SYSTEM AND METHOD FOR COOPERATIVE CONTROL OF CONNECTED AUTONOMOUS VEHICLES (CAV) AND MANUALLY OPERATED CONNECTED VEHICLES (MCV)

Abstract

The present disclosure provides a system and method for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually connected vehicles (MCVs) moving to form traffic on the same or intersecting roads. The method includes collecting states for each of the CAVs, each of the MCVs, and each of the traffic signs regulating the traffic, and solving a multi-variable mixed-integer problem (MIP) that optimizes a cost function for values ​​of control commands that change the state of each CAV and values ​​of control commands that change the state of each of the traffic signs. The cost function is optimized according to a motion model of each CAV, constraints that model general traffic rules, timing constraints, and a motion model of each MCV. The method further includes transmitting the optimized values ​​of the control commands to the corresponding CAVs and the corresponding traffic signs.

Description

[Technical field]

[0001] The present disclosure relates generally to traffic control systems, and more specifically to systems and methods for cooperative control of connected autonomous vehicles (CAVs) and manually connected vehicles (MCVs). [Background technology]

[0002] Automation of transport systems, even partial automation, leads to fewer road accidents and more efficient utilization of the transport network. Connected autonomous vehicles (CAVs) are therefore likely to contribute to improving safety and traffic flow, and, as a consequence, reducing congestion, travel times, emissions and energy consumption. Although road conditions are highly dynamic, i.e. vehicular participants and their behavior change rapidly and significantly, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications enable advanced and efficient planning and decision making by providing access to real-time information about all vehicles in the transport network. Summary of the Invention [Problem to be solved by the invention]

[0003] Some approaches use a multi-layer guidance and control architecture implemented on-board for motion planning and control of the CAV. For example, at the top level, a navigation system determines a route plan through the traffic network from the CAV's current location to a desired location. Furthermore, a decision-making system determines a target driving behavior at any point in time based on the route plan, current environmental conditions, and the behavior of other traffic participants. Furthermore, based on the target driving behavior, including lane following, lane changing, or stopping, a motion planning algorithm calculates a dynamically feasible and safe trajectory that can be tracked in real time by a controller such as a model predictive control (MPC).

[0004] In addition, by coordinating CAVs, it is possible to achieve optimal behavior for the transportation network. As an example, we describe a first come, first serve (FCFS) policy for autonomous traffic management at intersections. Another example is a coordination strategy for intersections using nonlinear optimization or mixed-integer linear programming (MILP).

[0005] However, mixed traffic situations may be encountered where CAVs coexist with manually operated connected vehicles (MCVs) controlled by human drivers. In such situations, it is difficult to achieve optimal behavior for the traffic network since MCVs are not motion controlled as CAVs. In some approaches, CAVs are used to control the motion of MCVs by occupying lanes in a specific way. However, this can be dangerous and can have adverse effects on CAVs, their behavior, and their passengers. In normal situations, the motion / flow of MCVs is regulated by traffic rules. Some traffic rules are dynamic, e.g., traffic lights, lanes with speed limits, etc., and can be changed in real time and displayed on appropriate panels. The motion of MCVs can be controlled by setting dynamic traffic signs. However, dynamic traffic signs are set based on the prevailing road conditions, mostly by human operators, without considering the goals of each vehicle.

[0006] Thus, there is a need for a system and method for optimally jointly controlling the motion of CAVs and MCVs to optimally achieve the goals of the CAVs and MCVs in a transportation network. [Means for solving the problem]

[0007] An objective of some embodiments is to provide a system and method for jointly controlling connected autonomous vehicles (CAVs) and manually operated connected vehicles (MCVs). As used herein, CAVs refer to autonomous vehicles and MCVs refer to manually operated vehicles. According to one embodiment, CAVs can be continuously controlled at any time and space to achieve optimization of the traffic network, such as minimizing the average or worst case travel time, overall idling time, etc. In contrast, MCVs are uncontrolled vehicles. In other words, it is impossible or at least impractical to control the motion of MCVs at each time and space. In various traffic situations where only MCVs are traveling, the motion of MCVs is controlled by traffic signs. For example, the flow of MCVs is influenced by the timing and sequencing of traffic lights, so that the overall operation of the traffic network is positively influenced. Thus, the motion of MCVs is indirectly controlled by controlling traffic signs. However, because MCVs are driven by human drivers and indirectly controlled based on traffic signs, MCVs can only be controlled at specific locations, which limits control over the traffic network.

[0008] Some embodiments are based on the recognition that it may be beneficial to jointly control CAVs and MCVs to optimize overall benefits for the CAVs and MCVs, such as minimizing average or worst case travel time, overall idling time, etc. Jointly controlling CAVs and MCVs is problematic, in part, for the following reasons: For example, to control CAVs directly at each time and in each space, each CAV is individually controlled based on its motion model and a model of the environment. In contrast, to control MCVs indirectly by changing the state of traffic signs, various techniques combine multiple MCVs together and control them as an aggregate of traffic, using various statistical characteristics such as traffic density. While such statistical aggregation is effective for indirectly controlling MCVs to optimize the traffic network, it hinders jointly optimizing CAVs and MCVs to achieve a set of individual goals for these specific individual vehicles and a common goal for that specific group of individual vehicles. This is because individual statistical behavior has different principles that prevent joint optimization, specifically, statistical optimization optimizes the average behavior to achieve the desired characteristics of the road, e.g., average speed, average density of vehicles, etc., while individual optimization optimizes the behavior of each individual unit in achieving its unit-specific goals. In other words, statistical optimization of traffic depends only on the number, position and speed of vehicles on the road, but does not consider the goals of each vehicle. Individual optimization does consider such goals, and optimizing a group of vehicles individually optimizes the individual goals for all vehicles in the group. Individual optimization can also optimize common goals for a particular group of vehicles.

[0009] According to one embodiment, the motion of the CAV can be described by a motion model described by one or more differential or difference equations that relate control commands to the state of the CAV while satisfying general traffic rules. The general traffic rules include, for example, rules for crossing intersections, avoiding collisions with adjacent vehicles, occupying open lanes, and satisfying lane speed limits. Thus, based on the state of the CAV and desired control objectives, control commands can be optimized that achieve the control objectives constrained by the general traffic rules. The state of the CAV includes at least one of the position, speed, acceleration, and lane of the CAV.

[0010] In contrast, the motion of the MVC depends on the state of the traffic signs and the response of the human driver of the MCV to such states. Thus, joint optimization of the CAV and the MVC is a multi-objective optimization over decision variables that include control commands to the CAV that change the state of the CAV and control commands that change the state of the traffic signs. However, such joint optimization is difficult.

