Operationalizing connected and automated truck platooning on Canadian highway infrastructure for GHG emissions mitigation

在加拿大高速公路基础设施上实施联网和自动化卡车队列，以减少温室气体排放

• 批准号: 577207-2022
• 负责人: Yang, CancanC
• 金额: $ 8.76万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749280
• 关键词: Operationalizing; connected; automated; truck; platooning

Over the next decades, truck freight transport is expected to undergo revolutionary changes with the deployment of 'Connected and Autonomous Vehicles' (CAV) technologies. This transformation is expected to reduce fuel consumption and GHG emissions at both the: 1) vehicle level: considering the cooperative operation of platooned trucks with reduced air drag; and 2) network level: adopting efficient use of highways through traffic congestion mitigation. While this is encouraging, the associated infrastructure planning and resources required to accommodate the expected pressures on our highway systems have fallen behind. To address this gap, the objective of the proposed project is to provide answers to the following two questions: 1) What truck platoon control strategies, traffic management, and infrastructure adaptations are needed to realize the full potential of CAV's emission benefits? 2) How much, realistically, CAV-enabled GHG abatement can be achieved by 2050? Addressing these questions requires a concerted effort from the policymakers, practitioners, and researchers from five distinct disciplines: Structures, Transportation, Aerodynamics, Optimization, and AI/Machine Learning, which compromise the research team and partnership members.As a result, the proposed project will generate experimental data and analytical tools to enable both infrastructure- and transportation-related solutions to the challenges of rolling out truck platooning on existing Canadian highway systems. These solutions include 1) formulation of infrastructure safety sensible regulations on platoon operation; 2) development of adaptation measures that will future-proof Canadian highway infrastructure to proliferate the CAV's GHG benefits to the national scale; 3) optimization of vehicle-level platoon control strategies and network-level traffic management to maximize GHG reductions. The proposed partnership will create a unique knowledge-sharing platform to assist policymakers and stakeholders in navigating the complex pathways for CAV technologies - allowing for transformational reductions in GHG emissions within Canadian road freight transportation.

未来几十年，随着“联网和自动驾驶车辆”（CAV）技术的部署，卡车货运预计将发生革命性的变化。这一改造预计将在以下两个方面减少燃料消耗和温室气体排放：1）车辆层面：考虑队列卡车的协同运行，减少空气阻力； 2) 网络层面：通过缓解交通拥堵来有效利用高速公路。虽然这令人鼓舞，但适应我们高速公路系统预期压力所需的相关基础设施规划和资源却已经落后。为了解决这一差距，拟议项目的目标是回答以下两个问题： 1) 需要哪些卡车队列控制策略、交通管理和基础设施改造来实现 CAV 排放效益的全部潜力？ 2) 到 2050 年，CAV 能够实现多少温室气体减排？解决这些问题需要来自五个不同学科的政策制定者、从业者和研究人员的共同努力：结构、运输、空气动力学、优化和人工智能/机器学习，这会损害研究团队和合作伙伴成员的利益。因此，拟议的项目将生成实验数据和分析工具，以提供基础设施和运输相关的解决方案，以应对在加拿大现有高速公路系统上推出卡车队列的挑战。这些解决方案包括 1) 制定有关排操作的基础设施安全合理法规； 2) 制定适应措施，使加拿大高速公路基础设施面向未来，将 CAV 的温室气体效益扩大到全国范围； 3）优化车辆级编队控制策略和网络级交通管理，以最大限度地减少温室气体排放。拟议的合作伙伴关系将创建一个独特的知识共享平台，帮助政策制定者和利益相关者探索 CAV 技术的复杂路径，从而实现加拿大公路货运中温室气体排放的转型减少。