CAREER: Pathway to a Driverless Highway Transportation System: A Behavior Analysis and Trajectory Control Approach

这项学院早期职业发展(Career)计划拨款调查近期的高速公路交通，部分车辆由路边单位自动化和控制(例如，使用无人驾驶汽车和联网车辆技术)。这项研究项目探索了控制这些自动车辆的轨迹的策略，不仅可以优化它们自己的性能，还可以改善附近人类驾驶员的体验。这些新的基于轨迹的控制概念和模型可以应用于高速公路基础设施、电子设备和车辆技术的相关工业发展。研究成果有望为评估将目前由人驾驶的公路运输系统转变为完全自动化的公路运输系统的可行性和潜在好处提供方法学基础。此外，研究成果将被整合到创造性教育倡议中，以激励和准备学生追求工程职业生涯。该项目建立了一个虚拟交通实验室，学生可以在课堂学习期间玩互动驾驶游戏。这个基于经验的虚拟学习平台支持工程课程开发、面向高中生和代表性不足的群体的推广，以及与行业合作者和学术同行的研讨会。研究目标是：(1)发现高速公路上相邻司机之间相互作用的基本模式；(2)基于这些相互作用模式，创建控制分布式自动车辆轨迹的策略，以改善整体交通性能。本研究通过将高保真驾驶模拟器与现有的交通模拟器相结合，建立了一个虚拟的高速公路实验平台，从而形成了一个灵活的高速公路驾驶环境，便于记录人与车的交互。此外，这项工作创建了一种新的数据分析方法，将收集的轨迹分解为一组基本片段，使我们能够应用数据挖掘技术来揭示驾驶员的交互模式。这些努力旨在克服交通研究中的两个突出挑战：收集高保真车辆轨迹数据的难度，以及缺乏分析此类数据的量化方法。基于这些发现，将开发一种交通控制框架，规划无人驾驶车辆的轨迹，以提高交通效率，减少对环境的影响，增加安全性，并改善所有司机的体验。本研究将轨道优化方法的研究范围从传统的孤立的个体轨道推进到共享运输通道中多条相互依赖、相互作用的轨道。

This Faculty Early Career Development (CAREER) Program grant investigates near-future highway traffic with a portion of vehicles being automated and controllable by roadside units (e.g., using driverless car and connected vehicle technologies). This research project explores strategies of controlling the trajectories of these automated vehicles to not only optimize their own performance but also improve the experience of nearby human drivers. These novel trajectory-based control concepts and models can be applied to related industrial developments in highway infrastructure, electronic devices, and vehicle technologies. The research outcomes expect to provide a methodological foundation for evaluating the feasibility and the potential benefits of transferring the current human-driven highway transportation system into one that is fully automated. Further, the research outcomes are to be integrated into creative education initiatives to motivate and prepare students to pursue an engineering career. This project establishes a virtual traffic laboratory where students can play interactive driving games during classroom learning. This experience-based virtual learning platform supports engineering curriculum developments, outreach to high school students and underrepresented groups, and workshops with industrial collaborators and academic peers.The research objectives are to: (i) discover fundamental patterns of interactions among neighboring drivers on a highway and (ii) create strategies for controlling trajectories of distributed automated vehicles to improve the overall traffic performance based on these interaction patterns. This research establishes a virtual highway experiment platform by integrating high-fidelity driving simulators with off-the-shelf traffic simulators, resulting in a flexible highway driving environment that facilitates the easy recording of driver-to-vehicle interactions. Further, this effort creates a new data analysis method that decomposes collected trajectories into a set of elemental segments, allowing us to apply data mining techniques to uncover driver interaction patterns. These efforts aim to overcome two prominent challenges in transportation research: the difficulty of collecting high-fidelity vehicle trajectory data and the lack of quantitative methods for analyzing such data. Based on these discoveries, a traffic control framework is to be developed that plans trajectories for driverless vehicles in order to improve traffic efficiency, reduces environmental impacts, increases safety, and improves the experience of all drivers. This research advances the scope of trajectory optimization methodologies from traditional isolated individual trajectories to multiple interdependent and interactive trajectories in a shared transportation channel.