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.

在接下来的几十年中，随着“连接和自动驾驶汽车”（CAV）技术的部署，卡车货运运输有望实现革命性变化。预计这种转换将减少燃油消耗和温室气体排放，这两者都在：1）车辆水平：考虑到空气阻力减少的排队卡车的合作操作；和2）网络级别：通过缓解交通拥堵采用高速公路的有效利用。尽管这令人鼓舞，但相关的基础设施计划和适应我们高速公路系统预期压力所需的资源却落后了。为了解决这一差距，拟议项目的目的是为以下两个问题提供答案：1）需要哪些卡车排控制策略，交通管理和基础设施适应来实现CAV排放益处的全部潜力？ 2）到2050年，实际上可以实现多少cav的温室气体消除？ 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加拿大高速公路系统。这些解决方案包括1）基础设施安全性在排操作中的明智法规； 2）制定适应措施，这些措施将使未来的加拿大高速公路基础设施扩大了CAV的温室气体对国家规模的利益； 3）优化车辆级排控制策略和网络级别的流量管理，以最大程度地减少温室气体。拟议的合作伙伴关系将创建一个独特的知识共享平台，以协助决策者和利益相关者浏览CAV技术的复杂途径 - 允许加拿大道路运输中的温室气体排放转换。