Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In this paper, we propose a cooperative algorithm that utilizes both connected and autonomous vehicles (CAV) and schedule-driven traffic control to create better traffic flow in the city. The algorithm enables an intersection scheduling agent to adjust the arrival time of an approaching platoon through use of wireless communication to control the velocity of vehicles. The sequence of approaching platoons is thus shifted toward a new shape that has smaller cumulative delay. We demonstrate how this algorithm outperforms the original approach in a real-time traffic signal control problem.
Taking advantage of autonomously controlled vehicles, and the use of information from connected vehicles for intersection control has been investigated in several researches. The trajectory of fully autonomous vehicles can be manipulated to optimize an objective function @cite_5 @cite_0 @cite_12 @cite_10 . Those approaches can achieve either better safety or efficiency through interaction between intersections and vehicles. @cite_3 propose an extension of AIM to enable vehicles to apply motion planning for optimizing speed.
@cite_3 @cite_0 @cite_5 @cite_10 @cite_12
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The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study by examining the relationship between the precision of cars' motion controllers and the efficiency of the intersection controller. We propose a planning-based motion controller that can reduce the chance that autonomous vehicles stop before intersections, and show that this controller can increase the efficiency of the intersection control mechanism. Cooperative driving technology with intervehicle communication has attracted increasing attention recently. It aims to improve driving safety and efficiency using appropriate motion scheduling of all the encountered vehicles. Under cooperative driving control, the motion of individual vehicles could be conducted in a safe, deterministic, and smooth manner. This is particularly useful to heavy-duty vehicles since their acceleration deceleration capacity is relatively low. Specifically in this paper, cooperative driving at blind crossings (crossings without traffic lights) is studied. A concept of safety driving patterns is proposed to represent the collision-free movements of vehicles at crossings. The solution space of all allowable movement schedules is then described by a spanning tree in terms of safety driving patterns; four trajectory planning algorithms are formulated to determine the driving plans with least execution times using schedule trees. The group communication strategy for intervehicle networks is also analyzed. Finally, simulation studies have been conducted, and results demonstrate the potentiality and usefulness of the proposed algorithms for cooperative driving at blind crossings This paper develops a real-time traffic signal optimization algorithm in the presence of connected and autonomous vehicles (CAVs). The proposed algorithm leverages information from connected vehicl... Under the Connected Vehicles (CV) environment, it is possible to create a Cooperative Vehicle Intersection Control (CVIC) system that enables cooperation between vehicles and infrastructure for effective intersection operations and management when all vehicles are fully automated. Assuming such a CVIC environment, this paper proposed a CVIC algorithm that does not require a traffic signal. The CVIC algorithm was designed to manipulate individual vehicles' maneuvers so that vehicles can safely cross the intersection without colliding with other vehicles. By eliminating the potential overlaps of vehicular trajectories coming from all conflicting approaches at the intersection, the CVIC algorithm seeks a safe maneuver for every vehicle approaching the intersection and manipulates each of them. An additional algorithm was designed to deal with the system failure cases resulting from inevitable trajectory overlaps at the intersection and infeasible solutions. A simulation-based case study implemented on a hypothetical four-way single-lane approach intersection under varying congestion conditions showed that the CVIC algorithm significantly improved intersection performance compared with conventional actuated intersection control: 99 and 33 of stop delay and total travel time reductions, respectively, were achieved. In addition, the CVIC algorithm significantly improved air quality and energy savings: 44 reductions of CO2 and 44 savings of fuel consumption.