[0011] Some embodiments are based on the recognition that in order to jointly control the CAV and the MCV, it is necessary to describe individual motion models of the CAV and the MCV. Specifically, while the motion model of the CAV can be described as a function of the control command being optimized, it is necessary to describe the motion model of the MCV as a function of the state of the traffic sign. However, it is difficult to determine such a function because different drivers may react differently to different states of a traffic sign, such as a traffic light. For example, depending on the situation, a human driver may accelerate, decelerate or stop his vehicle when the state of the traffic light is changing from a green light to a yellow light.

[0012] Some embodiments are based on the recognition that the motion model of the MCV can be described by multiple functions, each of which is effective at a different time. Different functions are formulated for different dynamic traffic rules. Examples of dynamic traffic rules include traffic signs, e.g., traffic lights that can receive control commands to change the timing to allow more or fewer vehicles to pass in a particular direction, variable speed limits that can receive control commands to change the maximum speed of vehicles in a particular lane, dynamically enabled lanes that can receive control commands to open or block access to a lane, etc.

[0013] Each function represents a motion model for a corresponding dynamic traffic rule. In other words, each function describes the behavior of the MCV in response to the corresponding dynamic traffic rule. The motion model represented by these functions is called a rule-regulated motion model. In addition, a rule-free function is formulated that represents a rule-free motion model. The rule-free motion model is not affected by the dynamic traffic rules. The selection of the function to be active for the MCV depends on the state of the traffic sign, the state of the MCV, and the state of other vehicles. The state of the MCV includes at least one of the position, speed, acceleration, and lane of the MCV.

[0014] Thus, the functions themselves may not be dependent on the state of the traffic sign, however the function to be active in a particular control step is selected depending on the state of the traffic sign. Collectively, these functions are referred to herein as switching (discrete) functions that select a function representing a motion model for the MCV based on one or a combination of the state of the traffic sign, the state of the MCV, and the state of other vehicles.

[0015] Although the switching functions describe the individual motion of the MCVs, due to their discontinuous nature, the joint multivariable optimization of the control commands for the CAVs and the control commands for the traffic signs is represented by a mixed integer optimization problem that must be solved to jointly optimize the CAVs and MCVs to achieve their individual goals and the common goal of their group.

[0016] Accordingly, one embodiment discloses a traffic control system for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually operated connected vehicles (MCVs) moving to form traffic on the same or intersecting roads, the traffic control system including at least one processor and a memory having stored thereon instructions that, when executed by the at least one processor, cause the traffic control system to: collect digital representations of states for each of the CAVs, each of the MCVs, and each of the traffic signs regulating the traffic; solve a multi-variable mixed-integer problem that optimizes, over a finite future time horizon, a cost function for values ​​of control commands that change a state of each of the CAVs and values ​​of control commands that change a state of each of the traffic signs; The instructions cause a motion model of each of the CAVs to solve a motion-independent problem (MIP), in which cost functions of individual objectives of each CAV and each MCV and a common objective of each group of the CAVs and the MCVs are optimized according to a motion model of each of the CAVs described by a differential equation relating control commands for a CAV to changes in the state of the CAV, constraints modeling general traffic rules, timing constraints, and a motion model of each of the MCVs described by switching functions relating dynamic traffic rules for an MCV to the state of the MCV and to the state of each of the traffic signs, and the instructions further cause the optimized values ​​of control commands to be transmitted to the corresponding CAV and to the corresponding traffic sign.

[0017] Accordingly, another embodiment discloses a method for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually operated connected vehicles (MCVs) moving in traffic on the same or intersecting roads. The method includes collecting digital representations of states for each of the CAVs, each of the MCVs, and each of the traffic signs regulating the traffic, and solving a multivariate mixed integer problem (MIP) that optimizes a cost function for values ​​of control commands that change the state of each of the CAVs and values ​​of control commands that change the state of each of the traffic signs over a finite future prediction time interval, where the cost functions for individual goals of each CAV and each MCV and a common goal of individual groups of the CAVs and the MCVs are optimized according to a motion model of each of the CAVs described by a differential equation that relates control commands for a CAV to changes in the state of the CAV, constraints that model general traffic rules, timing constraints, and a motion model of each of the MCVs described by switching functions that relate dynamic traffic rules for an MCV to the state of the MCV and to the state of each of the traffic signs, and the method further includes transmitting the optimized values ​​of control commands to a corresponding CAV and corresponding traffic sign.

[0018] Accordingly, yet another embodiment discloses a non-transitory computer-readable storage medium having embodied thereon a program executable by a processor for performing a method for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually operated connected vehicles (MCVs) moving in a traffic-forming manner on the same or intersecting roads. The method includes collecting digital representations of states for each of the CAVs, each of the MCVs, and each of the traffic signs regulating the traffic, and solving a multivariate mixed integer problem (MIP) that optimizes a cost function for values ​​of control commands that change the state of each of the CAVs and values ​​of control commands that change the state of each of the traffic signs over a finite future prediction time interval, where the cost functions for individual goals of each CAV and each MCV and a common goal of individual groups of the CAVs and the MCVs are optimized according to a motion model of each of the CAVs described by a differential equation that relates control commands for a CAV to changes in the state of the CAV, constraints that model general traffic rules, timing constraints, and a motion model of each of the MCVs described by switching functions that relate dynamic traffic rules for an MCV to the state of the MCV and to the state of each of the traffic signs, and the method further includes transmitting the optimized values ​​of the control commands to a corresponding CAV and corresponding traffic sign.

[0019] Embodiments of the present disclosure are further described hereinafter with reference to the accompanying drawings, in which the drawings are not necessarily to scale, with emphasis generally being placed upon illustrating the principles of embodiments of the present disclosure. [Brief description of the drawings]

