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Register a new userLongitudinal Control of Vehicles in Traffic Microsimulation
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Current state-of-art traffic microsimulation tools cannot accurately estimate safety, efficiency, and mobility benefits of automated driving systems and vehicle connectivity because of not considering physical and powertrain characteristics of vehicles and resistance forces. This paper proposes realistic longitudinal control functions for autonomous vehicles with and without vehicle-to-vehicle communications and a realistic vehicle-following model for human-driven vehicles, considering driver characteristics and vehicle dynamics. Conventional longitudinal control functions apply a constant time gap policy and use empirical constant controller coefficients, potentially sacrificing safety or reducing throughput. Proposed longitudinal control functions calculate minimum safe time gaps at each simulation time step and tune controller coefficients at each simulation time step during acceleration and deceleration to maximize throughput without compromising safety.
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This paper studies the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the anticipative mode, more immediately beneficial when automated vehicle fleet penetration is low, and 2. the connected mode, beneficial in coordinated car-following scenarios and high automated vehicle penetrations appropriate for autonomous vehicle specific applications. Probabilistic constraints handle safety considerations, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Real traffic scenarios are then modeled using time headway distributions from traffic data. To study impact over a range of demands, we vary input vehicle volume from low to high and then vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with automated vehicles introduced which utilize probabilistic constraints to balance safety and traffic compactness, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10% - 20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3% - 9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.
Cooperative vehicular technology in recent times has aided in realizing some state-of-art technologies like autonomous driving. Effective and efficient prioritization of emergency vehicles (EVs) using cooperative vehicular technology can undoubtedly aid in saving property and lives. Contemporary EV prioritization, called preemption, is highly dependent on existing traffic infrastructure. Accessing crucial decision parameters for preemption like speed, position and acceleration data in real-time is almost impossible in current systems. The connected vehicle can provide such data in real-time, which makes EV preemption more responsive and effective. Also, autonomous vehicles can help in optimizing the timing in traffic phases and minimize human-related loss like higher headway times and inconsistent inter-vehicle spacing when following each other. In this paper, we introduce self-coordinating a decentralized traffic control system termed as Virtual Traffic Light plus for Emergency Vehicle (VTL+EV) to prioritize EVs in an intersection. The proposed system can expedite EVs movement through intersections and impose minimal waiting time for ordinary vehicles. The VTL+EV algorithm also can improve overall throughput making an intersection more efficient.
Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.
Originally, the decision and control of the lane change of the vehicle were on the human driver. In previous studies, the decision-making of lane-changing of the human drivers was mainly used to increase the individuals benefit. However, the lane-changing behavior of these human drivers can sometimes have a bad influence on the overall traffic flow. As technology for autonomous vehicles develop, lane changing action as well as lane changing decision making fall within the control category of autonomous vehicles. However, since many of the current lane-changing decision algorithms of autonomous vehicles are based on the human driver model, it is hard to know the potential traffic impact of such lane change. Therefore, in this study, we focused on the decision-making of lane change considering traffic flow, and accordingly, we study the lane change control system considering the whole traffic flow. In this research, the lane change control system predicts the future traffic situation through the cell transition model, one of the most popular macroscopic traffic simulation models, and determines the change probability of each lane that minimizes the total time delay through the genetic algorithm. The lane change control system then conveys the change probability to this vehicle. In the macroscopic simulation result, the proposed control system reduced the overall travel time delay. The proposed system is applied to microscopic traffic simulation, the oversaturated freeway traffic flow algorithm (OFFA), to evaluate the potential performance when it is applied to the actual traffic system. In the traffic flow-density, the maximum traffic flow has been shown to be increased, and the points in the congestion area have also been greatly reduced. Overall, the time required for individual vehicles was reduced.
A system of cooperative unmanned aerial vehicles (UAVs) is a group of agents interacting with each other and the surrounding environment to achieve a specific task. In contrast with a single UAV, UAV swarms are expected to benefit efficiency, flexibility, accuracy, robustness, and reliability. However, the provision of external communications potentially exposes them to an additional layer of faults, failures, uncertainties, and cyber-attacks and can contribute to the propagation of error from one component to other components in a network. Also, other challenges such as complex nonlinear dynamic of UAVs, collision avoidance, velocity matching, and cohesion should be addressed adequately. The main applications of cooperative UAVs are border patrol; search and rescue; surveillance; mapping; military. Challenges to be addressed in decision and control in cooperative systems may include the complex nonlinear dynamic of UAVs, collision avoidance, velocity matching, and cohesion. In this paper, emerging topics in the field of cooperative UAVs control and their associated practical approaches are reviewed.
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