Systems and Methods for a Vehicle and a Storage Medium

Abstract

The present invention provides a system and method for a vehicle and a storage medium. A method for managing the behavior of traffic lights is provided, the method comprising: using at least one processor to obtain information corresponding to a region of interest including two or more road segments, each road segment being associated with a plurality of physical traffic lights configured to control traffic movement associated with the road segment; for each of the two or more road segments, using at least one processor to generate a logical traffic light representing a grouping of the plurality of physical traffic lights; and using at least one processor to determine one or more characteristics of each logical traffic light based on the information corresponding to the region of interest. A system and a computer program product are also provided.

Description

Background Art

[0001] The present invention relates to a system and method for a vehicle and a storage medium, and more particularly to a system and method for managing traffic light behavior. Background Art

[0003] When making decisions near a region of interest (e.g., an intersection), the system can consider the behavior of individual physical traffic lights at the region of interest. However, this can be particularly troublesome and prone to inconsistencies, errors or error handling, and inefficiencies especially when there are a large number of physical traffic lights controlling traffic movement at the region of interest. Summary of the Invention

[0004] According to one aspect of the present invention, a method for a vehicle includes: using at least one processor to obtain information corresponding to a region of interest including two or more road segments, wherein each road segment is associated with a plurality of physical traffic lights configured to control traffic movement associated with the road segment; for each of the two or more road segments, using the at least one processor to generate a logical traffic light representing a grouping of the plurality of physical traffic lights; and using the at least one processor to determine one or more characteristics of each logical traffic light based on the information corresponding to the region of interest.

[0005] According to one aspect of the present invention, a method for a vehicle includes: using at least one processor to obtain region information of at least one region of interest of the vehicle, wherein the at least one region of interest includes two or more road segments, and each road segment is associated with a corresponding logical traffic light representing an aggregation of one or more corresponding physical traffic lights controlling vehicle movement at the road segment, and wherein the region information includes information related to the logical traffic lights associated with the road segments in the at least one region of interest; using the at least one processor, using the region information of the at least one region of interest to determine traffic light information associated with a route of the vehicle, the route including at least one road segment of the at least one region of interest; and using the at least one processor, using the traffic light information to operate the vehicle along the route.

[0006] According to one aspect of the present invention, a system for a vehicle includes: at least one processor, and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the above method.

[0007] According to one aspect of the present invention, at least one non-transitory storage medium stores instructions that, when executed by at least one processor, cause the at least one processor to perform the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1A Shows an example intersection with physical traffic lights.

[0009] Figure 1B Shows an example of Figure 1A the logical traffic lights representing the physical traffic lights at the intersection.

[0010] Figures 2A - 2C Shows Figure 1A at the intersection of Figure 1B the state change of the logical traffic lights.

[0011] Figure 3 Is a diagram of an example finite state machine (FSM) showing the state of an intersection.

[0012] Figure 4 Is a flowchart of a process for determining information about logical traffic lights.

[0013] Figure 5 Is an example environment of a vehicle that can implement one or more components including an autonomous system.

[0014] Figure 6 Is a diagram of one or more systems of a vehicle including an autonomous system.

[0015] Figure 7 Is a diagram of one or more devices and / or Figure 5 and Figure 6 one or more components of a system.

[0016] Figure 8 Is a diagram of certain components of an autonomous system.

[0017] Figure 9 Shows a block diagram of an architecture for managing the traffic light behavior of a vehicle.

[0018] Figure 10 Is a flowchart of a process for using information about logical traffic lights to manage the traffic light behavior of a vehicle. DETAILED DESCRIPTION

[0019] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the described embodiments of the invention may be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form in order to avoid unnecessarily obscuring aspects of the invention.

[0020] In the drawings, for ease of description, a specific arrangement or order of schematic elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that unless explicitly described, the specific order or arrangement of the schematic elements in the drawings is not intended to imply a required order or sequence of processing, or separation of processing. In addition, unless explicitly described, the inclusion of schematic elements in the drawings is not intended to imply that such elements are required in all embodiments, nor that the features represented by such elements cannot be included in some embodiments or combined with other elements in some embodiments.

[0021] Furthermore, in the drawings, connecting elements (such as solid lines, dashed lines, or arrows, etc.) are used to illustrate connections, relationships, or associations between or among two or more other schematic elements. The absence of any such connecting element is not intended to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the content of the present invention. In addition, for ease of illustration, a single connecting element may be used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents the communication of a signal, data, or instruction (e.g., "software instruction"), those skilled in the art should understand that such an element may represent one or more signal paths (e.g., a bus) that may be required to affect the communication.

[0022] Although terms such as "first", "second", and / or "third" are used to describe various elements, these elements should not be limited by these terms. The terms "first", "second", and / or "third" are only used to distinguish one element from another. For example, without departing from the scope of the described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0023] The terms used in the description of the various embodiments described herein are included only for the purpose of describing a particular embodiment and are not intended to be limiting. As used in the description of the various embodiments and the appended claims, the singular forms "a", "an", and "the" are also intended to include the plural forms and may be used interchangeably with "one or more than one" or "at least one", unless the context clearly dictates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms "comprises", "comprising", "includes", and / or "including" are used in this specification, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0024] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof, etc.) that is to communicate with another unit, this means that the unit can receive information from the other unit directly or indirectly and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is inherently wired and / or wireless. Additionally, two units can communicate with each other even if the information transmitted between the first unit and the second unit is modified, processed, relayed, and / or routed. For example, even if the first unit receives information passively and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediate unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message can refer to a network packet (e.g., a data packet, etc.) that includes data.

[0025] As used herein, depending on the context, the term "if" is optionally interpreted to mean "when", "at the time", "in response to determining that", and / or "in response to detecting", etc. Similarly, depending on the context, the phrase "if it has been determined" or "if [the stated condition or event] is detected" is optionally interpreted to mean "when determining...", "in response to determining that", or "when detecting [the stated condition or event]" and / or "in response to detecting [the stated condition or event]", etc. Further, as used herein, terms such as "has", "have", or "having" are intended to be open-ended terms. Additionally, unless expressly stated otherwise, the phrase "based on" is intended to mean "at least partially based on".

[0026] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0027] General Overview

[0028] For making reliable and efficient decisions, a computing system is configured to simulate (or configure) the behavior of traffic lights at a region of interest (e.g., an intersection) in a consistent, robust, and complete manner. Specifically, the computing system groups / aggregates multiple physical traffic lights that control the same road segment (e.g., the same incoming road segment) at the region of interest into a single logical traffic light. The computing system uses a finite state machine (FSM) defined by a series of states to determine the state of the region of interest (e.g., determine the state of the logical traffic light(s) that control(s) the road segment(s) at the intersection). The individual states of the FSM representing the state of the region of interest are determined by the possible permutations and combinations of the states of the (one or more) logical traffic light groups for the road segments in the region of interest. The computing system can determine that the region of interest is in a particular state in response to a trigger (e.g., a distance trigger or a time trigger). The computing system can transition the region of interest through a state cycle formed by multiple states and transition the logical traffic lights associated with the region of interest to states corresponding to the respective states of the region of interest. The computing system can simulate the behavior of the logical traffic lights at the region of interest in response to different traffic conditions and adjust the properties of the physical traffic lights in the real world corresponding to the logical traffic lights, such as the duration, location, or number of the respective states (such as green, yellow, red, etc.) of the physical traffic lights.

