Real-time optimal intersection control system for automated/cooperative vehicles

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جزئیات بیشتر

انتشار

۲۰۲۱

پایگاه داده

نشریه الزویر

نوع نگارش مقاله

مقاله پژوهشی

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scopus – master journals – JCR

ایمپکت فاکتور

۴٫۲۷۶ در سال ۲۰۲۰

شاخص H_index

۲۶ در سال ۲۰۲۱

شاخص SJR

۰٫۹۰۱ در سال ۲۰۲۰

شاخص Quartile (چارک)

Q1 در سال ۲۰۲۰

مدل مفهومی

ندارد

پرسشنامه

ندارد

متغیر

ندارد

رفرنس

دارد

قوانین استفاده

خرید محصول توسط کلیه کارت های شتاب امکان پذیر است و بلافاصله پس از خرید، لینک دانلود محصول در اختیار شما قرار خواهد گرفت و هر گونه فروش در سایت های دیگر قابل پیگیری خواهد بود.

توضیحات مختصر محصول
Real-time optimal intersection control system for automated/cooperative vehicles

فهرست مطالب مقاله:

Abstract

Automated vehicles (AVs)  are  an  emerging technology in  the automotive industry. The numerous research efforts that are  being dedicated to the development of these systems makes them achievable in the near future. The need to make this technology mature is jus- tified by  potential safety gains from the elimination of human error, the enhancement of mobility through the reduction of  congestion, and the protection of  the environment through the reduction in  vehicle emissions. Algorithms are   needed to deliver optimal and/or sub-optimal solutions for  situations and/or scenarios that AVs would encounter in  the field. In this paper, an  attempt to optimize the movement of AVs through intersec- tions is developed. The  developed model is a real-time optimization problem subjected to dynamic constraints (i.e., ordinary differential equations governing the motion of a vehicle) and static constraints (i.e., maximum achievable velocities). By virtue of the Lagrangian for- mulation used in  Pontryagin’s minimum principle and convex optimization, the solution that minimizes the trip time is obtained. This  logic  is simulated and compared to the oper- ation of a roundabout, an all-way stop sign, and a traffic-signal-controlled intersection. The results demonstrate that a 55% reduction in  delay is  achievable compared to the best of these three intersection control strategies, on  average. An  interesting byproduct of  this new logic  is  a 43% reduction in  fuel  consumption (reduction from an  average of  ۲۰۰ mL to 115 mL) and CO2  emissions (a reduction from 445 g for  the roundabout to 265 g).

بخشی از متن مقاله:
  1. Introduction

 

Since  the introduction of automation, numerous systems that used to  require human intervention are  becoming auto- mated. Examples of these systems include auto manufacturing systems, subway trains, and automated vehicles (AVs). In recent years, computers have become integral components of vehicles, handling various tasks automatically, from meeting the stoichiometric coefficient of the combustion reaction in  the engine to  cruise control. Substantial research efforts are being dedicated to  the enhancement of these systems (de  La Fortelle, 2015; Zohdy and Rakha,  ۲۰۱۲; Li and Wang, 2012; Azimi   et al.,  ۲۰۱۳; Rakha and Kamalanathsarma, 2011; Lee  and Park,  ۲۰۱۲; Le  et al.,  ۲۰۱۳; Abdul   Aziz  et al.,  ۲۰۱۲;

Ye et al., 2015; Roozemond, 2001). The ultimate goal  behind this trend is to make vehicles truly autonomous, and the prin- cipal  motivation behind this effort is the expected safety, economic and mobility advantages associated with the introduc- tion of automated/autonomous vehicles. At this stage various prototypes exist. Yet, the technology implemented is still  not mature enough for mass field  implementation. Considerable development in algorithms is required so that the vehicle per- forms as expected when facing any  predicted or unpredicted situations. The legislative side  of the matter is also  a challenge. For example, who would be  accountable in the case  of incidents, and to  whom or what should a driver’s license be  given?

One  of the challenging scenarios researchers need to  address is how automated vehicles should optimally traverse an intersection that is not  equipped with any  control devices. Numerous solutions for this problem have been and are still  being developed. One  of these solutions is the Cooperative Adaptive Cruise Control (CACC) (Massera et al., 2016) system and par- ticularly its  intersection version (iCACC) (Zohdy and Rakha,  ۲۰۱۶). The main objective of this system is to optimize vehicle trajectories while at the same time reducing vehicle delay. The  optimality characteristic of this method for  this particular situation is not  proven. Medina et al. (2015) tried to address this issue for a T-intersection. A mathematical model was  devel- oped and linearized. Collision avoidance was  implemented but no dynamic constraints were used. Examples of dynamic con- straints include acceleration constraints and velocity constraints. The authors were successful in solving this problem for six vehicles. Gratner and Stefan (2016) simulated a T-Intersection where only  two vehicles were present. The study did not  con- sider the effects of other vehicles on the road (i.e., there is no car-following model). The aim  was  to develop a collision pre- diction system. The computational burden of the system prevents it from being real-time and thus responsive in a dynamic environment. The developed model features static constraints on accelerations and nonlinear equations of motion. Zhu et al. (2009) developed a methodology for making vehicles cross an intersection cooperatively by computing the required speed. Their  paper focuses on scheduling and no developed dynamic model was  provided. Li et al. (2013) developed a reservation- based autonomous intersection control in VISSIM. The tool  uses purely kinematic equations, and the vehicles communicate only  with a centralized intersection controller. This controller determines the passing sequence of the approaching vehicles on  a first-come, first-served basis.

In this paper, a comprehensive and extended vehicle model is proposed. This model considers real  traffic pattern (i.e. Car- Following model, acceleration constraints obtained from real  driver data, constraints on  velocity based on  the geometry of the road). The  time series of the vehicle’s position, velocity, and acceleration are  computed. The  vehicle delay, number of stops, fuel  consumption and CO2  emissions are  also  computed as part of the results in a post-processing step.

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