Hierarchical Optimization-based Coordinated Control of Traffic Rules and Mixed Traffic in Multi-Intersection Environments

Patent metadata

Field	Value
Publication number	US-2024331535-A1
Application number	US-202318180888-A
Country	US
Kind code	A1
Filing date	Mar 9, 2023
Priority date	Mar 9, 2023
Publication date	Oct 3, 2024
Grant date	—

Abstract

The present disclosure provides a system and a method for jointly controlling one or multiple connected and automated vehicles (CAVs) and one or multiple human-driven vehicles (HDVs) subject to integer constraints for crossing each of multiple intersections on a road. The method comprises collecting digital representation of states of each of the CAVs, HDVs, and traffic signs, solving an optimization problem jointly optimizing traffic flows based on a macroscopic traffic flow model in a centralized traffic controller (CTC) subject to convex relaxation of the integer constraints, solving a multi-variable mixed-integer programming (MIP) problem in each of multiple intersection traffic controllers (ITCs) optimizing a cost function and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, and transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.

First claim

We claim:

1 . A traffic control system for jointly controlling one or multiple connected and automated vehicles (CAVs) and one or multiple human-driven vehicles (HDVs) moving across multiple intersections of roads subject to integer constraints for crossing each of the multiple intersections, comprising: at least one processor; and
a memory having instructions stored thereon that, when executed by the at least one processor, cause the traffic control system to: collect digital representation of states of each of the CAVs, each of the HDVs, and each of traffic signs regulating traffic on the roads; solve an optimization problem jointly optimizing traffic flows based on a macroscopic traffic flow model in a centralized traffic controller (CTC) for the multiple intersections using convex optimization subject to convex relaxation of the integer constraints for crossing each of the multiple intersections; solve, individually for each of the multiple intersections, a multi-variable mixed-integer programming (MIP) problem in each of multiple intersection traffic controllers (ITCs) optimizing a cost function, and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, subject to the integer constraints to produce values of control commands changing states of each of the CAVs associated with an intersection of the multiple intersections and values of control commands changing states of each of the traffic signs associated with the intersection, wherein the cost function is optimized subject to a motion model of a CAV associated with the intersection described by a differential equation relating a control command to the CAV with a change of a state of the CAV, and subject to a motion model of an HDV described by a switch function relating a dynamic traffic rule for the HDV with a state of the HDV and a state of a corresponding traffic sign; and transmit the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.

2 . The traffic control system of claim 1 , wherein the switch function for each of the HDVs includes one or multiple motion models for one or multiple switching conditions, the one or multiple motion models including at least one of:
a first motion model for a stopping maneuver in a stopping zone at each of the multiple intersections of roads in a transportation network if a corresponding traffic sign is red for a crossing direction and the HDV is within a first safety distance from the stopping zone; a second motion model for a safe leading vehicle following behavior if a leading vehicle is within a second safety distance in front of the HDV and the leading vehicle is in the same lane of the same road segment as the HDV; and a third motion model for traveling at a desired average speed if there is no leading vehicle within the second safety distance in front of the HDV and the leading vehicle is not in the same lane as the HDV, or the HDV is not within the first safety distance from the stopping zone and the corresponding traffic sign is not red.

3 . The traffic control system of claim 1 , wherein the multi-variable MIP problem in each of the multiple ITCs includes a mapping between multiple collision-free states of the traffic signs and values of the traffic signs for each crossing direction of the multiple intersections in a transportation network that is controlled by a hierarchical traffic control system.

4 . The traffic control system of claim 3 , wherein
the multi-variable MIP problem in each of the multiple ITCs includes multiple mixed-integer equality and inequality constraints to enforce traffic rules for the CAVs and the HDVs driving in a neighborhood of each of the multiple intersections in the transportation network that is controlled by the hierarchical traffic control system, and the traffic rules include constraints for crossing through an intersection of the multiple intersections based on a collision-free state of a corresponding traffic sign, capacity limit constraints for each of the multiple intersections or each of road segments in the transportation network, collision avoidance constraints between pairs of vehicles, lane change constraints for overtaking of vehicles, speed limit constraints, and traffic sign timing constraints.

5 . The traffic control system of claim 1 , wherein the cost function of the multi-variable MIP problem in each of the multiple ITCs includes a maximization of traveled distance for each of the CAVs and HDVs driving in a neighborhood of one or more intersections of the multiple intersections in a transportation network, a minimization of an error between a current lane value and a preferred lane value for each of the CAVs and HDVs, a minimization of a number of lane changes for each of the CAVs and HDVs, a minimization of slack variables for one or multiple constraint violations, and a minimization of a least squares tracking error between predicted traffic flow values and reference CTC traffic flow values in a cost function adaptation method of a hierarchical traffic control system.

6 . The traffic control system of claim 1 , wherein
the macroscopic traffic flow model in the CTC is described as a directed graph, each node of the directed graph corresponds to a road segment of multiple road segments and each edge of the directed graph corresponds to a connection between two road segments of the multiple road segments, and the connection between two road segments indicates a direction to cross through an intersection of the multiple intersections in a transportation network that is controlled by a hierarchical traffic control system.

7 . The traffic control system of claim 6 , wherein
the macroscopic traffic flow model in the CTC is a set of discrete-time differential equations that include one or multiple differential state variables and one or multiple control input variables, and each of the one or multiple differential state variables and one or multiple control input variables is included in optimization variables of a convex optimization problem that is solved in the CTC.

8 . The traffic control system of claim 7 , wherein
the one or multiple differential state variables include vehicle density variables that define a number of vehicles for each pair of a road segment of the multiple road segments and a traffic flow maneuver at each time step in a prediction time window of the CTC, and the one or multiple control input variables include in-flow and out-flow variables that define a number of vehicles entering and exiting, respectively, for each pair of the road segment and the traffic flow maneuver at each time step in the prediction time window of the CTC.

9 . The traffic control system of claim 8 , wherein
a solution of the convex optimization problem is used by the CTC to compute a set of optimal traffic flow probability values subject to the convex relaxation of mixed-integer constraints for vehicles crossing each of the multiple intersections, switching behavior of the traffic signs, or collision-free states for the traffic signs of each of the multiple intersections, and the optimal traffic flow probability values of the CTC are used by a cost function adaptation method in each of the multiple ITCs to minimize a tracking error between predicted traffic flow values and CTC traffic flow values over a prediction time horizon of the ITCs for each crossing direction of the multiple intersections.

10 . The traffic control system of claim 1 , wherein the CTC solves a convex optimization problem with a cost function that includes at least one of a maximization of a sum of traffic flow variables or a minimizat

Assignees

• Mitsubishi Electric Res Laboratories Inc

Inventors

• Quirynen Rien
• Suriyarachchi Nilesh
• Di Cairano Stefano

Classifications

• G08G1/0133

  for classifying traffic situation · CPC title

• G08G1/0129

  for creating historical data or processing based on historical data · CPC title

• G08G1/166

  for active traffic, e.g. moving vehicles, pedestrians, bikes · CPC title

• G08G1/0116

  from roadside infrastructure, e.g. beacons · CPC title

• G08G1/091 Primary

  Traffic information broadcasting (broadcasting communication H04H) · CPC title