[0029] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement managing traffic light behavior. The database stores a data structure associating each area of interest with a logical traffic light grouping of the physical traffic lights at the area of interest, as well as the corresponding status of the logical traffic light grouping, without storing information related to a large number of physical traffic lights. For visualization, a graphical interface can be used to provide the logical traffic lights of the road segments in the area of interest.

[0030] Through the implementation of the systems, methods, and computer programs described herein, the technology for managing traffic light behavior has the following advantages. First, the technology can use a single logical traffic light to represent a large number of physical traffic lights (e.g., two or more traffic lights per road segment; can be a larger number such as 5, 10, or 20) at a road segment (e.g., intersection) at the area of interest, and use a series of states of the area of interest to determine the traffic light behavior at the area of interest, which can facilitate determining the behavior of all traffic lights in a simple, consistent, robust, and complete manner for reliable and efficient decision-making. Conversely, especially in the case where there are a large number (e.g., 5, 10, or 20) of physical traffic lights controlling traffic movement at a road segment, determining the status of the area of interest based on individually determining the status of each of the several physical traffic lights can be cumbersome; this may lead to inconsistencies, errors, or error handling and inefficiencies. Second, the technology enables simulating the behavior of the logical traffic lights at the area of interest in response to different traffic conditions and configuring the physical traffic lights in the real world based on the results of the simulation, which can be more efficient, accurate, reliable, and economical. Third, the technology enables simulating the behavior of a vehicle along a route including one or more areas of interest or the behavior of a vehicle on a specific area of interest, which can be used for route planning or scheduling. Fourth, the technology enables storing information about the logical traffic lights at a road segment or the status of an area of interest (e.g., intersection) in the database without separately storing information about a large number of physical traffic lights, which can greatly save storage space, simplify storage processing, and use fewer communication resources and / or computing resources for updates, for example, when editing a map. Fifth, the technology can manage traffic light behavior at the intersection level rather than at the level of individual physical traffic lights, which can be easier to edit, update, and expand to multiple levels (e.g., route level or district level). Sixth, the technology can ensure that the statuses of all physical traffic lights and all road segments at an intersection are consistent with each other and change together at the same time. Seventh, the technology can use a graphical interface to display a single logical traffic light rather than a large number of physical traffic lights for an area of interest, which can greatly simplify visualization and make it easier to view and understand.

[0031] Example Technologies for Managing Traffic Light Behavior

[0032] Implementations of the present invention provide techniques for managing traffic light behavior using logical traffic lights at a region of interest (e.g., an intersection). For example, the techniques may be implemented by a computing system including one or more computing devices in a simulated environment. The computing system uses corresponding logical traffic lights to simulate the behavior of physical traffic lights at the region of interest and adjusts the properties of the physical traffic lights in a virtual world (e.g., a simulation program or application) or the real world. The computing system may also use corresponding logical traffic lights to simulate the behavior of vehicles on the region of interest and adjust the properties of the physical traffic lights in a virtual world or the real world, and / or adjust the operations and / or routes for the vehicles in a virtual world or the real world. In some cases, the techniques may be implemented in a vehicle system such that the vehicle system is capable of maneuvering through an intersection in the real world.

[0033] Figure 1A A schematic diagram showing an example intersection 100 in a map. In some embodiments, intersection 100 is a representation of an intersection through which vehicles pass and corresponds to a region of interest of the vehicles. For example, in some cases, intersection 100 is a visualization in a simulated environment executed by an application running on a computing system. As shown, intersection 100 includes four road segments 110, 120, 130, and 140 around a center C (as shown by the dashed box in Figures 1A - 1B ). Each road segment represents a region included in the intersection and is associated with two roads having, for example, opposite or angled road directions. Each road includes one or more lanes. As an example, road segment 110 is associated with a first road 114 having a first road direction 113 and a second road 116 having a second road direction 115. In some examples, the second road direction 115 is opposite to the first road direction 113. In some examples, the angle between the first road direction 113 and the second road direction 115 is greater than 0 degrees and less than 90 degrees.

[0034] At (or around) each road segment, there is one or more physical traffic lights located there and configured to control traffic movement for the road segment and one or more other road segments. As Figure 1AAs shown, at (or around) road block 110, there are six physical traffic lights 112a, 112b, 112c, 112d, 112e, 112f; at (or around) road block 120, there are three physical traffic lights 122a, 122b, 122c; at (or around) road block 130, there are six physical traffic lights 132a, 132b, 132c, 132d, 132e, 132f; at (or around) road block 140, there are three physical traffic lights 142a, 142b, 142c. Each physical traffic light includes three bulbs, such as red, green, and yellow. In one embodiment, the physical traffic light includes an arrow, such as a left arrow, a right arrow, an up arrow, or a down arrow. For example, physical traffic light 112d includes a right arrow.

[0035] Each physical traffic light is placed facing the road block to control the movement of traffic (e.g., including vehicles and / or pedestrians) associated with (e.g., coming from) the road block. For example, arrow 111a shows that physical traffic light 112a is placed facing vehicles traveling on road 114 associated with road block 110, and arrow 111d shows that physical traffic light 112d is placed facing road block 130 to control the traffic movement associated with road block 130. Thus, a road block can be associated with the physical traffic light located at the road block and the physical traffic lights located at one or more other road blocks at the same intersection 100, and the physical traffic lights are placed facing the road block to control the traffic movement associated with that road block.

[0036] As Figure 1A shown, there are seven physical traffic lights (connected by solid lines to vehicle 102) placed facing road block 110 to regulate the traffic movement of vehicles from road block 110. The seven physical traffic lights include 112a, 112b, 112c, 132a, 132b, 132c, 132e. Among them, physical traffic lights 112a, 112b, 112c are located at road block 110, and physical traffic lights 132a, 132b, 132c, 132e are located at the position of road block 130 facing road block 110.

[0037] Vehicle 102 is traveling along a route (e.g., route 104) approaching intersection 100 from road block 110. To make driving decisions, in one embodiment, vehicle 102 monitors the behavior of the physical traffic lights (i.e., the seven physical traffic lights 112a, 112b, 112c, 132a, 132b, 132c, 132e) that control the traffic movement of road block 110 at intersection 100, which can be troublesome and prone to inconsistencies, errors, or inaccuracies, as well as inefficiencies.

[0038] To address the above problems, the implementation of the present invention provides grouping (or aggregating) multiple physical traffic lights for the same road segment at a region of interest (e.g., an intersection) into a single logical traffic light. The behavior of the physical traffic lights is represented by the state of the single logical traffic light. Thus, instead of making driving decisions based on the behavior of the physical traffic lights, a vehicle can rely only on the current state of the single logical traffic light.

[0039] Figure 1B Shows a representation Figure 1A Example 150 of a logical traffic light for the physical traffic lights at intersection 100. In some embodiments, example 150 is a visualization in a simulation environment executed by an application running on a computing system. Each logical traffic light corresponds to a respective road segment. For example, logical traffic light 152 for road segment 110 is an aggregation or grouping of seven physical traffic lights 112a, 112b, 112c, 132a, 132b, 132c, 132e. Similarly, logical traffic light 154 represents a grouping of the multiple physical traffic lights that control traffic movement associated with road segment 120, logical traffic light 156 represents a grouping of the multiple physical traffic lights that control traffic movement associated with road segment 130, and logical traffic light 158 represents a grouping of the multiple physical traffic lights that control traffic movement associated with road segment 140.

