Microscopic agent-based traffic simulation is an important tool for the efficient and safe resolution of various traffic challenges accompanying the introduction of autonomous vehicles on the roads. Both the variety of questions that can be asked and the quality of answers provided by simulations, however, depend on the underlying models. In mixed traffic, the two most critical models are the models describing the driving behaviour of humans and AVs, respectively. This paper presents AVDM (Autonomous Vehicle Driving Model), a hierarchical AV behaviour model that allows the holistic evaluation of autonomous and mixed traffic by unifying a wide spectrum of AV functionality, including long-term planning, path planning, complex platooning manoeuvres, and low-level longitudinal and lateral control. The model consists of hierarchically layered modules bidirectionally connected by messages and commands. On top, a high-level planning module makes decisions whether to join/form platoons and how to follow the vehicle’s route. A platooning manoeuvres layer guides involved AVs through the manoeuvres chosen to be executed, assisted by the trajectory planning layer, which, after finding viable paths through complex traffic conditions, sends simple commands to the low-level control layer to execute those paths. The model has been implemented in the BEHAVE mixed traffic simulation tool and achieved a 92% success rate for platoon joining manoeuvres in mixed traffic conditions. As a proof of concept, we conducted a mixed traffic simulation study showing that enabling platooning on a highway scenario shifts the velocity-density curve upwards despite the additional lane changing and manoeuvring it induces.

基于微观代理的交通模拟是有效和安全地解决伴随在道路上引入自动驾驶汽车而带来的各种交通挑战的重要工具。但是，可以提出的各种问题和模拟提供的答案的质量均取决于基础模型。在混合交通中，两个最关键的模型分别是描述人类和AV驾驶行为的模型。本文介绍了AVDM（自动驾驶模型），这是一种分层的AV行为模型，通过统一广泛的AV功能（包括长期计划，路径规划，复杂的排队动作和低速驾驶），可以对自治和混合交通进行整体评估。级别的纵向和横向控制。该模型由通过消息和命令双向连接的分层模块组成。最重要的是，一个高级计划模块将决定是否加入/组排以及如何遵循车辆路线。排队操纵层在轨迹规划层的辅助下，引导相关的AV车辆通过选择执行的操纵，在通过复杂交通状况找到可行的路径后，将简单的命令发送到低层控制层以执行这些路径。该模型已在BEHAVE混合交通模拟工具中实现，并且在混合交通条件下的排联队成功率达到92％。作为概念证明，