Real-time optimal intersection control system for automated/cooperative vehicles
|نوع نگارش مقاله||
scopus – master journals – JCR
۴٫۲۷۶ در سال ۲۰۲۰
۲۶ در سال ۲۰۲۱
۰٫۹۰۱ در سال ۲۰۲۰
|شاخص Quartile (چارک)||
Q1 در سال ۲۰۲۰
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.
فهرست مطالب مقاله:
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:|
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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