[0040] In one embodiment, a logical traffic light includes multiple logical bulbs, such as one or more logical red bulbs, logical yellow bulbs, and / or logical green bulbs. Information of the logical traffic light includes information of each logical bulb. Information of the logical bulb includes at least one of the following: shape (e.g., circular, right arrow, left arrow, up arrow, down arrow, or unknown), color (e.g., red, yellow, green, or unknown), state (e.g., lit, off, flashing, or unknown), and duration (e.g., 5s, 10s, or 20s). In an embodiment, the logical bulb of the logical traffic light corresponds to a physical bulb among the multiple physical traffic lights represented by the logical traffic light that has at least one of the same shape, the same color, and the same state.

[0041] In one embodiment, the data structure for storing data associated with logical traffic light bulbs in a database (e.g., in a computing system or on a remote server) is as follows:

[0042]

[0043]

[0044] In some embodiments, a vehicle approaching an intersection uses historical data (and / or other real-time data) and the current time point to determine, through simulation, the current state of the logical traffic light for the corresponding road segment. The simulation can also determine the remaining time of the current state of the logical traffic light. In an example, the state of the logical traffic light changes after a cycle that includes 20 seconds of red, 5 seconds of yellow, and 20 seconds of green. The historical data shows that at 8:00 am, the state of the logical traffic light starts from red. Then, according to the simulation, at 9:01 am, the vehicle can determine that the state of the logical traffic light is red and the remaining time of the red state is 5 seconds; at 9:02 am, the vehicle can determine that the state of the logical traffic light is green and the remaining time of the green state is 15 seconds.

[0045] In one embodiment, the data structure for storing data associated with the logical traffic light (e.g., logical traffic light 152) of a road segment (e.g., road segment 110) in a database is as follows:

[0046]

[0047] In some embodiments, based on one or more of the current location of the vehicle, the route of the vehicle, and the current speed of the vehicle, the vehicle determines what the current state of the logical traffic light and the remaining time of the current state are when the vehicle arrives at the intersection from the corresponding road segment. Based on the current state and the remaining time in that state, the vehicle can determine what action the vehicle is to take, such as stop, decelerate, continue at the current speed, or increase the current speed.

[0048] At the intersection, the vehicle arrives from a road segment (or marked as an incoming roadblock or from-roadblock (e.g., road segment 110)), and there can be one or more outgoing road segments (or marked as to-roadblock (e.g., road segments 120, 130, 140)). The vehicle can determine the operation based on the logical traffic lights for the outgoing road segments.

[0049] In one embodiment, the data structure for storing data associated with the logical traffic lights (e.g., 154, 156, 158) of the incoming road segment and the outgoing road segments in a database is as follows:

[0050]

[0051] The traffic light behavior associated with an intersection is the accumulation of the logical traffic light behaviors of the road segments in the intersection. In one embodiment, the data structure for storing traffic light data associated with (one or more) intersections in a database is as follows:

[0052]

[0053] The traffic light behavior at an intersection is represented by the traffic light behavior of the logical traffic lights of the road blocks in the intersection. The states of the logical traffic lights of different road blocks are consistent with each other and change in a synchronous manner. The state of the intersection is represented by the state of the logical traffic lights. When the state of the intersection changes, the states of all the logical traffic lights change to the new state; and when the state of any one logical traffic light changes, the state of the intersection changes (or moves) to the new state.

[0054] As an example, Figures 2A to 2C shows the states of the logical traffic lights 152, 154, 156, 158 of the road blocks 110, 120, 130, 140 at intersection 100. Figure 2A shows the first state 200 of intersection 100, in which the green logic bulbs of the logical traffic lights 152, 156 are lit, and the red logic bulbs of the logical traffic lights 154, 158 are lit. Figure 2B shows the second state 210 of intersection 100, in which the yellow logic bulbs of the logical traffic lights 152, 156 are lit, and the red logic bulbs of the logical traffic lights 154, 158 are lit. Figure 2C shows the third state 220 of intersection 100, in which the red logic bulbs of the logical traffic lights 152, 156 are lit, and the green logic bulbs of the logical traffic lights 154, 158 are off. In some cases, Figures 2A to 2C is a visualization in a simulation environment executed by an application program running on a computing system.

[0055] In one embodiment, the state representing the behavior of the logical traffic lights at an intersection is represented by a finite state machine (FSM). Figure 3 Shows an example FSM 300. The FSM is defined by a finite number of states (state 1, state 2, …, state n, where n is an integer greater than 1). In some embodiments, the FSM is configured for the logical traffic lights of a road block. In some embodiments, the FSM is configured for an intersection. Each state of the FSM is determined by a combination of the states of the logical traffic lights of the road blocks in the intersection. The state of the FSM can be the first state 200, the second state 210, or the third state 220. For example, the FSM represents Figures 2A to 2C the three states shown in Figure 2A where state 1 represents Figure 2B the first state 200 of Figure 2CThe third state 220. The states of the FSM form a cycle (e.g., returning from state n to state 1), and the state of the intersection sequentially moves to the next state according to the FSM at the end of the duration of the current state. The state of the intersection is one of the states of the FSM at any given time. The FSM 300 can be used to maintain the entire intersection in a consistent state. For example, as Figure 2A and Figure 2B shown, from the first state 200 to the second state 210, only the states of the logic traffic lights 152, 156 change, but the FSM moves the state of the entire intersection from the first state to the second state.

[0056] As an example, the intersection includes two road blocks with two corresponding logic traffic lights. The durations of the red, yellow, and green signals for the logic traffic lights are 30 seconds, 5 seconds, and 20 seconds respectively. The intersection has six states as shown below. After state 6 ends, the intersection changes back to state 1.

[0057]

[0058] In one embodiment, the vehicle determines that the intersection changes from the current state to a new state in response to the occurrence of a trigger event. The new state after the transition can be the state immediately following the current state in the FSM. In an embodiment, the trigger event is associated with the distance between the vehicle and the intersection. For example, the distance is the distance between the center of the vehicle and the center of the intersection, such as the length of the dashed line from the vehicle 102 to the center C of the intersection 100 as Figure 1A shown. In addition to or as an alternative to the distance, the trigger event can also be associated with a delay time. In an embodiment, the trigger event is associated with the expiration of a time period after the start of the simulation of the traffic light behavior at the intersection, for example.

[0059] In one embodiment, the traffic light information of the intersection is stored in a database. In the database, the identifier of the intersection is stored in association with multiple states. Each of the multiple states is associated with the following: the corresponding duration of the state (e.g., 20 seconds, 10 seconds, or 5 seconds), the identifiers of the multiple road blocks at the intersection (e.g., the incoming road block, the outgoing road block, and / or other adjacent road blocks), and the information of the logic traffic lights associated with the multiple road blocks.

[0060] In one embodiment, the data structure for storing the traffic light data of the intersection in the database is as shown below:

[0061]

[0062] The traffic light data of intersections stored in the database can be visualized in a graphical interface. For example, instead of presenting the physical traffic lights at an intersection (e.g., as shown in Figure 1A ), the logical traffic lights of the road blocks at the intersection are used to define the traffic light behavior and presented in the map (as shown in Figure 1B ), which can reduce complexity and make it easier to view and understand. In addition, as shown in Figures 2A - 2C , the computing system can manage the change of the state of the traffic light behavior of intersections at the intersection level, which can be easier to edit, update, and extend to multiple levels, such as the route level or the area level. In one example, along the route traversed by a vehicle, the computing system can manage the traffic light behavior at multiple intersections along the route. In another example, in the area where the vehicle is located, the computing system can manage the traffic light behavior at multiple intersections within the area.