[0020] [Figure 1] FIG. 2 illustrates an example of a traffic situation in a traffic network including interconnected conflict zones, according to some embodiments of the present disclosure. [Diagram 2] FIG. 1 is a schematic diagram illustrating the principles used for jointly controlling a connected autonomous vehicle and a manually operated connected vehicle, according to one embodiment of the disclosure. [Diagram 3] FIG. 1 illustrates a schematic diagram of a motion model for a manually operated connected vehicle according to one embodiment of the disclosure. [Figure 4] FIG. 1 illustrates a schematic for formulating a multivariate mixed integer problem (MIP) according to one embodiment of the present disclosure. [Figure 5A] FIG. 2 illustrates an overview of a branch and bound method, according to one embodiment of the present disclosure. [Figure 5B] FIG. 2 is a block diagram illustrating a branch-and-bound mixed-integer optimization algorithm for searching for an integer-feasible optimal solution based on a nested tree of search spaces and corresponding lower / upper bounds, in accordance with some embodiments of the present disclosure. [Figure 6] FIG. 1 is a block diagram illustrating a traffic control system for jointly controlling connected autonomous vehicles and manually operated connected vehicles, according to one embodiment of the disclosure. [Figure 7A] FIG. 1 is a schematic diagram illustrating a vehicle including a multi-layer guidance and control architecture employing the principles of some embodiments. [Figure 7B] FIG. 2 is a schematic diagram illustrating the interaction between the multi-layer guidance and control architecture and other controllers of the vehicle according to one embodiment of the present disclosure. [Figure 8A] FIG. 1 illustrates an example application of a traffic control system in a traffic network including multiple interconnected intersections, according to one embodiment of the present disclosure. [Figure 8B] FIG. 2 illustrates an exemplary traffic scene in a traffic network of multiple interconnected intersections and junctions, according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. In other instances, the devices and methods are shown only in block diagram form in order to avoid obscuring the present disclosure.

[0022] As used herein and in the claims, the words "for example," "for example," and "such as," as well as "comprising," "having," "including," and other verb forms thereof, when used in conjunction with a list of one or more components or other items, should be construed as open-ended. This means that the list should not be considered to exclude additional components or items. The term "based on" means based at least in part on. Furthermore, it should be understood that the terms and terminology used herein are for purposes of description and should not be considered as limiting. Any headings used herein are for convenience only and should not be construed as having any legal or limiting effect.

[0023] FIG. 1 illustrates an example of a traffic situation 100 in a traffic network including interconnected conflict zones, according to some embodiments of the present disclosure. The interconnected conflict zones include physically interconnected conflict zones and communicatively interconnected conflict zones. An example of physically interconnected conflict zones includes an intersection or junction where vehicles can move from one conflict zone to another. An example of communicatively interconnected conflict zones includes an intersection or junction that shares traffic information from one conflict zone to another conflict zone via a communication channel.

[0024] The traffic network includes an intersection 101 and another intersection 103 that are physically interconnected with each other via road section 105, and additional road sections 107, 109, 111, 113, 115, and 117. Examples of interconnected conflict zones include intersections 101-103 and / or junctions that may connect multiple lanes and / or road sections. Examples of conflict-free zones include road sections 105-117 that include one or more lanes that allow traffic in either one direction or multiple directions.

[0025] The traffic situation 100 includes a number of vehicles 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139 and / or other traffic participants within a local area of ​​intersections 101-103 and road sections 105-117. Each of the vehicles in the traffic network, for example, the vehicles 119-139, may be an autonomous vehicle, a semi-autonomous vehicle, or a manually operated vehicle. The autonomous vehicles and / or semi-autonomous vehicles are referred to as connected autonomous vehicles (CAVs), or simply controlled vehicles, and the manually operated vehicles or other traffic participants are referred to as manually operated connected vehicles (MCVs). Some examples of the vehicles 119-139 include two-wheeled vehicles such as motorbikes, four-wheeled vehicles such as cars, or vehicles with four or more wheels such as trucks.

[0026] According to some embodiments, the vehicles 119-139 follow general traffic rules. The general traffic rules include, for example, rules regarding crossing intersections, avoiding collisions with adjacent vehicles, occupying open lanes, and meeting lane speed limits. In addition, the general traffic rules include dynamic traffic rules that dynamically change upon receiving corresponding control commands. Examples of dynamic traffic rules include traffic signs, e.g., traffic lights that can receive control commands to change the timing to allow more or fewer vehicles to pass in a particular direction, variable speed limits that can receive control commands to change the maximum speed of vehicles in a particular lane, dynamically enabled lanes that can receive control commands to open or block access to a lane, etc. The dynamic traffic rules may be displayed on a digital display on the road segment to inform the vehicles 119-139 of the status of the dynamic traffic rules. For example, traffic light display 149 may be configured to display the status of the traffic lights for intersection 101, lane speed display may be configured to display the lane speed limit, and lane access status display 151 may be configured to display a status indicating whether access to lane 153 is available.

[0027] In some embodiments, traffic situation 100 corresponds to a public urban area where road segments 105-117 form multiple (multiple) intersections, such as intersections 101 and 103. In the public urban area, traffic conditions at intersections 101 and 103 determine traffic flow because traffic congestion typically starts at a conflict zone, such as intersection 103, and spreads to non-conflicting road segments, e.g., road segments 105-117. Traffic conditions at intersections 101 and 103 are interdependent such that a change at one conflict zone (e.g., intersection 103) will spread further to other interconnected conflict zones, such as intersection 101 (also referred to as adjacent intersections 101 of intersection 103).

[0028] In other embodiments, the traffic situation 100 corresponds to a dedicated traffic network, including, for example, one or more parking areas for a valet parking system and interconnected road segments. Other examples of similar traffic situations including traffic networks with multiple interconnected conflict zones and road segments include large factories, networks of warehouses, smart distribution centers and / or shipping yards. Examples of types of CAVs include privately owned vehicles (e.g., in the case of a valet parking system), trucks (e.g., in the case of a workshop management system), or shuttles for passenger pickup and drop-off.

[0029] In some implementations, a traffic network includes one or more road-side units (RSUs) for infrastructure-based real-time sensing of conditions on vehicles 119-139 and other traffic participants in a local area around each of the RSUs. For example, the traffic network includes one RSU 141 and another RSU 143, a core network 145, and a cloud network 147, and establishes an Internet of Vehicles (IoV) environment including V2V and V2I communications, also known as vehicle-to-everything (V2X) communications. Some embodiments are based on the recognition that to establish communications between various vehicles (e.g., vehicles 119-139) in the traffic network, communications between the cloud network 147 and the vehicle 119 on the road segment 105 need to propagate through the RSU 141 or RSU 143 and the core network 145 such that multi-hop communications are established.

[0030] The embodiments of the present disclosure are based on the recognition that an on-board controller of a vehicle, such as vehicle 139, cannot obtain information about nearby vehicles (such as vehicle 135), pedestrians, and environmental conditions that are outside of its visual range. For example, vehicle 139 traveling on road section 115 is crossing intersection 101 after vehicle 37 (larger than vehicle 135) has crossed intersection 101, and vehicle 135 (a smaller vehicle) is also entering intersection 101. In such a situation, the visibility of vehicle 135 is blocked by vehicle 137 as shown in FIG. 1. If the communication link between vehicle 135 and vehicle 139 is affected or if vehicle 135 and vehicle 139 use different communication protocols, vehicle 139 cannot see vehicle 135 because vehicle 137 is in the way. As a result, a collision between vehicle 135 and vehicle 139 may occur. Additionally, some embodiments of the present disclosure are based on the recognition that multi-hop communication between the cloud network 147 and the vehicle 119 may result in long communication delays that are unacceptable in the real-time context of cloud-based vehicle control.

