Conflict-point formulation of intersection control for autonomous vehicles

Levin, Michael W.; Rey, David

doi:10.1016/j.trc.2017.09.025

Cited by 145 publications

(84 citation statements)

References 20 publications

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“…Ding et al [8] designed a method to predict the potential for yellow-light or red-light-running and determine a balance between algorithm e ciency and computational time. Levin and Rey [9] developed a reservation-based intersection control protocol and improved its applicability to situations with a large number of vehicles. Li et al [10] proposed a trajectory planning method based on shooting heuristic [11,12] with a piecewise function; the simulation revealed that the model could decrease the stop-and-go movements.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Fang

Yang

Wang

et al. 2020

Journal of Advanced Transportation

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Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

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Section: Introductionmentioning

confidence: 99%

Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Fang

Yang

Wang

et al. 2020

Journal of Advanced Transportation

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show abstract

“…Levin and Rey [18] proposed a conflict point formulation for optimizing intersection throughput referred to as the AIM* protocol. As in Altché and De La Fortelle [1], the IM decides on AVs' entry time and speed but instead of discretizing time, the authors discretize the intersection and focus on conflict points to ensure safety.…”

Section: Intersection Management With Autonomous Vehiclesmentioning

confidence: 99%

“…To coordinate traffic during blue phases, we adapt a mixed-integer programming formulation from Levin and Rey [18] to maximize local pressure. For blue phases, lane service rates can be obtained by solving an optimization problem wherein collision avoidance constraints are imposed at all conflicts point of the intersection.…”

Section: Blue Phasementioning

confidence: 99%

“…Recall that the current time period consists of the interval [t, t + ∆t[. Let t v (c) ≥ t be a decision variable representing the arrival time of vehicle v at point c ∈ ρ v and let τ v (c) ≥ 0 be a decision variable representing the time conflict point c ∈ ρ v is reserved for vehicle v. The values of these variables is determined by the speed assigned and the departure assigned to v as described in Levin and Rey [18].…”

Section: Blue Phasementioning

confidence: 99%

“…This can be formulated as: i∈A n a w i (t) v∈V n i (t) z v . The remaining of the blue phase model is identical to that presented in Levin and Rey [18]. Note that movement capacity is only implicitly represented within the blue phase model.…”

Section: Blue Phasementioning

confidence: 99%

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Blue phase: Optimal network traffic control for legacy and autonomous vehicles

Rey

Levin

2019

Transportation Research Part B: Methodological

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With the forecasted emergence of autonomous vehicles in urban traffic networks, new control policies are needed to leverage their potential for reducing congestion. While several efforts have studied the fully autonomous traffic control problem, there is a lack of models addressing the more imminent transitional stage wherein legacy and autonomous vehicles share the urban infrastructure. We address this gap by introducing a new policy for stochastic network traffic control involving both classes of vehicles. We conjecture that network links will have dedicated lanes for autonomous vehicles which provide access to traffic intersections and combine traditional green signal phases with autonomous vehicle-restricted signal phases named blue phases. We propose a new pressure-based, decentralized, hybrid network control policy that activates selected movements at intersections based on the solution of mixed-integer linear programs. We prove that the proposed policy is stable, i.e. maximizes network throughput, under conventional travel demand conditions. We conduct numerical experiments to test the proposed policy under varying proportions of autonomous vehicles. Our experiments reveal that considerable trade-offs exist in terms of vehicle-class travel time based on the level of market penetration of autonomous vehicles. Further, we find that the proposed hybrid network control policy improves on traditional green phase traffic signal control for high levels of congestion, thus helping in quantifying the potential benefits of autonomous vehicles in urban networks.

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A Combinatorial Dynamic Network Trajectory Reservation Algorithm for Connected Autonomous Vehicles

Levin

2018

Netw Spat Econ

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Conflict-point formulation of intersection control for autonomous vehicles

Cited by 145 publications

References 20 publications

Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Blue phase: Optimal network traffic control for legacy and autonomous vehicles

A Combinatorial Dynamic Network Trajectory Reservation Algorithm for Connected Autonomous Vehicles

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