[0063] Figure 4 is a flowchart of a process 400 for managing traffic light behavior, particularly for determining information related to the logical traffic lights of road blocks in a region of interest. In some embodiments, process 400 is performed (e.g., fully and / or partially) by a computing system including at least one computing device. The computing system can be in a server or in a vehicle system.

[0064] In process 400, the computing system obtains information corresponding to the region of interest (402). In some embodiments, the region of interest includes an intersection having two or more road blocks. Each road block is associated with a plurality of physical traffic lights that control traffic movement associated with the road block (e.g., vehicles from the road block enter the region of interest).

[0065] In some embodiments, the plurality of physical traffic lights are located at two or more different road blocks in the region of interest. As an example, as shown in Figure 1A , road block 110 is associated with seven physical traffic lights 112a, 112b, 112c, 132a, 132b, 132c, 132e that regulate vehicles entering intersection 100 from road block 110. Among them, physical traffic lights 112a, 112b, 112c are located at road block 110, and physical traffic lights 132a, 132b, 132c, 132e are located at the position of road block 110 of road block 130.

[0066] In some embodiments, the computing system determines a region of interest from a map based on at least one of the current location of the vehicle, the current route of the vehicle, and a particular area. In some examples, the computing system selects an area to determine traffic light behavior in an area that includes one or more intersections. In some examples, the route of the vehicle includes one or more intersections. In some examples, based on the location of the vehicle, there is one or more intersections around the vehicle. The computing system obtains area information of each intersection to determine the traffic light behavior of the intersection.

[0067] The information corresponding to the region of interest includes information about physical traffic lights in the region of interest, e.g., location, orientation, type, shape, color, or status. The information about physical traffic lights in the region of interest is consistent with each other. In some embodiments, the computing system obtains the current status of at least one physical traffic light associated with a road segment, and determines the current status of the remaining physical traffic lights associated with the road segment based on the obtained current status of the at least one physical traffic light.

[0068] Continuing to refer to process 400, the computing system determines, for each road segment, information about a logical traffic light representing a grouping of multiple physical traffic lights associated with the road segment (404) based on the information corresponding to the region of interest. The computing system may generate a logical traffic light for the road segment and determine one or more characteristics of the logical traffic light based on the information corresponding to the region of interest. The information about the logical traffic light includes one or more characteristics of the logical traffic light.

[0069] In some embodiments, as Figure 1B shown, a logical traffic light (e.g., Figure 1B logical traffic lights 152, 154, 156, 158) includes a plurality of logical bulbs (e.g., red, yellow, green). The information about the logical traffic light includes information about each logical bulb among the plurality of logical bulbs, and the information includes at least one of shape, color, and status. The information about the logical traffic light may further include at least one of the number of statuses, the duration of each status, and behavior. The behavior of the logical traffic light may include interaction with one or more other logical traffic lights at the region of interest. Each logical bulb among the plurality of logical bulbs corresponds to a corresponding physical bulb among the multiple physical traffic lights having the same shape, the same color, and the same status.

[0070] In some embodiments, the computing system stores in a database information related to a logical traffic light associated with two or more road blocks at a region of interest. The computing system may generate an identifier for the region of interest and associate the identifier of the region of interest with a plurality of states. Each state in the plurality of states is associated with: the duration of the state (e.g., 5 seconds, 20 seconds, 30 seconds), identifiers of a plurality of road blocks in the region of interest, and information about the logical traffic light associated with each of the plurality of road blocks in the state.

[0071] In some embodiments, the computing system uses a finite state machine (FSM) defined by the plurality of states to determine the state of the region of interest. The computing system may determine that the region of interest is in a particular state among the plurality of states at a particular moment. The computing system may transition the region of interest from a first state to a second state in a state transition loop formed by the plurality of states at the end of the duration of the first state. The FSM maintains the region of interest in a consistent state. The computing system may transition the logical traffic light associated with the region of interest to a state corresponding to the second state of the region of interest in conjunction with transitioning the region of interest from the first state to the second state.

[0072] In some embodiments, the computing system also associates the identifier of the region of interest with one or more trigger events and transitions the region of interest to a corresponding particular state in response to the occurrence of each of the one or more trigger events. In some examples, the trigger event is associated with the distance between a vehicle and the region of interest. In some examples, the trigger event is associated with the expiration of a time period.

[0073] In some embodiments, the computing system generates a representation of a vehicle, configures the vehicle to approach and / or pass through the region of interest, and determines the behavior of the vehicle when the vehicle is approaching and / or passing through the region of interest based on a change in the state of the logical traffic light at the region of interest. The computing system may adjust the corresponding duration of at least one state among the plurality of states based on the result of determining the behavior of the vehicle.

[0074] Continuing to refer to process 400, in some embodiments, the computing system configures one or more properties (406) of at least one physical traffic light associated with the logical traffic light (e.g., in a virtual world or the real world) based on the determined information corresponding to the logical traffic light. The computing system can change one or more characteristics of at least one logical traffic light associated with the area of interest based on a change in the state of the area of interest, and change one or more properties of at least one physical traffic light corresponding to at least one logical traffic light based on a change in one or more characteristics of at least one logical traffic light. For example, the computing system can change the duration of each state of at least one physical traffic light, the location of at least one physical traffic light, and / or the number of at least one physical traffic light.

[0075] In some embodiments, the computing system provides data including one or more characteristics of the logical traffic light for visualization (e.g., on a display of the computing system) using a graphical interface associated with the logical traffic light.

[0076] Example Systems and Applications

[0077] Implementations of the present invention provide techniques for managing traffic light behavior using logical traffic lights at areas of interest, which can be applied in any suitable system and / or any suitable application. For illustration, the following references Figures 5 to 10 describe implementations of the techniques in vehicles such as autonomous vehicles.

[0078] Referring now to Figure 5 , an example environment 500 is shown in which vehicles including autonomous systems and vehicles not including autonomous systems are operated. As illustrated, environment 500 includes vehicles 502a - 502n, objects 504a - 504n, routes 506a - 506n, area 508, vehicle-to-infrastructure (V2I) devices 510, network 512, remote autonomous vehicle (AV) system 514, queue management system 516, and V2I system 518. Vehicles 502a - 502n, vehicle-to-infrastructure (V2I) devices 510, network 512, autonomous vehicle (AV) system 514, queue management system 516, and V2I system 518 are interconnected via a wired connection, a wireless connection, or a combination of wired or wireless connections (e.g., establishing a connection for communication, etc.). In some embodiments, objects 504a - 504n are interconnected with at least one of vehicles 502a - 502n, vehicle-to-infrastructure (V2I) devices 510, network 512, autonomous vehicle (AV) system 514, queue management system 516, and V2I system 518 via a wired connection, a wireless connection, or a combination of wired or wireless connections.

