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

职业：无人驾驶公路运输系统之路：行为分析和轨迹控制方法

• 批准号: 1453949
• 负责人: Xiaopeng Li
• 金额: $ 50万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453949&HistoricalAwards=false
• 关键词: CAREER; Pathway; Driverless; Highway; Transportation

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.

这项教师早期的职业发展（职业）计划赠款调查了近未通行的高速公路交通，其中一部分车辆是由路边单元自动化和可控制的（例如，使用无人驾驶汽车和连接的车辆技术）。该研究项目探讨了控制这些自动化车辆轨迹的策略，不仅可以优化自己的性能，还可以改善附近人类驾驶员的经验。这些新型基于轨迹的控制概念和模型可以应用于公路基础设施，电子设备和车辆技术的相关工业发展。研究结果期望为评估当前人类驱动的高速公路运输系统的可行性和潜在好处提供方法论基础，并将其带入完全自动化的高速公路。此外，研究成果应纳入创意教育计划，以激励和准备学生从事工程职业。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的发展，向高中生和代表性不足的小组进行宣传，以及与工业合作者和学术同伴的讲习班。研究目标是：（i）发现高速公路上相邻驱动程序的基本互动模式，并在高速公路上和（ii）在控制型号的轨迹方面改善了互动型的轨迹，以改善这些型号的交通方式，以改善这些互动的交通方式。这项研究通过将高保真驱动模拟器与现成的交通模拟器集成，从而建立了一个虚拟高速公路实验平台，从而带来了灵活的高速公路驾驶环境，从而促进了驾驶员与车辆相互作用的易于记录。此外，这项工作创建了一种新的数据分析方法，该方法将收集的轨迹分解为一组元素段，从而使我们能够将数据挖掘技术应用于揭示驾驶员交互模式。这些努力旨在克服运输研究中的两个重大挑战：收集高保真轨迹数据的困难以及缺乏分析此类数据的定量方法。基于这些发现，将开发一个交通管制框架，该框架为无人驾驶汽车计划轨迹，以提高交通效率，减少环境影响，提高安全性并改善所有驾驶员的体验。这项研究将轨迹优化方法的范围从传统的孤立单个轨迹到共享运输渠道中的多个相互依存和交互式轨迹。