[0031] To that end, in some embodiments, various communication technologies are utilized to support vehicular communications, such as the IEEE Dedicated Short-Range Communications / Wireless Access in Vehicular Environments (DSRC / WAVE) family of standards for vehicular networks, 3GPP Cellular-Vehicle-to-Anything (C-V2X), and others. However, due to high costs, it is impractical for vehicles 119-139 to support multiple short-range communication technologies, leading to compatibility issues between vehicles 119-139 communicating with each other. Thus, vehicles equipped with IEEE DSRC / WAVE cannot communicate with other vehicles equipped with 3GPP C-V2X, and vice versa. As a result, the accuracy of real-time control decisions made by on-board multi-layer guidance and control architectures in individual vehicles may be affected, since these real-time decisions may be based on incomplete information of the traffic situation 100.

[0032] According to some embodiments, edge infrastructure devices (e.g., RSUs 141 and 143) have advantages in terms of controlling traffic over using only cloud network 147 and / or only on-board control devices (including multi-layer guidance and control architectures). For example, edge infrastructure devices can be installed at intersections 101 and 103 and can directly communicate with vehicles approaching intersections 101 and 103. In addition, edge infrastructure devices can be equipped with multiple communication technologies to be able to communicate with all vehicles 119-139. Edge infrastructure devices are stationary, which allows edge infrastructure devices to reliably communicate with vehicles 119-139 and collect high quality environmental data.

[0033] In addition, edge infrastructure devices may be configured to continuously monitor traffic and environmental conditions. In some embodiments of the present disclosure, the edge infrastructure devices use additional sensors, such as distance range finders, radar, lidar, and / or cameras, and sensor fusion techniques to accurately detect the status of vehicles 119-139 and other traffic participants, such as bicycles and pedestrians.

[0034] According to one embodiment, CAVs can be continuously controlled from edge infrastructure devices at any time and space to achieve optimization of the traffic network, such as minimizing average or worst-case travel time, overall idling time, etc. In contrast, MCVs are uncontrolled vehicles. In other words, it is impossible or at least impractical to control the motion of MCVs at each time and space. In various traffic situations where only MCVs are running, the motion of MCVs is controlled by traffic signs. For example, the flow of MCVs is influenced by the timing and sequencing of traffic lights, so that the overall operation of the traffic network is positively influenced. Thus, the motion of MCVs is indirectly controlled by controlling traffic signs. However, since MCVs are driven by human drivers and indirectly controlled based on traffic signs, MCVs can only be controlled at certain locations, which limits the control over the traffic network.

[0035] Some embodiments are based on the recognition that it may be beneficial to jointly control CAVs and MCVs to optimize overall benefits for the CAVs and MCVs, such as minimizing average or worst case travel time, overall idling time, etc. To that end, it is an objective of some embodiments to provide a traffic control system for jointly controlling CAVs and MCVs. In some embodiments, the traffic control system is implemented using one or more mobile edge computers (MECs), which may be embedded as part of one or more RSUs or may be separate devices connected to the RSUs 122-124, the cloud network 118, and / or the core network 120.

[0036] FIG. 2 shows an overview of the principles used to jointly control CAVs and MCVs, according to one embodiment of the present disclosure. Joint control of CAVs and MCVs is problematic, in part, for the following reasons: For example, to directly control a CAV at each time and in each space, each CAV is individually controlled based on its motion model and a model of the environment. In contrast, to indirectly control MCVs by changing the state of traffic signs, various techniques use various statistical characteristics, such as traffic density, to control multiple MCVs together as a traffic aggregation. While such statistical aggregation is effective for indirectly controlling MCVs to optimize the traffic network, it hinders the joint optimization of CAVs and MCVs to achieve individual goals for these specific individual vehicles and common goals for that specific group of individual vehicles. This is because individual statistical behavior has various principles that hinder joint optimization, specifically because statistical optimization optimizes the average behavior to achieve the desired characteristics of the road, e.g., average speed, average density of vehicles, etc., while individual optimization optimizes the behavior of each individual unit in achieving its unit-specific goals. In other words, statistical optimization of traffic depends only on the number, positions and speeds of vehicles on the road, but does not consider the goals of each vehicle. Individual optimization does consider such goals, and optimizing a group of vehicles individually optimizes the individual goals for all vehicles in the group.

[0037] According to one embodiment, the motion of the CAV 201 can be described by a motion model 203 described by one or more differential equations that relate control commands to the state of the CAV 201 while satisfying the general traffic rules. Thus, based on the state of the CAV 201 and the desired control objectives, control commands can be optimized to achieve the control objectives constrained by the general traffic rules. The state of the CAV 201 includes at least one of the position, speed, acceleration, and lane of the CAV 201.

[0038] In contrast, the motion of the MVC 205 depends on the state of the traffic signs and the response of the human driver of the MCV 205 to such conditions. Thus, joint optimization of the CAV 201 and the MVC 205 is a multi-objective optimization over decision variables that include control commands to the CAV 201 that change the state of the CAV 201 and control commands that change the state of the traffic signs. However, such joint optimization is difficult.

[0039] Some embodiments are based on the recognition that in order to jointly control the CAV 201 and the MCV 205, it is necessary to describe individual motion models of the CAV 201 and the MCV 205. Specifically, the motion model 203 of the CAV 201 can be described as a function of the control command being optimized, but the motion model 207 of the MCV 205 needs to be described as a function of the state of the traffic sign. However, determining such a function is difficult because different drivers may react differently to different states of a traffic sign, such as a traffic light. For example, depending on the situation, a human driver may accelerate, decelerate or stop his vehicle when the traffic light state is changing from a green light to a red light.

[0040] Some embodiments are based on the realization that the motion model 207 of the MCV 205 can be described by multiple functions, each of which or different combinations of which are active at different times, as illustrated in FIG.

