کنترل سرعت هوشمند وسایل نقلیه خودمختار
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کنترل سرعت هوشمند وسایل نقلیه خودمختار

عنوان فارسی مقاله: کنترل سرعت طولی هوشمند وسایل نقلیه خودمختار در تعامل با وسایل نقلیه رانده شده توسط انسان حواس پرت
عنوان انگلیسی مقاله: Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری
کلمات کلیدی فارسی: وسایل نقلیه خودمختار، نظارت بر حواس پرتی، کنترل سرعت، کنترل پیش بینی مدل، شبکه های عصبی پیچشی
کلمات کلیدی انگلیسی: Autonomous vehicle, distraction monitoring, velocity control, model predictive control, convolutional neural networks
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2954863
دانشگاه: School of Information Science and Engineering, Southeast University, Nanjing 210096, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 15
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14044
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Collaborative Framework


III. Smart Longitudinal Velocity Control of AV to Avoid Distracted Driver


IV. Benefit Analysis of Collaborative Framework


V. Simulations


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


With the development of commercialized autonomous vehicles (AVs), the interaction between AVs and human-driven vehicles has become increasingly important. Nevertheless, on the one hand, complex driver behaviors like distraction are hard to detect by AVs, which may lead to traffic accidents because of the late alert to the following vehicles. On the other hand, advanced techniques such as the real-time image or video processing and vehicle-to-vehicle (V2V) communications make it possible to let AVs receive monitoring signals from nearby vehicles, predict the latent risks, and make smart control to avoid the vehicles driven by distracted drivers. Hence, in this paper, we envisage a collaborative framework integrating human driver distraction monitoring, V2V communications, and AV velocity control. Then, we design the smart velocity control of AVs by taking into consideration the distraction behaviors of the drivers in the human-driven vehicles, and by formulating it as a feasible optimization problem based on model predictive control (MPC) strategies. Furthermore, we analyze the safety benefits that the collaborative framework could help improve on the condition of preserving traffic performance. Finally, we implement the contrast tests of real-time evaluation on driver distraction monitoring based on convolutional neural networks (CNNs) and perform simulations of smart velocity control strategies of the AV at avoiding the distracted driver and reducing rear-end collisions. Through the analysis and the simulations, we show our framework could increase the safety regions, reduce the rear-end collisions, and thus increase the safety of the whole transportation networks.


Introduction


In recent years, intelligent transportation system (ITS) has attracted much attention from both academia and industry, which is expected to improve transportation safety and mobility [1]–[3]. As one of the significant technologies, autonomous driving systems have been aimed to bring us safety, as well as autonomy, namely making driving decisions independently [4]–[6]. Meanwhile, much attention has been paid to the theoretical research and industrial practice of autonomous vehicles (AVs) [7]–[9]. Nevertheless, AVs and human drivers are expected to coexist for a long time. Thus, it is important to consider their interactions [10], [11]. In particular, on the one hand, the driving manner of AVs may seem to be stubborn without face-to-face communications between human drivers. For instance, many AVs only use on-board sensors to perceive the environment thus having difficulties anticipating the motion of surrounding human-driven vehicles [12]. On the other hand, the implicit and complex states and behaviors of human drivers like distractions and fatigue, which are hard to detect by the AVs, may result in sudden brakes and subsequent accidents because of the late alert to the following AVs. Moreover, the movements of human-driven vehicles usually involve a high level of uncertainty and randomness [13], and sometimes human drivers dangerously trade off safety for throughput [14], both bringing potential risks to the AVs.

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