字图像处理、机器学习和控制算法的快速发展，以及对车对车（V2V）和车对基础设施（V2I）技术的不断增加的投资，将彻底改变我们在日常生活中使用车辆和通勤的方式。特别是汽车主动安全系统，在过去50年里取得了巨大的成功，并有助于控制交通事故。尽管如此，仅在美国，每年就有3万多人死亡，200万人受伤，而在世界范围内，这一数字要多得多。据估计，仅在美国，交通事故对经济的影响就高达每年3000亿美元。在驾驶安全性（和舒适性）方面的进一步改进需要下一代主动安全系统更加主动（而不是被动），并且能够理解和解释驾驶员的意图。未来的主动安全系统必须考虑到驾驶员技能的多样性、驾驶员在交通中的行为以及整体交通状况。本研究旨在通过考虑驾驶员、车辆、主动安全控制系统和环境之间的相互作用，提高当前汽车主动安全控制系统（ASCS）的能力。除了解决汽车工业的基本问题外，这项研究还将在其他网络物理领域产生影响，在这些领域中，人类手动控制车辆或设备，包括：飞行、重型机械操作、采矿、远程机器人和机器人医疗。制造对人类操作员来说感觉和行为“自然”的自主/自动化系统并不总是那么容易。随着这些系统和机器更多地参与与人类的日常互动，使它们以可预测的方式运行的需求比以往任何时候都更加迫切。为了实现本项目拟研究的目标，本项目将利用对驾驶员认知状态的估计，对ASCS进行相应的调整，以实现与驾驶员的无缝操作。具体来说，将开发新的方法来推断驾驶员的长期和短期行为，通过使用贝叶斯网络和神经形态算法来估计驾驶员的技能和当前的注意力状态，以及从转向和踏板输入中获得的动态运动线索。这些信息将被注入到ASCS操作中，以便通过利用自适应、实时、模型预测最优控制理论的最新成果来提高其性能。驾驶员和ASCS之间正确的自主水平和工作量分配将确保驾驶员和控制系统之间不会发生冲突，并且不会损害安全性和乘客舒适度。一项全面的计划将用于测试和验证开发的理论，通过收集几个人类受试者的测量数据，同时操作虚拟现实驾驶模拟器。

The automotive industry finds itself at a cross-roads. Current advances in MEMS sensor technology, the emergence of embedded control software, the rapid progress in computer technology, digital image processing, machine learning and control algorithms, along with an ever increasing investment in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, are about to revolutionize the way we use vehicles and commute in everyday life. Automotive active safety systems, in particular, have been used with enormous success in the past 50 years and have helped keep traffic accidents in check. Still, more than 30,000 deaths and 2,000,000 injuries occur each year in the US alone, and many more worldwide. The impact of traffic accidents on the economy is estimated to be as high as $300B/yr in the US alone. Further improvement in terms of driving safety (and comfort) necessitates that the next generation of active safety systems are more proactive (as opposed to reactive) and can comprehend and interpret driver intent. Future active safety systems will have to account for the diversity of drivers' skills, the behavior of drivers in traffic, and the overall traffic conditions.This research aims at improving the current capabilities of automotive active safety control systems (ASCS) by taking into account the interactions between the driver, the vehicle, the ASCS and the environment. Beyond solving a fundamental problem in automotive industry, this research will have ramifications in other cyber-physical domains, where humans manually control vehicles or equipment including: flying, operation of heavy machinery, mining, tele-robotics, and robotic medicine. Making autonomous/automated systems that feel and behave "naturally" to human operators is not always easy. As these systems and machines participate more in everyday interactions with humans, the need to make them operate in a predictable manner is more urgent than ever.To achieve the goals of the proposed research, this project will use the estimation of the driver's cognitive state to adapt the ASCS accordingly, in order to achieve a seamless operation with the driver. Specifically, new methodologies will be developed to infer long-term and short-term behavior of drivers via the use of Bayesian networks and neuromorphic algorithms to estimate the driver's skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs. This information will be injected into the ASCS operation in order to enhance its performance by taking advantage of recent results from the theory of adaptive and real-time, model-predictive optimal control. The correct level of autonomy and workload distribution between the driver and ASCS will ensure that no conflicts arise between the driver and the control system, and the safety and passenger comfort are not compromised. A comprehensive plan will be used to test and validate the developed theory by collecting measurements from several human subjects while operating a virtual reality-driving simulator.