基于惩罚成本函数的驾驶员反应对车辆通信效率的影响

Q3 Social Sciences
M. Iskandarani
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引用次数: 2

摘要

本研究考察并考虑了影响人机界面(HVI)有效性的三个关键时间因素。一种基于阈值的机制被创建来考虑协同驾驶和先进车辆控制系统(AVCS)的场景。对于AVCS和协同驾驶,所建立的模型考虑了车载机界面时间、人机界面时间和传输时间。为了实现自适应智能,增强HVI设计,提高车辆安全性,提出了一个表示慢速驾驶员反应惩罚成本的阈值函数。惩罚成本函数(PCF)用于使车辆控制系统在驾驶员对安全和警告信息反应缓慢的情况下进行干预和控制。此外,该研究表明,基于avcs的车辆系统总体上响应更快,受PCF函数的影响较小。根据比较图,通过这项工作创建的数学模型允许限制效率值和每个驾驶场景的上限。作为车辆机电一体化的一部分,这将改善更可靠的控制系统的创建,这将影响车辆在合作环境中的相互通信方式。利用MATLAB仿真对数学模型进行了验证。仿真涵盖了0.33和0.5两种极限情况,采用车辆数量递增(10、20、30、40、50)来检验车辆数量增加对通信效率的影响,并检验了AVCS和具有合作的AVCS都具有接近的水平,并在临界值处收敛。成功完成的模拟表明,吞吐量随着车辆数量的增加而下降,尽管在极限情况下,两种场景和驾驶系统的变化几乎相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of Driver Response on Efficiency of Vehicular Communication using Penalty Cost Function (EVCPCF)
This study examines and takes into account three key timing factors that have an impact on the effectiveness of human-machine interfaces (HVI). A threshold-based mechanism is created to account for both cooperative driving and advanced vehicle control system (AVCS) scenarios. For AVCS and cooperative driving, the developed model takes into account on-board machine interface time, human interface time, and transmission time.. A threshold function that represents the penalty cost of a slow driver reaction is presented in order to enable adaptive intelligence, enhance HVI design, and increase vehicle safety. The Penalty Cost Function (PCF) is used to make vehicle control systems intervene and take control in situations where the driver is responding slowly to safety and warning messages. Additionally, this study demonstrates that AVCS-based vehicular systems are more responsive overall and are less impacted by the PCF function than cooperative systems. The mathematical models created through this work allowed for a limiting efficiency value and capping for each driving scenario, according to comparative plots. This will improve the creation of more reliable control systems as part of a vehicle's mechatronics, which will affect how vehicles communicate with one another in a cooperative setting. MATLAB simulation was used to verify the mathematical model. The simulation covered two limiting cases of 0.33 and 0.5 and used incrementing numbers of vehicles (10, 20, 30, 40, 50) to check the impact of increasing vehicle numbers on communication efficiency and examine that both AVCS and AVCS with cooperative will have close levels and converge at limiting values. The successfully completed simulation demonstrated that throughput decreased as the number of vehicles increased, although in the limiting case, both scenarios and the driving system changed virtually by the same percentage.
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来源期刊
Open Transportation Journal
Open Transportation Journal Social Sciences-Transportation
CiteScore
2.10
自引率
0.00%
发文量
19
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