Longitudinal Control for Connected and Automated Vehicles in Contested Environments
Longitudinal Control for Connected and Automated Vehicles in Contested Environments
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DOI：
10.3390/electronics10161994
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发表时间：
2021-08-01
期刊：
影响因子：
2.9
通讯作者：
Noei, Mohammadreza
中科院分区：
文献类型：
--
作者：
Noei, Shirin;Parvizimosaed, Mohammadreza;Noei, Mohammadreza
汽车工程师协会（SAE）定义了六个级别的驾驶自动化，从0级到5级。自动驾驶系统执行3-5级自动驾驶车辆的整个动态驾驶任务。将动态驾驶任务从驾驶员委托给自动驾驶系统可以消除因驾驶员错误而导致的撞车事故。在道路使用者和专用于自动驾驶系统的车辆之间共享状态、共享意图、寻求协议或共享规定性信息可以进一步增强动态驾驶任务性能、安全性和交通运营。在实验室环境、测试轨道或公共道路中测试协作自动驾驶系统之前，需要进行广泛的模拟，以降低运营成本并达到可接受的风险水平。可以使用车辆动力学仿真工具（例如，CarMaker和CarSim）或交通微观模拟工具（例如，Vissim和Aimsun）。车辆动力学仿真工具主要用于小规模的验证和确认，而交通微观仿真工具主要用于大规模的验证。车辆动力学模拟工具可以模拟每个场景中仅几个车辆的纵向、横向和垂直动力学（例如，在CarMaker中最多10辆车，在CarSim中最多20辆车）。传统的交通微观仿真工具可以在不模拟车辆动力系统的情况下模拟每个场景中许多车辆的车辆跟驰、换道和间隙接受行为。车辆动力学仿真工具比交通微观仿真工具计算密集，但更准确。由于软件架构或计算能力的限制，简化对流交通微观仿真工具的基础假设可能很久以前就已经是一种必要的妥协。因此，需要一种仿真工具来优化计算复杂度和准确度，以合理的准确度模拟每个场景中的许多车辆。本研究提出了一种交通微观仿真工具，采用简化的车辆动力系统模型和基于模型的故障检测方法，在噪声和未知输入下的每个仿真时间步长以合理的精度模拟许多车辆。我们的交通微观仿真工具考虑了驾驶员特征，车辆模型，等级，路面条件，操作模式，车辆间通信漏洞和交通条件，以合理的精度估计纵向控制变量，在每个模拟时间步长为许多传统车辆，专用于自动驾驶系统的车辆，以及配备合作自动驾驶系统的车辆。建议的车辆跟踪模型和纵向控制功能进行了验证，14个车型，手动，自动化和合作自动化模式下的两个驾驶时间表下的三个恶意故障幅度传输的加速度。
The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3-5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.