[0079] Vehicles 502a - 502n (individually referred to as vehicle 502 and collectively referred to as vehicles 502) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 502 is configured to communicate with V2I device 510, remote AV system 514, queue management system 516, and / or V2I system 518 via network 512. In some embodiments, vehicle 502 includes cars, buses, trucks, and / or trains, etc. In some embodiments, vehicle 502 is the same as or similar to vehicle 600 described herein (see Figure 6 ). In some embodiments, vehicles 600 in a group of vehicles 600 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicle 502 travels along corresponding routes 506a - 506n (individually referred to as route 506 and collectively referred to as routes 506). In some embodiments, one or more vehicles 502 include an autonomous system (e.g., an autonomous system the same as or similar to autonomous system 602).

[0080] Objects 504a - 504n (individually referred to as object 504 and collectively referred to as objects 504) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., a building, a sign, a fire hydrant, etc.), etc. Each object 504 (e.g., located at a fixed location and over a period of time) is stationary or (e.g., having a speed and associated with at least one trajectory) moving. In some embodiments, object 504 is associated with a corresponding location in area 508.

[0081] Routes 506a - 506n (individually referred to as route 506 and collectively as routes 506) are each associated with (e.g., define) a series of actions (also referred to as a trajectory) that an AV can navigate along. Each route 506 begins at an initial state (e.g., a state corresponding to a first spatio - temporal location and / or speed, etc.) and ends at a final goal state (e.g., a state corresponding to a second spatio - temporal location different from the first) or a goal region (e.g., a subspace of acceptable states (e.g., termination states)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or region includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, route 506 includes multiple acceptable sequences of states (e.g., multiple sequences of spatio - temporal locations) that are associated with (e.g., define) multiple trajectories. In an example, route 506 includes only high - level actions or imprecise state locations, such as a series of connecting roads indicating a direction change at a roadway intersection, etc. Additionally or alternatively, route 506 can include more precise actions or states, such as, for example, a specific target lane or precise location within a lane region and a target rate at those locations. In an example, route 506 includes multiple precise state sequences along at least one high - level action with a finite look - ahead horizon to reach an intermediate goal, where the combination of successive iterations of the finite - horizon state sequences cumulatively corresponds to multiple trajectories that together form a high - level route terminating at the final goal state or region.

[0082] Region 508 includes a physical area (e.g., a geographic area) that a vehicle 502 can navigate. In an example, region 508 includes at least one state (e.g., a country, a province, an individual state among multiple states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, region 508 includes at least one named arterial road (referred to herein as a "road"), such as a highway, an interstate highway, a parkway, a city street, etc. Additionally or alternatively, in some examples, region 508 includes at least one unnamed road, such as a driveway, a section of a parking lot, a section of an open space and / or undeveloped area, a dirt road, etc. In some embodiments, a road includes at least one lane (e.g., the portion of the road that a vehicle 502 can traverse). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.

[0083] The vehicle-to-infrastructure (V2I) device 510 (sometimes referred to as a vehicle-to-everything (V2X) device) includes at least one device configured to communicate with the vehicle 502 and / or the V2I system 518. In some embodiments, the V2I device 510 is configured to communicate with the vehicle 502, the remote AV system 514, the queue management system 516, and / or the V2I system 518 via the network 512. In some embodiments, the V2I device 510 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), lane markings, streetlights, a parking meter, etc. In some embodiments, the V2I device 510 is configured to communicate directly with the vehicle 502. Additionally or alternatively, in some embodiments, the V2I device 510 is configured to communicate with the vehicle 502, the remote AV system 514, and / or the queue management system 516 via the V2I system 518. In some embodiments, the V2I device 510 is configured to communicate with the V2I system 518 via the network 512.

[0084] The network 512 includes one or more wired and / or wireless networks. In an example, the network 512 includes a cellular network (e.g., a long term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-based network, a cloud computing network, etc., and / or a combination of some or all of these networks.

[0085] The remote AV system 514 includes at least one device configured to communicate with the vehicle 502, the V2I device 510, the network 512, the queue management system 516, and / or the V2I system 518 via the network 512. In an example, the remote AV system 514 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 514 is co-located with the queue management system 516. In some embodiments, the remote AV system 514 participates in the installation of some or all of the components of the vehicle (including an autonomous system, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing). In some embodiments, the remote AV system 514 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0086] The queue management system 516 includes at least one device configured to communicate with the vehicle 502, the V2I device 510, the remote AV system 514, and / or the V2I system 518. In an example, the queue management system 516 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 516 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including an autonomous system and / or vehicles not including an autonomous system), etc.).

[0087] In some embodiments, the V2I system 518 includes at least one device configured to communicate with the vehicle 502, the V2I device 510, the remote AV system 514, and / or the queue management system 516 via the network 512. In some examples, the V2I system 518 is configured to communicate with the V2I device 510 via a connection different from the network 512. In some embodiments, the V2I system 518 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 518 is associated with a municipality or a private institution (e.g., a private institution for maintaining the V2I device 510, etc.).

[0088] Provide Figure 5 The number and arrangement of the illustrated elements are provided as an example. Compared with Figure 5 the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements with a different arrangement. Additionally or alternatively, at least one element of the environment 500 may perform one or more functions described as being performed by Figure 5 at least one different element. Additionally or alternatively, at least one group of elements of the environment 500 may perform one or more functions described as being performed by at least one different group of elements of the environment 500.

[0089] Now referring to Figure 6 , the vehicle 600 includes an autonomous system 602, a powertrain control system 604, a steering control system 606, and a braking system 608. In some embodiments, the vehicle 600 is related to the vehicle 502 (see Figure 5)The same or similar. In some embodiments, vehicle 600 has autonomous capabilities (e.g., implementing at least one of the following functions, features, and / or devices, etc., where the at least one function, feature, and / or device enables vehicle 600 to operate partially or fully without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that dispense with reliance on human intervention) and / or highly autonomous vehicles (e.g., vehicles that dispense with reliance on human intervention in certain situations), etc.). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference can be made to SAE International standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire content of which is incorporated by reference. In some embodiments, vehicle 600 is associated with an autonomous queue manager and / or a ride-sharing company.

[0090] Autonomous system 602 includes a sensor suite that includes one or more devices such as camera 602a, LiDAR sensor 602b, radar sensor 602c, and microphone 602d. In some embodiments, autonomous system 602 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance traveled by vehicle 600, etc.). In some embodiments, autonomous system 602 uses one or more devices included in autonomous system 602 to generate data associated with environment 500 described herein. The data generated by one or more devices of autonomous system 602 can be used by one or more systems described herein to observe the environment (e.g., environment 500) in which vehicle 600 is located. In some embodiments, autonomous system 602 includes communication device 602e, autonomous vehicle computing 602f, and safety controller 602g.

[0091] Camera 602a includes being configured to communicate with communication device 602e, autonomous vehicle computing 602f, and / or safety controller 602g via a bus (e.g., with Figure 7at least one device that communicates over a bus 702 that is the same as or similar to the bus). The camera 602a includes at least one camera (e.g., a digital camera using an optical sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing an image including a physical object (e.g., a car, a bus, a curb, and / or a person, etc.). In some embodiments, the camera 602a generates camera data as output. In some examples, the camera 602a generates camera data including image data associated with the image. In this example, the image data may specify at least one parameter corresponding to the image (e.g., image characteristics such as exposure, brightness, etc., and / or an image timestamp, etc.). In such an example, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 602a includes a plurality of independent cameras configured (e.g., positioned) on a vehicle to capture images for the purpose of stereovision (stereo vision). In some examples, the camera 602a includes a plurality of cameras that generate image data and transmit the image data to the autonomous vehicle computing 602f and / or a queue management system (e.g., a queue management system that is the same as or similar to the Figure 5 queue management system 516). In such an example, the autonomous vehicle computing 602f determines the depth to one or more objects in the field of view of at least two of the plurality of cameras based on the image data from at least two cameras. In some embodiments, the camera 602a is configured to capture an image of an object within a distance (e.g., up to 100 meters and / or up to 1 kilometer, etc.) relative to the camera 602a. Thus, the camera 602a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to the camera 602a.

