Decoupled cooperative trajectory optimization for connected highly automated vehicles at urban intersections

Krajewski, Robert; Themann, Philipp; Eckstein, Lutz

doi:10.1109/ivs.2016.7535470

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…In [24], He et al proposed a novel approach to erase on-road lane change by allowing the vehicle to turn onto any downstream lane at the intersection. Then in [25], [26], quality-of-experience (QoE) was taken into consideration. In [25], Krajewski et al formulated the traffic as a graph, and found a solution keeping the balance of passengers' driving comfort of global optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Then in [25], [26], quality-of-experience (QoE) was taken into consideration. In [25], Krajewski et al formulated the traffic as a graph, and found a solution keeping the balance of passengers' driving comfort of global optimization. Later in [26], Dai et al proposed a QoE-oriented approach using convex optimization.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, the only algorithm considering the influence of channel condition in the AIC system is proposed in Ref. [25]. However, when the channel condition is unstable, its inconsistency of information between EC server and vehicles can result in collisions that the EC server cannot predict.…”

Section: Introductionmentioning

confidence: 99%

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Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Zhang

Liu

et al. 2020

IEEE Access

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Traffic light-free intersection control is envisioned to alleviate congestion and manage vehicles intelligently. With the help of vehicle-to-infrastructure (V2I) communication and edge computing (EC), vehicles are instructed to cross the intersection with high vehicle safety and traffic efficiency without traffic lights. However, unstable channel conditions can lead to the reduction of traveling safety. In this paper, we propose a robust autonomous intersection control (AIC) approach with global optimization scheduling, which protects connected autonomous vehicles from collision under any channel conditions while achieving decent traffic efficiency. In particular, we propose an AIC model that gives vehicles certain autonomy under centralized control to ensure the traveling safety in case of some emergencies. By conducting an interference graph, we simplify the AIC problem as a weighted maximal clique problem with restriction. To improve the fairness and efficiency in terms of vehicle passage, multiple factors such as travel delay, traffic of the current lane and passengers' desired speed are considered. Furthermore, we propose a heuristic algorithm to search the solution space. For further optimization, a particle swarm optimization algorithm is proposed, achieving a near-optimal result with adjustable overhead. Finally, we build the simulation model and conduct a comparative performance evaluation. Simulation results demonstrate the superiority of our proposed scheme. INDEX TERMS Connected autonomous vehicles, autonomous intersection control, edge computing, robustness, vehicular network, optimization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Zhang

Liu

et al. 2020

IEEE Access

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“…Managing AV traffic has been studied by many researchers. They used AVs to collect and share information based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications (e.g., [1], [2], [3], [4], [5], [6], and [7]). Despite the importance of managing AVs, changing all vehicles to autonomous versions will take time.…”

Section: Introductionmentioning

confidence: 99%

Using Vehicle-Mounted Camera to Collect Information for Managing Mixed Traffic

Namazi

Holthe-Berg

Lofsberg

et al. 2019

2019 15th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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With increasingly rapid advances in the field of producing modern and autonomous vehicles, the need for intelligent traffic management systems, which take advantage of the vehicle's abilities to sense and communicate, has increased. A considerable amount of literature has been published on managing traffic that includes only autonomous vehicles. However, changing all vehicles to autonomous versions is a longterm process. In the near future, traffic will be a mixture of human-driven and autonomous vehicles. To date, few studies have investigated mixed traffic in intelligent management systems. The main objective of this research is to study the possibility of using a vehicle-mounted camera to sense and collect the required traffic data of the surrounding vehicles in mixed traffic. To achieve this, a vehicle with a monocular camera is used to collect image information for detecting and counting the vehicles in different lanes and estimating their distance and speed on the defined route. The results indicate that our proposed image processing algorithms can acquire the information needed for intelligent traffic management systems.

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“…As to V2V-based trajectory control, several control algorithms have been proposed at an isolated intersection without a traffic signal. Some researchers have argued that the application of CAV technologies to traffic control has the potential to remove traditional signal controllers at isolated intersections, if reliable connectivity of V2V information was provided (17)(18)(19)(20). Such proposed control algorithms were designed with the aim of avoiding collisions and improving traffic efficiency, that is, in reduced travel time and total delay.…”

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confidence: 99%

Optimal Platoon Trajectory Planning Approach at Arterials

Liu

Wang

Hoogendoorn

2019

Transportation Research Record: Journal of the Transportation R

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Cooperative (automated) vehicles have the potential to enhance traffic efficiency and fuel economy on urban roads, especially at signalized intersections. An optimal control approach to optimize the trajectories of cooperative vehicles at fixed-timing signalized intersections along an arterial is presented. The proposed approach aims to optimize throughput first, and then to maximize comfort while minimizing travel delay and fuel consumption. The proposed approach is flexible in dealing with both quadratic and more complex cost functions. Assuming fixed timing signal control in a cycle and vehicle-to-infrastructure communication, the red phase is taken into account in position constraints for vehicles that cannot pass the intersection in the green phase. Safety is guaranteed by constraining the inter-vehicle distance larger than some desired value. The approach is scalable and can be used for joint trajectory planning of one platoon approaching another stationary platoon. It can also be extended to multiple intersections with fixed signal plans. To verify the performances of the controlled platoon, simulation under three different traffic scenarios is conducted, namely: an isolated intersection with/without downstream vehicle queues, and platoon control at multiple intersections. Three baseline scenarios without control are also designed to compare performances in relation to both mobility and fuel consumption in each controlled scenario. The results demonstrate that the controlled vehicles generate plausible behavior under control objectives and constraints. Moreover, the consideration of downstream vehicle queues and the application at both an isolated signalized intersection and arterial corridors on urban roads verify the flexible characteristics of the control framework.

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Decoupled cooperative trajectory optimization for connected highly automated vehicles at urban intersections

Cited by 14 publications

References 15 publications

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles

Using Vehicle-Mounted Camera to Collect Information for Managing Mixed Traffic

Optimal Platoon Trajectory Planning Approach at Arterials

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