[0041] FIG. 3 shows a schematic diagram of a motion model 207 of the MCV 205 according to one embodiment of the present disclosure. Different functions are formulated for different dynamic traffic rules. For example, a function 303 for dynamic traffic rule 1, a function 305 for dynamic traffic rule 2, and a function 307 for dynamic traffic rule n are formulated. Each of the functions 303-307 represents a motion model for a corresponding dynamic traffic rule. In other words, each function describes the behavior of the MCV 205 according to the corresponding dynamic traffic rule. The motion model represented by the functions 303-307 is referred to as a rule-regulated motion model. In addition, a rule-free function 301 is formulated to represent a rule-free motion model. The rule-free motion model is not affected by the dynamic traffic rules.

[0042] The selection of the function to be active for the MCV 205 depends on the traffic sign state 311, the MCV 205 state 309, and the other vehicle state 313. The MCV 205 state 309 includes at least one of the MCV 205's position, speed, acceleration, and lane. Thus, the functions 301-307 themselves may not depend on the traffic sign state 311, but the selection of the function to be active at a particular control step depends on the traffic sign state 311. Collectively, the functions 301-307 are referred to herein as switching (discrete) functions that select a function representing a motion model for the MCV 205 based on one or a combination of the traffic sign state 311, the MCV 205 state 309, and the other vehicle state 313. The functions 301-307 individually describe the dynamic traffic rules that the MCV should follow.

[0043] According to one embodiment, the selected function may be used as a motion model of the MCV 205 for joint optimization of the CAV 201 and the MCV 205. In particular, the traffic control system solves a multivariate mixed integer problem (MIP) that optimizes a cost function for values ​​of control commands that change the state of each of the CAVs and values ​​of control commands that change the state of each of the traffic signs. The cost function is optimized according to the motion models of each of the CAVs and each of the MCVs.

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[0052] The collision avoidance rule (9) regarding the vehicle position is independent of other variables controlled by the traffic control system other than the position of the vehicle itself and is therefore not a dynamic traffic rule, since the definition of compliance or violation of a traffic rule can always be the same when the vehicle position is stationary.

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[0058] In some implementations, the normalization in both (13a) and (13b) may be omitted.

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[0063] Furthermore, according to one embodiment, a multivariate mixed integer problem (MIP) for optimizing the cost function (12) is formulated as described below in FIG.

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[0073] According to one embodiment, the MIP problem (14) can be solved by using a branch and bound method.

[0074] 5A illustrates an overview of a branch-and-bound method according to one embodiment of the present disclosure. The branch-and-bound method involves sequentially creating partitions of the MIP problem (14) and then solving the partitions, each partition corresponding to a particular region of the discrete optimization variable search space. In some embodiments, the branch-and-bound method involves selecting a partition or node and selecting discrete optimization variables to branch the selected partition into smaller partitions or search spaces, resulting in a nested tree of partitions or search spaces.

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[0077] FIG. 5B shows a block diagram of a branch-and-bound mixed integer optimization algorithm for searching an integer-feasible optimal solution based on a nested tree of search regions and corresponding lower / upper bounds, according to some embodiments of the present disclosure. In block 515, branch search tree information is initialized for MIP in the current time step based on MIP data 517 including matrices and vectors. The initialization can additionally use branch search tree information and MIP solution information from the previous time step 519 to provide a warm-start style initialization for the current time step. The objective of the branch-and-bound mixed integer optimization algorithm is to determine lower and upper bounds for the target value of the mixed integer solution. In block 521, it is checked whether the difference between the lower bound and the upper bound is less than a tolerance value. If the difference is less than a tolerance value, it is deemed that the mixed integer optimal solution is determined in block 523.

[0078] If the difference is greater than or equal to the tolerance, then the next node in the nested tree corresponding to the next region or partition of the integer variable search space is selected with feasible variable fixed values ​​based on the pre-solution branching technique in block 525. After node selection, the corresponding integer relaxation problem is solved with feasible variable fixed values ​​based on the post-solution branching technique in block 527.

[0079] If the integer relaxed problem has a feasible solution, the resulting relaxed solution provides a lower bound on the objective value for that particular region or partition of the integer variable search space. If it is determined in block 529 that the objective value is greater than the currently known upper bound on the mixed integer solution's objective value, then in block 531 the selected node is pruned or removed from the branch tree. However, if it is determined that the objective value is lower than the currently known upper bound, then in block 533 it is checked whether the relaxed solution is an integer feasible. If the relaxed solution is an integer feasible, then in block 535 the currently known upper bound and the corresponding mixed integer solution estimate are updated.

[0080] If the integer relaxed problem has a feasible solution and the objective value is lower than the currently known upper bound but the relaxed solution is still not an integer feasible solution, then the lower bound is updated to be the minimum of the objective values ​​for the remaining leaf nodes in the branching tree in block 537 and the selected node is pruned from the branching tree in block 539. Additionally, starting from the current node, in block 541, discrete variables having fractional values ​​are selected for branching according to a particular branching strategy, which causes a resulting sub-problem corresponding to a region or partition of the discrete search space to be created and appended as a child of that node in the branching tree in block 543.

[0081] Some embodiments are based on branching one of the binary optimization variables that have fractional values ​​in the integer relaxation solution. For example, if a binary optimization variable d ∈ {0,1} has fractional values ​​as part of the integer relaxation optimal solution, two partitions of the mixed integer problem are created by adding an equality constraint d=0 to one sub-problem and an equality constraint d=1 to the other sub-problem, respectively. Some embodiments are based on a reliability branching strategy for variable selection, which aims to predict future branching behavior based on information from previous branching decisions.

[0082] Some embodiments are based on a branch-and-bound method using a depth-first node selection strategy that can be implemented using a last-in-first-out (LIFO) buffer. The next node to be solved is selected as one of the children of the current node, and this process is repeated until the node is pruned, i.e., the node becomes infeasible or optimal, or the currently known upper bound dominates, followed by a backtracking procedure. Alternatively, some embodiments are based on a branch-and-bound method using a best-first strategy that selects the node that currently has the lowest local lower bound. Some embodiments employ a combination of depth-first and best-first node selection approaches, where a depth-first node selection approach is used until an integer feasible solution is determined, and then a best-first node selection approach is used during subsequent iterations of the branch-and-bound based optimization algorithm. The latter implementation is motivated by aiming to determine an integer feasible solution early at the start of the branch-and-bound procedure (depth-first) in order to allow for early pruning and then a greedy search for a better feasible solution (best-first).

[0083] The branch-and-bound mixed integer optimization algorithm continues to iterate until one or more termination conditions are met, for example, until a maximum execution time is reached and all nodes in the branch tree are pruned such that no new nodes can be selected to solve the convex relaxation or branch, and / or the difference in optimality between the global lower bound and the global upper bound of the objective value is smaller than a tolerance value.