[0092] In an embodiment, the camera 602a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects that provide visual navigation information. In some embodiments, the camera 602a generates traffic light detection (TLD) data (or traffic light data) associated with one or more images. In some examples, the camera 602a generates TLD data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 602a that generates TLD data is different from other camera-containing systems described herein in that the camera 602a may include one or more cameras having a wide field of view (e.g., a wide-angle lens, a fish-eye lens, and / or a lens having a viewing angle of about 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0093] The Light Detection and Ranging (LiDAR) sensor 602b includes at least one device configured to communicate with the communication device 602e, the autonomous vehicle computing 602f, and / or the safety controller 602g via a bus (e.g., a bus identical or similar to the Figure 7 bus 702). The LiDAR sensor 602b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 602b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 602b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 602b. In some embodiments, the light emitted by the LiDAR sensor 602b does not penetrate the physical object it encounters. The LiDAR sensor 602b further includes at least one light detector that detects the light after the light emitted from the light emitter encounters a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 602b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing the objects included in the field of view of the LiDAR sensor 602b. In some examples, at least one data processing system associated with the LiDAR sensor 602b generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundary of the physical object in the field of view of the LiDAR sensor 602b.

[0094] The Radio Detection and Ranging (RADAR) sensor 602c includes at least one device configured to communicate with the communication device 602e, the autonomous vehicle computing 602f, and / or the safety controller 602g via a bus (e.g., a bus identical or similar to the Figure 7at least one device that communicates via a bus (e.g., a bus identical or similar to bus 702). The radar sensor 602c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the radar sensor 602c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves emitted by the radar sensor 602c encounter physical objects and are reflected back to the radar sensor 602c. In some embodiments, the radio waves emitted by the radar sensor 602c are not reflected by some objects. In some embodiments, at least one data processing system associated with the radar sensor 602c generates a signal representing the objects included in the field of view of the radar sensor 602c. For example, at least one data processing system associated with the radar sensor 602c generates an image representing the boundaries of the physical objects and / or the surface of the physical objects (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundaries of the physical objects in the field of view of the radar sensor 602c.

[0095] The microphone 602d includes at least one device configured to communicate with the communication device 602e, the autonomous vehicle computing 602f, and / or the safety controller 602g via a bus (e.g., a bus identical or similar to Figure 7 bus 702). The microphone 602d includes one or more microphones (e.g., an array microphone and / or an external microphone, etc.) that capture an audio signal and generate data associated with (e.g., representing) the audio signal. In some examples, the microphone 602d includes a transducer device and / or a similar device. In some embodiments, one or more of the systems described herein may receive the data generated by the microphone 602d and determine the position (e.g., distance, etc.) of the object relative to the vehicle 600 based on the audio signal associated with the data.

[0096] The communication device 602e includes at least one device configured to communicate with the camera 602a, the LiDAR sensor 602b, the radar sensor 602c, the microphone 602d, the autonomous vehicle computing 602f, the safety controller 602g, and / or the drive-by-wire (DBW) system 602h. For example, the communication device 602e may include a device identical or similar to Figure 7 the communication interface 714. In some embodiments, the communication device 602e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0097] The autonomous vehicle computing 602f includes at least one device configured to communicate with the camera 602a, LiDAR sensor 602b, radar sensor 602c, microphone 602d, communication device 602e, safety controller 602g, and / or DBW system 602h. In some examples, the autonomous vehicle computing 602f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablets, etc.), and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units, etc.). In some embodiments, the autonomous vehicle computing 602f is the same as or similar to the autonomous vehicle computing 800 described herein. Additionally or alternatively, in some embodiments, the autonomous vehicle computing 602f is configured to communicate with an autonomous vehicle system (e.g., an autonomous vehicle system the same as or similar to the remote AV system 514 of Figure 5 ), a queue management system (e.g., a queue management system the same as or similar to the queue management system 516 of Figure 5 ), a V2I device (e.g., a V2I device the same as or similar to the V2I device 510 of Figure 5 ), and / or a V2I system (e.g., a V2I system the same as or similar to the V2I system 518 of Figure 5 ).

[0098] The safety controller 602g includes at least one device configured to communicate with the camera 602a, LiDAR sensor 602b, radar sensor 602c, microphone 602d, communication device 602e, autonomous vehicle computing 602f, and / or DBW system 602h. In some examples, the safety controller 602g includes one or more controllers (such as electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 600 (e.g., the powertrain control system 604, steering control system 606, and / or braking system 608, etc.). In some embodiments, the safety controller 602g is configured to generate control signals that take precedence over (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing 602f.

[0099] The DBW system 602h includes at least one device configured to communicate with the communication device 602e and / or the autonomous vehicle computing 602f. In some examples, the DBW system 602h includes one or more controllers (e.g., an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 600 (e.g., the powertrain control system 604, the steering control system 606, and / or the braking system 608, etc.). Additionally or alternatively, one or more controllers of the DBW system 602h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 600 (e.g., turn signals, headlights, door locks, and / or windshield wipers, etc.).

[0100] The powertrain control system 604 includes at least one device configured to communicate with the DBW system 602h. In some examples, the powertrain control system 604 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 604 receives a control signal from the DBW system 602h, and the powertrain control system 604 causes the vehicle 600 to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a certain direction, decelerate in a certain direction, turn left, and / or turn right, etc. In an example, the powertrain control system 604 increases, maintains the same, or decreases the energy (e.g., fuel and / or electricity, etc.) provided to the motor of the vehicle, causing at least one wheel of the vehicle 600 to rotate or not rotate.

[0101] The steering control system 606 includes at least one device configured to rotate one or more wheels of the vehicle 600. In some examples, the steering control system 606 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 606 causes two front wheels and / or two rear wheels of the vehicle 600 to rotate left or right, causing the vehicle 600 to turn left or right.

[0102] The braking system 608 includes at least one device configured to actuate one or more brakes to decelerate the vehicle 600 and / or keep it stationary. In some examples, the braking system 608 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 600 on the corresponding rotors of the vehicle 600. Additionally or alternatively, in some examples, the braking system 608 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0103] In some embodiments, vehicle 600 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring properties of the state or condition of vehicle 600. In some examples, vehicle 600 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel rate sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor.

[0104] Now refer to Figure 7 , a schematic diagram of illustrative device 700. As illustrated, device 700 includes a processor 704, a memory 706, a storage device 708, an input interface 710, an output interface 712, a communication interface 714, and a bus 702. In some embodiments, device 700 corresponds to: at least one device of vehicle 502 (e.g., at least one device of a system of vehicle 502); and / or one or more than one device of network 512 (e.g., one or more than one device of a system of network 512). In some embodiments, one or more than one device of vehicle 502 (e.g., at least one device of a system of vehicle 502), and / or one or more than one device of network 512 (e.g., one or more than one device of a system of network 512) includes at least one device 700 and / or at least one component of device 700. As Figure 7 shown, device 700 includes a bus 702, a processor 704, a memory 706, a storage device 708, an input interface 710, an output interface 712, and a communication interface 714.

