Optimal Access Management for Cooperative Intersection Control

Medina, Alejandro Ivan Morales; Creemers, Falco; Lefeber, Erjen; Wouw, Nathan van de

doi:10.1109/tits.2019.2913589

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…Furthermore, mathematical modeling approaches have been studied by formulating optimization problems [11]- [17]. The optimization problem is formulated to minimize vehicle travel time and vehicle crossing time within an intersection that uses vehicle positions and speeds to numerically solve under constraints such as collision avoidance and speed limits.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Effect of Optimization Time-Scale on Learning-Based Cooperative Merging Control at a Nonsignalized Intersection

et al. 2023

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Automated driving and the widespread use of large-scale communication infrastructure are expected to facilitate highly cooperative driving. Although considerable research has focused on developing efficient cooperative control methods for nonsignalized intersections, the effect of cooperative control for conflicting target vehicles on future traffic flow is yet to be investigated. Therefore, we aim to investigate whether the impact of such cooperative control on future traffic should be considered. We established a traffic simulator and several machine-learning methods to select the optimal cooperative method. The decision tree and deep neural network were trained on two indices that evaluate short-term/long-term predictive control: to minimize the travel time of the (1) conflicting vehicles and (2) all vehicles including future traffic flow. Simulation analysis results indicated that there were no significant differences in the total travel times between these indices. This finding indicates that efficient traffic flow, which includes future traffic flow, is achievable by short-term cooperative control methods that can be established easily.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Effect of Optimization Time-Scale on Learning-Based Cooperative Merging Control at a Nonsignalized Intersection

et al. 2023

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“…The ICU comprehensively considers all vehicle information and assigns an appropriate passing time to each vehicle. In order to capture the effective relative motion law of vehicles and describe the dynamic platoon related to each lane at intersections, an advanced hybrid queuing model was proposed in [14]. This model designed a traffic management method based on model predictive control to minimize the travel time of vehicles at the intersection.…”

Section: A Unsignalized Autonomous Intersection Managementmentioning

confidence: 99%

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Zhou¹,

Shen²,

Wang³

et al. 2022

Preprint

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With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs and connected human-driven vehicles (CHVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs and CAVs, and systemically investigates the unsignalized intersection management strategy from upper decision-making level to lower execution level. Combined with the designed vehicle planning and control algorithm, the unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm, and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs and CHVs is built to design the right of way management for CAVs and CHVs, respectively. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. The intersection management strategy captures the real-world balance between efficiency and safety for potential future intelligent traffic systems.

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“…For instance, Yang et al ( 20 ) proposed an algorithm for finding the optimal departure sequence to minimize the total delay for various stages of connected vehicle technology, namely, conventional vehicles, connected vehicles, and AVs. Similarly, Medina et al ( 21 ) formulated a mixed-integer linear program based on model predictive control that also optimizes the sequence of vehicles crossing an autonomous intersection. Using model predictive control, Kamal et al ( 22 ) showed that the smoothness of traffic flow can be improved as well at signal-free intersections.…”

mentioning

confidence: 99%

“…In some studies, only one vehicle is allowed to pass the merging zone at a time with a fixed speed ( 29 , 30 ), limiting the intersection management efficiency. Other studies have to deal with the complexity induced by mixed-integer decision variables ( 21 , 32 ), which we avoid in our framework because only vehicle acceleration (not necessarily an integer) is considered as the control variable. In addition, it would be beneficial to define a comprehensive objective function that ensures a desired level of system performance for both intersection throughput and traffic smoothness.…”

mentioning

confidence: 99%

Optimal Control of Automated Vehicles for Autonomous Intersection Management With Design Specifications

Wang

Mahlberg

Levin

2022

Transportation Research Record: Journal of the Transportation R

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In this article, we study optimal control of automated vehicles (AVs) at an autonomous intersection with design specifications, where the intersection manager (computer) controls the acceleration/deceleration of AVs within a control region to determine their conflict-free trajectories. We develop a mathematical model to describe the cooperative dynamics of vehicles within the control zone of a signal-free intersection. Further, we formulate an optimization problem with the objective of maximizing traffic throughput while minimizing passenger discomfort. We then address the problem using mathematical programming to yield the optimal control input for each vehicle inside the control zone. We apply the proposed control mechanism for optimally coordinating the movement of AVs at an autonomous intersection subject to physical and safety constraints. In other words, the solution, if implemented, would allow vehicles to pass the intersection with safety guarantees (avoiding collisions), high-level efficiency (maximizing throughput), and good comfort (minimizing discomfort) without deploying traffic lights. In addition, we derive analytical specifications for the design and deployment of autonomous intersections, which appears to have been largely ignored in prior studies. Finally, we conduct a series of numerical experiments to show the effectiveness of the methodology proposed in this article. As the framework developed here is fairly general and can be extended to cover many complex traffic scenarios, we believe that the procedure presented in this article will be useful with the advent of AVs in the near future.

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Optimal Access Management for Cooperative Intersection Control

Cited by 15 publications

References 30 publications

Effect of Optimization Time-Scale on Learning-Based Cooperative Merging Control at a Nonsignalized Intersection

Effect of Optimization Time-Scale on Learning-Based Cooperative Merging Control at a Nonsignalized Intersection

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Optimal Control of Automated Vehicles for Autonomous Intersection Management With Design Specifications

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