[0084] In one embodiment, the branch-and-bound calculations are reduced by fixing some of the variables to specific values ​​based on the state of the CAV and MCV before performing the branch-and-bound. For example, if the CAV is far away from the intersection, a distance greater than the distance that can be traversed at the maximum speed according to the speed limit in the finite time of the optimization range, the membership variables of the CAV at the intersection may be set according to the CAV not at the intersection. Similarly, if a first CAV is far behind a second CAV, the variables indicating that the first CAV has overtaken the second CAV may be set as if this overtaking did not occur. Similarly, if the MCV is far away from the intersection, all models related to the operation of the MCV at the intersection may be ignored and the corresponding variables may be set such that those models stall.

[0085] 6 illustrates a block diagram of a traffic control system according to one embodiment of the present disclosure. The traffic control system 600 runs in the cloud or on one or more mobile edge computers (MECs). The traffic control system 600 may additionally require one or more edge infrastructure devices (e.g., RSUs for infrastructure-based sensing) consisting of or operably connected to a set of sensors to collect digital representations of conditions for each of the CAVs, each of the MCVs, and each of the traffic signs regulating traffic.

[0086] The traffic control system 600 includes several interfaces that connect the traffic control system 600 to other systems and devices. For example, the traffic control system 600 includes a network interface controller (NIC) 601. The NIC 601 is adapted to connect the traffic control system 600 to a network 605 via a bus 603, which in turn connects the traffic control system 600 to one or more devices 606. Examples of such devices include, but are not limited to, vehicles, traffic lights, traffic sensors, roadside units (RSUs), mobile edge computers (MECs), and passenger mobile devices.

[0087] The traffic control system 600 receives real-time traffic data 615 via the network 605 using a receiver interface 617a connected to a receiver 619. The traffic data 615 includes digital representations of the status for each of the CAVs, each of the MCVs, and each of the traffic signs. Additionally or alternatively, the traffic control system 600 may include a control interface 617b configured to send control commands to one or more devices 606 to change their respective states, such as acceleration, speed, lighting status, etc. The control interface 617b may send the control commands and / or any other communication means using a transmitter 611.

[0088] In some embodiments, a human machine interface (HMI) 621 connects the traffic control system 600 to a keyboard 623 and a pointing device 625. The pointing device 625 may include a mouse, a trackball, a touchpad, a joystick, a pointing stick, a stylus, or a touch screen, among others. The traffic control system 600 may also be linked via a bus 603 to a display interface adapted to connect the traffic control system 600 to a display device such as a computer monitor, a camera, a television, a projector, or a mobile device, among others. The traffic control system 600 may also be connected to an application interface adapted to connect the traffic control system 600 to one or more pieces of equipment to perform various power distribution tasks.

[0089] The traffic control system 600 includes a processor 613 and a memory 626 that stores instructions executable by the processor 613. The processor 613 may be a single-core processor, a multi-core processor, a computing cluster, a network of multiple connected processors, or any number of other configurations. The memory 626 may include random access memory (RAM), read only memory (ROM), flash memory, or any other suitable memory system. The processor 613 may be connected to one or more input / output devices via a bus 603. The stored instructions implement a method for collaboratively controlling CAVs and MCVs. In some embodiments, the traffic control system 600 includes a storage device that stores a map configuration 631. For example, the map configuration 631 may include location data (e.g., GPS data) for conflict-free road segments, conflict zones, and truck lanes within each road segment of the traffic network.

[0090] Additionally, the storage device 629 is configured to store MIP constraints and objectives 633 of the (MIP) problem (14) that is solved every time step. For example, the MIP constraints and objectives 633 may be configured to enforce physical limitations, vehicle speed limits and / or safety constraints, timing constraints of timers of motion, and to minimize a weighted combination of distance from target, difference from speed criteria, and energy consumption for each vehicle or group of vehicles in the traffic network. In some embodiments, the MIP constraints and objectives 633 lead to the solution of a MILP / MIQP problem.

[0091] The processor 613 is configured to solve a multivariate mixed integer problem (MIP) that optimizes a cost function for values ​​of control commands that change the state of each of the CAVs and values ​​of control commands that change the state of each of the traffic over a finite future prediction time interval. The cost functions for the individual objectives of each CAV and each MCV and the common objective of each group of CAVs and MCVs are optimized according to a motion model of each of the CAVs described by a differential equation that relates control commands for the CAVs to changes in the state of the CAVs, according to constraints that model general traffic rules, according to timing constraints, and according to a motion model of each of the MCVs described by a switching function that relates dynamic traffic rules for the MCVs to the state of the MCVs and to the state of each of the traffic signs. The optimized values ​​of the control commands are then transmitted to the corresponding CAVs and the corresponding traffic signs using the transmitter interface 609.

[0092] In each of the CAVs, the optimized values ​​of the received control commands can be used by a multi-layer guidance and control architecture to control the motion of the CAVs to improve the overall safety, time and energy efficiency of the traffic flow of vehicles operating in the transportation network.

[0093] 7A shows a schematic diagram of a vehicle 701 including a multi-layer guidance and control architecture 703 according to some embodiments of the present disclosure. The multi-layer guidance and control architecture 703 controls the vehicle 701 based on control commands received from the traffic control system 600. As used herein, the vehicle 701 is one of the CAVs in the transportation network. The vehicle 701 includes a steering system 705 and an engine or motor 707, which may be controlled directly by the multi-layer guidance and control architecture 703 or by other components of the vehicle 701.

[0094] The vehicle 701 may also include one or more on-board sensors 709 for sensing the surrounding environment. Examples of the sensors 709 include a distance range finder, radar, lidar, camera, etc. The vehicle 701 may also include one or more on-board sensors 711 for sensing its current amount of motion and internal conditions. Examples of the sensors 711 include a global positioning system (GPS), an accelerometer, an inertial measurement unit, a gyroscope, an axial rotation sensor, a torque sensor, a deflection sensor, a pressure sensor, a flow sensor, etc. The on-board sensors provide information to the multi-layer guidance and control architecture 703. The vehicle may be equipped with a transceiver 713 that enables communication capabilities for the multi-layer guidance and control architecture 703 via a wired or wireless communication channel, for example, for the vehicle 701 to communicate with the traffic control system 600.