[0105] Bus 702 includes components that permit communication among the components of device 700. In some embodiments, processor 704 is implemented in hardware, software, or a combination of hardware and software. In some examples, processor 704 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). Memory 706 includes a random access memory (RAM), a read only memory (ROM), and / or another type of dynamic and / or static storage device that stores data and / or instructions for use by processor 304 (e.g., flash memory, magnetic memory, and / or optical memory, etc.).

[0106] The storage device 708 stores data and / or software related to the operation and use of the device 700. In some examples, the storage device 708 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette tape, a magnetic tape, a CD-ROM, a RAM, a PROM, an EPROM, a FLASH-EPROM, an NV-RAM, and / or another type of computer-readable medium, and corresponding drives.

[0107] The input interface 710 includes components that permit the device 700 to receive information such as via a user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, the input interface 710 includes sensors for sensing information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, and / or an actuator, etc.). The output interface 712 includes components for providing output information from the device 700 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0108] In some embodiments, the communication interface 714 includes transceiver-like components that permit the device 700 to communicate with other devices via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection (e.g., a transceiver and / or separate receivers and transmitters, etc.). In some examples, the communication interface 714 permits the device 700 to receive information from and / or provide information to another device. In some examples, the communication interface 714 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, an interface, and / or a cellular network interface, etc.

[0109] In some embodiments, the device 700 performs one or more of the processes described herein. The device 700 performs these processes based on software instructions executed by a processor 704 that are stored in a computer-readable medium such as the memory 706 and / or the storage device 708. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes a storage space located within a single physical storage device or a storage space distributed across multiple physical storage devices.

[0110] In some embodiments, software instructions are read into memory 706 and / or storage device 708 from another computer-readable medium or from another device via communication interface 714. When the software instructions stored in memory 706 and / or storage device 708 are executed, they cause processor 704 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein. Thus, unless otherwise explicitly stated, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0111] Memory 706 and / or storage device 708 includes a data store or at least one data structure (e.g., a database, etc.). Device 700 is capable of receiving information from the data store or at least one data structure in memory 706 or storage device 708, storing information in the data store or at least one data structure, communicating information to the data store or at least one data structure, or searching for information stored in the data store or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0112] In some embodiments, device 700 is configured to execute software instructions stored in memory 706 and / or the memory of another device (e.g., another device that is the same as or similar to device 700). As used herein, the term "module" refers to at least one instruction stored in memory 706 and / or the memory of another device, which when executed by processor 704 and / or the processor of another device (e.g., another device that is the same as or similar to device 700), causes device 700 (e.g., at least one component of device 700) to perform one or more processes described herein. In some embodiments, the module is implemented in software, firmware, and / or hardware, etc.

[0113] Provided Figure 7 The number and arrangement of the illustrated components are by way of example. In some embodiments, compared to Figure 7 the illustrated components, device 700 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components of device 700 (e.g., one or more components) may perform one or more functions described as being performed by another component or another set of components of device 700.

[0114] Now refer to Figure 8, an example block diagram of an autonomous vehicle computing 800 (sometimes referred to as an "AV stack") is illustrated. As illustrated, the autonomous vehicle computing 800 includes a perception system 802 (sometimes referred to as a perception module), a planning system 804 (sometimes referred to as a planning module), a positioning system 806 (sometimes referred to as a positioning module), a control system 808 (sometimes referred to as a control module), and a database 810. In some embodiments, the perception system 802, the planning system 804, the positioning system 806, the control system 808, and the database 810 are included in and / or implemented in the vehicle's automatic navigation system (e.g., the autonomous vehicle computing 602f of the vehicle 600). Additionally or alternatively, in some embodiments, the perception system 802, the planning system 804, the positioning system 806, the control system 808, and the database 810 are included in one or more independent systems (e.g., one or more systems the same as or similar to the autonomous vehicle computing 800, etc.). In some examples, the perception system 802, the planning system 804, the positioning system 806, the control system 808, and the database 810 are included in one or more independent systems located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 800 are implemented in software (e.g., software instructions stored in memory), computer hardware (e.g., via a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), and / or a field-programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that, in some embodiments, the autonomous vehicle computing 800 is configured to communicate with remote systems (e.g., an autonomous vehicle system the same as or similar to the remote AV system 514, a queue management system 516 the same as or similar to the queue management system 516, and / or a V2I system 518 the same as or similar to the V2I system, etc.).

[0115] In some embodiments, the perception system 802 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 802 to detect the at least one physical object), and classifies the at least one physical object. In some examples, the perception system 802 receives image data captured by at least one camera (e.g., camera 602a), the image being associated with one or more physical objects within the field of view of the at least one camera (e.g., representing the one or more physical objects). In such examples, the perception system 802 classifies the at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of the physical objects by the perception system 802, the perception system 802 transmits data associated with the classification of the physical objects to the planning system 804.

[0116] In some embodiments, the planning system 804 receives data associated with a destination, and generates data associated with at least one route (e.g., route 506) along which a vehicle (e.g., vehicle 502) can travel toward the destination. In some embodiments, the planning system 804 periodically or continuously receives data from the perception system 802 (e.g., the data associated with the classification of the physical objects described above), and the planning system 804 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 802. In some embodiments, the planning system 804 receives data associated with an updated position of a vehicle (e.g., vehicle 502) from the positioning system 806, and the planning system 804 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 806.

[0117] In some embodiments, the positioning system 806 receives data associated with (e.g., representing) the location of a vehicle (e.g., vehicle 502) in an area. In some examples, the positioning system 806 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 602b). In certain examples, the positioning system 806 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 806 generates a combined point cloud based on the respective point clouds. In these examples, the positioning system 806 compares the at least one point cloud or the combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 810. Then, based on the positioning system 806 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 806 determines the position of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated prior to the navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of the roadway geometry, a map describing the connectivity of the road network, a map describing the physical properties of the roadways (such as traffic speed, traffic flow, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction, or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various other types of driving signal lights, etc.). In some embodiments, the map is generated in real time based on the data received by the perception system.

[0118] In another example, the positioning system 806 receives Global Navigation Satellite System (GNSS) data generated by a Global Positioning System (GPS) receiver. In some examples, the positioning system 806 receives GNSS data associated with the location of a vehicle in an area, and the positioning system 806 determines the latitude and longitude of the vehicle in the area. In such examples, the positioning system 806 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, the positioning system 806 generates data associated with the position of the vehicle. In some examples, based on the positioning system 806 determining the position of the vehicle, the positioning system 806 generates data associated with the position of the vehicle. In such examples, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[0119] In some embodiments, the control system 808 receives data associated with at least one trajectory from the planning system 804, and the control system 808 controls the operation of the vehicle. In some examples, the control system 808 receives data associated with at least one trajectory from the planning system 804, and the control system 808 controls the operation of the vehicle by generating and transmitting control signals to cause the powertrain control system (e.g., the DBW system 602h and / or the powertrain control system 604, etc.), the steering control system (e.g., the steering control system 606), and / or the braking system (e.g., the braking system 608) to operate. In an example, in the case where the trajectory includes a left turn, the control system 808 transmits a control signal to cause the steering control system 806 to adjust the steering angle of the vehicle 600, so that the vehicle 600 turns left. Additionally or alternatively, the control system 808 generates and transmits control signals to cause other devices of the vehicle 600 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change states.

