[论文解读] Cross-Platform Simulation Architecture with application to truck platooning impact assessment

Andrés Ladino, Xiao Lin+3 more|arXiv (Cornell University)|May 19, 2021

Traffic control and management参考文献 6被引用 2

一句话总结

本文提出了一种跨平台仿真架构，通过平台无关的逻辑层同步车辆动力学与决策行为，实现在Vissim和SymuVia中一致的卡车编队行驶行为。初步结果表明，在启停和切入场景下，编队运行稳定，保持了适当的车间距，并具备自适应加速度响应能力。

ABSTRACT

Simulation-based traffic impact assessment studies of advanced technologies such as truck platooning need to be carried out to ascertain their benefits for traffic efficiency, safety and environment. To reduce uncertainty in the results of such simulation-based studies, the same simulation studies can be performed in different simulation software. Many traffic simulation software packages (Aimsun, SymuVia, Vissim, SUMO) are currently available for traffic impact assessment of new technologies such as truckplatooning. However, to fully model and simulate the functionalities of such advanced technologies in different simulation environments, several extensions need to be made to the simulation platforms. In most cases, these extensions have to be programmed in different programming languages (C++, Python) and each simulator has its own simulator specific API. This makes it difficult to reuse software written for a specific functionality in one simulation platform in a different simulation platform. To overcome this issue, this paper presents a novel architecture for cross-platform simulation. The architecture is designed such that a specific functionality such as truck-platooning or any other functionality is made platform independent. We designed a cross-platform architecture for simulating a truck-platooning functionality using Vissim and SymuVia simulation software to determine the traffic flow effects of multi-brand truck platooning in the context of the EU project ENSEMBLE. In this draft paper, we present the structure of the framework as well as some preliminary results from a simple simulation performed with the cross-platform simulator.

研究动机与目标

• 解决在评估卡车编队时，不同交通仿真器之间仿真结果不一致的挑战。
• 开发一种与平台无关的框架，实现在异构仿真环境中的编队功能。
• 实现编队行为的跨平台验证，减少仿真器特定实现带来的偏差。
• 支持ENSEMBLE项目的目标，即评估多品牌卡车编队对交通流、安全性和效率的影响。

提出的方法

• 采用模块化分层架构，将决策逻辑与仿真器特定代码分离，实现在不同平台间的复用。
• 该框架使用通用应用程序接口连接Vissim和SymuVia，同步车辆状态和控制指令。
• 战术层处理环境输入（例如，车间距变化、切入行为），并决定编队行为。
• 底层控制实时更新车辆状态（位置、速度、加速度），并将结果反馈给仿真器。
• 系统支持动态事件，如紧急制动和手动退出编队，自动恢复至人工驾驶行为。
• 共享的数据交换协议确保了尽管底层API和编程语言存在差异，仿真器间行为仍保持一致。

实验结果

研究问题

• RQ1 如何在Vissim和SymuVia等不同交通仿真平台中一致地实现卡车编队功能？
• RQ2 该跨平台架构在启停和切入等动态交通事件下，能在多大程度上保持编队稳定性？
• RQ3 在多品牌场景下，异构卡车特性及未知的编队算法如何影响编队行为？
• RQ4 统一的逻辑层是否能够减少基于仿真的编队技术影响评估中的平台特异性偏差？

主要发现

• 在启停仿真中，编队在减速时保持了稳定的车间距，间隙极小，并在加速时迅速恢复。
• 在切入事件中，跟随车辆动态调整加速度，成功维持了编队凝聚力，未发生碰撞。
• 在制动过程中，编队中第二辆车与第一辆车之间的车间距显著减小，但停车后迅速恢复。
• 系统成功处理了紧急制动和手动退出事件，恢复至人工驾驶行为，未引发仿真不稳定。
• 该框架在Vissim和SymuVia中均表现出一致的编队行为，验证了跨平台方法的有效性。
• 该架构实现了编队逻辑在不同仿真器间的复用，降低了开发工作量，并提升了结果的可比性。