[0095] FIG. 7B illustrates a schematic diagram of the interaction between the multi-layer guidance and control architecture 703 (i.e., layers of algorithms and techniques for decision-making, motion planning, vehicle control and / or estimation) and other controllers 715 of the vehicle 701 in accordance with some embodiments of the present disclosure. For example, in some embodiments, the controllers 715 of the vehicle 701 are a steering controller 717 and a brake / throttle controller 719 that control the rotation and acceleration of the vehicle 701, respectively. In such a case, the multi-layer guidance and control architecture 703 outputs control inputs to the controllers 717 and 719 to control the state of the vehicle 701. The controllers 715 may also include a higher level controller, e.g., a lane keeping assist controller 721, that further processes the control inputs of the multi-layer guidance and control architecture 703. In either case, the controller 715 uses the output of the multi-layer guidance and control architecture 703 to control at least one actuator of the vehicle 701, such as the steering and / or braking of the vehicle 701, to control the motion of the vehicle 701. In some embodiments, the multi-layer guidance and control architecture 703 determines inputs to the vehicle 701 based on control commands received from the traffic control system 600. The inputs to the vehicle 701 may include one or a combination of the following: acceleration of the vehicle 701, engine torque of the vehicle 701, braking torque, and steering angle.

[0096] FIG. 8A illustrates an application of a traffic control system 600 in a traffic network including multiple interconnected intersections 801, 803, 805, 807, and 809 according to an embodiment of the present disclosure. The overall safety, time efficiency, and energy efficiency of traffic flow in this traffic network can be controlled by the traffic control system 600 according to an embodiment of the present invention. In some embodiments, the traffic control system 600 calculates a coarse motion plan for each CAV in the traffic network along its route from the CAV's current location to the CAV's desired destination, and simultaneously calculates control commands for controllable traffic rules (CTRs) that affect both the CAV and the MCV. CTRs refer to dynamic traffic rules. The coarse motion plan may include a sequence of entry and exit times and average speed values ​​for each CAV at each intersection along the CAV's route from the CAV's current location to the desired destination. Furthermore, the traffic control system 600 affects the behavior of the CAV and the MCV by controlling the CTRs 811, 813, 815, 817 in the entire zone. For example, if intersection 807 does not have a CTR, then a CTR may not need to be present at all intersections.

[0097] In the example situation of Figure 8A, north-south traffic at intersections 803 and 805 is much more congested than east-west traffic at intersections 807 and 809. Traffic control system 600 controls the planned future timing and speed trajectory for a CAV (e.g., vehicle 819) that plans to cross intersection 801 and proceed north-south of the much more congested intersection 803. To improve the overall safety, time efficiency, and energy efficiency of traffic flow in the transportation network, traffic control system 600 commands vehicle 819 to reduce its speed before and / or after crossing intersection 801 such that vehicle 819 is predicted to arrive at intersection 803 at a later time, thus reducing traffic congestion at intersection 803 and reducing the overall waiting time of one or more of the vehicles in the transportation network.

[0098] In addition, the traffic control system 600 also controls the CTR. For example, the traffic control system 600 may shorten the green light duration of the CTR traffic lights 811, 813, 815 in the east-west direction, and thus lengthen the green light duration in the north-south direction, which should ease traffic congestion by allowing more traffic to flow in the more congested north-south direction. However, the traffic control system 600 may only allow green light at 811, 813, 815 if a particular vehicle, such as 819, can pass, as opposed to a method related to averaging traffic volume. This allows the green light to be enabled as needed according to the particular vehicle purpose. For example, the CTR 811 may be green in the east-west direction, but switch to red when the MCV 821 is approaching and the vehicle 819 is also approaching, which causes the MCV 821 to stop and allow the vehicle 819 to pass.

[0099] 8B illustrates an exemplary traffic scene in a traffic network of multiple interconnected intersections and junctions according to one embodiment of the present disclosure. The traffic network includes one or more control vehicles, referred to as CAVs, e.g., 823, 825, 827, and 829, and one or more non-control traffic participants, such as MCVs 831, 833, 835, and 837. The traffic network itself may include multiple interconnected intersections, such as 839 (I1), 841 (I2), and 843 (I3), and multiple interconnected junctions, such as 845 (M1), 847 (M2), and 849 (M3). Both the intersections and junctions are referred to as conflict zones. The conflict zones are interconnected by multiple non-conflict road sections, each of which may include one or more lanes, e.g., 851 (L6), 853 (L47), and 855 (L36). In addition, Figure 8B shows stop lines, such as stop lines 857 (S1), 859 (S2) and 861 (S3), which indicate locations where a vehicle may wait for a certain period of time before entering the intersection. Furthermore, some of the intersections may have CTRs, such as 863-869, as controlled traffic lights.

[0100] 8B additionally illustrates routing information that may be provided by a routing or navigation module for each of the CAVs (e.g., for vehicle 823). For vehicle 823 at a current location 871 and heading for a desired destination 873, the routing or navigation module provides vehicle 823 with a sequence of roads and turns indicated by arrows 875a-875g. Similarly, the same or separate individual routing or navigation modules may provide a sequence of roads and turns from a current location to a desired destination for other vehicles (e.g., CAVs 825, 827, and 829).

[0101] However, it should be noted that the sequence of roads and turns 875a-875g does not itself yet specify a motion plan or route for the vehicle 823. There are several discrete decisions to make, such as which lane the vehicle should travel in, whether the vehicle should change lanes or stay in its current lane, whether the vehicle should start to slow down and stop at a stop line, whether the vehicle is allowed to cross an intersection, etc. Additionally, there are several continuous decisions to make, such as a timed sequence of positions and orientations the vehicle should achieve as it travels from its initial point to its destination. Additionally, it is also necessary to determine the behavior of the CTR, which affects the operation of both the CAV and the MCV. According to some embodiments, a motion plan including a sequence of one or more of the aforementioned discrete and / or continuous decisions along a route from a current position to a desired destination may be calculated by the traffic control system 600 for one or more CAVs, e.g., 823-829 and CTRs 863-869.

[0102] The following description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of exemplary embodiments will provide those skilled in the art with an enabling description for implementing one or more exemplary embodiments. It is contemplated that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosed subject matter as set forth in the appended claims.

[0103] In the following description, specific details are given to allow a thorough understanding of the embodiments. However, those skilled in the art will appreciate that the embodiments may be practiced without these specific details. For example, systems, processes and other elements in the disclosed subject matter may be shown as components in block diagram form in order to avoid obscuring the embodiments in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. Additionally, like reference numbers and names in the various drawings refer to like elements.

[0104] Also, particular embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe operations as a sequential process, many of the operations may be performed in parallel or simultaneously. In addition, the order of operations may be rearranged. A process may be terminated when its operations are completed, but may include additional steps not described or included in the diagram. Moreover, not all operations in any process specifically described may be performed in all embodiments. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, or the like. When a process corresponds to a function, the end of the function may correspond to a return of the function to the calling function or to the main function.

[0105] Furthermore, embodiments of the disclosed subject matter may be implemented, at least in part, either manually or automatically. Manual or automated implementations may be performed or at least assisted by the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, program code or code segments to perform the necessary tasks may be stored in a machine-readable medium. A processor may perform the necessary tasks.

[0106] The various methods or processes outlined herein may be coded as software executable on one or more processors employing any one of a variety of operating systems or platforms. In addition, such software may be written using any of a number of suitable programming languages ​​and / or programming or scripting tools, and compiled as executable machine language code or intermediate code that runs on a framework or virtual machine. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0107] The embodiments of the present disclosure may be embodied as methods, which are provided as examples. The operations performed as part of the method may be ordered in any suitable manner. Thus, while shown as sequential operations in the exemplary embodiments, embodiments may be constructed in which operations are performed in a different order than that shown, including performing some operations simultaneously.

[0108] Although the present disclosure has been described with reference to certain preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the present disclosure. It is therefore the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the present disclosure.

Claims

1. 1. A traffic control system for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually connected vehicles (MCVs) moving in a traffic-forming manner on the same or intersecting roads, the traffic control system 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: collecting digital representations of conditions for each of said CAVs, each of said MCVs, and each of said traffic signs regulating traffic; solving a multivariate mixed integer problem optimizing a cost function for values ​​of control commands to change the state of each of the CAVs and values ​​of control commands to change the state of each of the traffic signs over a finite future prediction time interval, wherein the cost functions for individual objectives of each CAV and each MCV and common objectives of individual groups of the CAVs and the MCVs are optimized according to a motion model of each of the CAVs described by differential or difference equations relating control commands for a CAV to changes in the state of the CAV, constraints modeling general traffic rules, timing constraints, and a switching function relating dynamic traffic rules for an MCV to the state of the MCV and to the state of each of the traffic signs; A traffic control system causing said optimized values ​​of control commands to be transmitted to corresponding CAVs and corresponding traffic signs.

2. The traffic control system of claim 1 , wherein the switching function includes a rule-free motion model that is not affected by the dynamic traffic rules, and one or more rule-regulated motion models.

3. The traffic control system of claim 2 , wherein a rule-regulated motion model for an MCV among the one or more rule-regulated motion models is selected based on the state of the traffic sign and the state of the MCV.

4. The traffic control system of claim 2 , wherein each rule-regulated motion model is independent of the state of the traffic sign.

5. 2. The traffic control system of claim 1, wherein the cost function includes a term indicative of a difference between a current speed and a desired speed of each of the CAVs and the MCVs.

6. 2. The traffic control system of claim 1, wherein the cost function includes a term indicative of a difference between a current lane and a desired lane for each of the CAV and the MCV.

7. The traffic control system of claim 1 , wherein the cost function includes a term that indicates a difference between a combination of current states of the traffic signs and a desired value for that combination.

8. The traffic control system of claim 1 , wherein the cost function includes a term that penalizes changes in traffic sign states.

9. The traffic control system of claim 1 , wherein the cost function includes a term that penalizes each vehicle's lane changes.

10. The traffic control system of claim 1 , wherein the timing constraints include at least one of a timer that enables or disables the traffic sign change and a timer that enables or disables a CAV travel lane change.

11. 2. The traffic control system of claim 1, wherein the multivariate mixed integer problem is one of a mixed-integer linear program (MILP) or a mixed-integer quadratic program (MIQP).

12. The traffic control system of claim 1 , wherein the processor is further configured to solve a multivariate mixed integer problem based on a branch and bound algorithm.

13. The traffic control system of claim 12 , wherein the branch and bound algorithm includes determining at least one variable based on the state of each of the CAVs and the MCVs.

14. 2. The traffic control system of claim 1, wherein the general traffic rules include one or more of rules regarding crossing intersections, avoiding collisions with adjacent vehicles, occupying open lanes, and meeting lane speed limits.

15. The traffic control system of claim 1 , wherein the dynamic traffic rules correspond to traffic signs that change based on corresponding control commands.

16. The traffic control system of claim 15 , wherein the traffic signs include one or more of a traffic light, a variable speed limit, and a lane access status.

17. 2. The traffic control system of claim 1, wherein the state of the CAV includes one or more of a position, a speed, an acceleration, and a lane of the CAV, and the state of the MCV includes one or more of a position, a speed, an acceleration, and a lane of the MCV.

18. The traffic control system of claim 1 , wherein the state of each of the traffic signs includes one or more of a traffic light status, a variable speed limit value, and a lane access status.

19. 1. A method for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually connected vehicles (MCVs) moving in a traffic-forming manner on the same or intersecting roads, the method comprising: collecting digital representations of conditions for each of the CAVs, each of the MCVs, and each of the traffic signs regulating traffic; and solving a multivariate mixed integer problem optimizing a cost function for values ​​of control commands to change the state of each of the CAVs and values ​​of control commands to change the state of each of the traffic signs over a finite future prediction time interval, wherein the cost functions for individual objectives of each CAV and each MCV and common objectives of individual groups of CAVs and MCVs are optimized according to a motion model of each of the CAVs described by differential equations relating control commands for a CAV to changes in the state of the CAV, constraints modeling general traffic rules, timing constraints, and a motion model of each of the MCVs described by switching functions relating dynamic traffic rules for an MCV to the state of the MCV and to the state of each of the traffic signs, the method further comprising: The method includes transmitting the optimized value of the control command to a corresponding CAV and a corresponding traffic sign.

20. 1. A non-transitory computer-readable storage medium having embodied thereon a program executable by a processor for performing a method for cooperatively controlling one or more connected autonomous vehicles (CAVs) and one or more manually connected vehicles (MCVs) moving in a traffic-forming manner on the same or intersecting roadways, the method comprising: collecting digital representations of conditions for each of the CAVs, each of the MCVs, and each of the traffic signs regulating traffic; and solving a multivariate mixed integer problem (MIP) optimizing a cost function for values ​​of control commands to change the state of each of the CAVs and values ​​of control commands to change the state of each of the traffic signs over a finite future prediction time interval, wherein the cost functions for individual objectives of each CAV and each MCV and common objectives of individual groups of CAVs and MCVs are optimized according to a motion model of each of the CAVs described by differential equations relating control commands for a CAV to changes in the state of the CAV, constraints modeling general traffic rules, timing constraints, and a motion model of each of the MCVs described by switching functions relating dynamic traffic rules for MCVs to the state of the MCVs and to the state of each of the traffic signs, the method further comprising: transmitting the optimized value of the control command to a corresponding CAV and a corresponding traffic sign.

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