[0120] In some embodiments, the perception system 802, the planning system 804, the positioning system 806, and / or the control system 808 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, the perception system 802, the planning system 804, the positioning system 806, and / or the control system 808 implement at least one machine learning model individually or in combination with one or more of the above systems. In some examples, the perception system 802, the planning system 804, the positioning system 806, and / or the control system 808 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in the environment, etc.).

[0121] The database 810 stores data transmitted to, received from, and / or updated by the perception system 802, the planning system 804, the positioning system 806, and / or the control system 808. In some examples, the database 810 includes a storage device for storing data and / or software related to operations and using at least one system of the autonomous vehicle computing 800 (e.g., related to Figure 7the same or similar storage device as storage device 708). In some embodiments, database 810 stores data associated with 2D and / or 3D maps of at least one area. In some examples, database 810 stores data associated with 2D and / or 3D maps of a part of a city, multiple parts of multiple cities, multiple cities, counties, states, and / or countries (e.g., nations), etc. In such examples, a vehicle (e.g., a vehicle the same or similar to vehicle 502 and / or vehicle 600) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, back roads, and / or off-road paths, etc.), and cause at least one LiDAR sensor (e.g., a LiDAR sensor the same or similar to LiDAR sensor 602b) to generate data associated with an image representing the objects included in the field of view of the at least one LiDAR sensor.

[0122] In some embodiments, database 810 can be implemented across multiple devices. In some examples, database 810 is included in a vehicle (e.g., a vehicle the same or similar to vehicle 502 and / or vehicle 600), an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to remote AV system 514), a queue management system (e.g., a queue management system the same or similar to Figure 5 queue management system 516), and / or a V2I system (e.g., a V2I system the same or similar to Figure 5 V2I system 518), etc.

[0123] Figure 9 FIG. shows a block diagram of an architecture 900 for managing the behavior of traffic lights according to one or more embodiments. In an embodiment, architecture 900 is implemented in an autonomous system of a vehicle. In some examples, the vehicle is Figure 6 the embodiment of vehicle 600 shown in, and architecture 900 is implemented by autonomous system 602 of vehicle 600. Architecture 900 is configured to manage the traffic light behavior at a region of interest (e.g., an intersection) in a consistent and robust manner by using a logical traffic light to represent a physical traffic light at the region of interest, for reliable and efficient decision-making.

[0124] Architecture 900 includes a perception system 910 (e.g., which can be the Figure 8 perception system 802 shown in in some embodiments) and a planning system 920 (e.g., which can be the Figure 8The planning system shown in 804). The perception system 910 selectively obtains area information of at least one area of interest (e.g., intersection) from the mapping database 906 based on, for example, the current location of the vehicle and / or the route of the vehicle. The mapping database 906 stores a data structure that associates each area of interest with a logical traffic light grouping of the physical traffic lights at the area of interest and the corresponding states determined by the state combinations of the logical traffic light groupings. Based on the area information and the route of the vehicle, the perception system 910 determines traffic light information 915, such as the state of the area of interest or the state of the logical traffic lights of the incoming road segments at the area of interest. The perception system 910 provides the traffic light information 915 to the planning system 920 to determine the actions to be taken by the vehicle when it arrives at the area of interest. For example, the actions to be taken can be to stop, decelerate, continue at the current speed, and other appropriate actions, etc. The planning system 920 determines the action based on the traffic light information 915 and other data (e.g., data from Figure 8 the positioning system 806 and the database 810). The vehicle is operated by a control system (e.g., Figure 8 the control system 808 shown) according to the determined action.

[0125] In one embodiment, the architecture 900 includes a mapping database 906 implemented, for example, in the Figure 8 database 810 shown. In another embodiment, the mapping database 906 is external to the architecture 900 and is stored in a server such as, for example, Figure 5 the remote AV system 514 shown. The mapping database 906 includes road network information, such as a high-precision map of the geometry of the road lanes, a map describing the connection properties of the road network, a map describing the physical properties of the road lanes (such as traffic speed, traffic flow, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction, or lane marking type and location, or a combination thereof, etc.), and a map describing the spatial location of areas of interest (such as intersections, crosswalks, traffic signs, or various other driving signals, etc.). In an embodiment, the high-precision map is constructed by adding data to a low-precision map through automatic or manual annotation. For illustrative purposes only, intersections are described herein as examples of areas of interest.

[0126] The mapping database 906 includes area information of intersections in the map. As described in further detail below, in one embodiment, the area information of an intersection includes an intersection identifier (ID), a series of states of the intersection representing the behavior of the traffic lights at the intersection, information related to the road segments at the intersection, and information related to the logical traffic lights of the road segments. In one embodiment, the mapping database 906 also stores information related to the physical traffic lights at the intersection.

[0127] The perception system 910 includes a map information extractor 912. The map information extractor 912 extracts area information for one or more regions of interest for the vehicle. The area information of an intersection includes an intersection identifier (ID), a series of states of the intersection representing the traffic light behavior at the intersection, information about the road segments in the intersection, and information about the logical traffic lights of the road segments. The area information can be stored in the data structure as described above.

[0128] In one example, based on the current location of the vehicle, the map information extractor 912 extracts area information for one or more intersections around the current location of the vehicle. In one example, based on the current route of the vehicle, the map information extractor 912 extracts area information for one or more intersections along the current route of the vehicle.

[0129] In one embodiment, the perception system 910 includes a traffic light information (TLI) generator 914. The TLI generator 914 is configured to use the area information of one or more intersections and the current route of the vehicle to generate traffic light information associated with the current route of the vehicle. For example, if the vehicle is approaching an intersection (e.g., Figure 1A intersection 100 as shown) at an incoming road segment (e.g., Figure 1A road segment 110 as shown), then the TLI generator 914 can determine, for example, based on a simulation of the traffic light behavior of the intersection and / or one or more trigger events, the driving speed of the vehicle, the current location of the vehicle, and / or the distance between the vehicle and the center of the intersection, what state the current state of the intersection is and how much time remains for the current state.

[0130] In an embodiment, the TLI generator 914 determines, for example, based on a simulation of the behavior of the logical traffic light of the incoming road segment and / or one or more trigger events, the driving speed of the vehicle, the current location of the vehicle, and / or the distance between the vehicle and the center of the intersection, what state the current state of the incoming road segment is and how much time remains for the current state. The TLI generator 914 can filter out other road segments at the intersection and use the traffic light data of the incoming road segment for simulation and / or determination.

[0131] In one embodiment, as Figure 9 shown, the architecture 900 includes a traffic light detection (TLD) system 902 for sensing or measuring the nature of the environment of the vehicle (e.g., Figure 6The camera 602a) shown. The TLD system 902 uses one or more cameras to obtain information related to traffic lights, street signs, and other objects that provide visual navigation information. The TLD system 902 generates TLD data 904. The TLD data can be in the form of image data (e.g., data in image data formats such as RAW, JPEG, PNG, etc.). The TLD system 902 uses a camera with a wide field of view (e.g., using a wide-angle lens or a fish-eye lens) to obtain information related to as many physical objects that provide visual navigation information as possible, so that the vehicle can access all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system is approximately 120 degrees or greater.

[0132] In some embodiments, the perception system 910 includes a traffic light information (TLI) generator 914 that receives TLD data 904 from the TLD system 902. The TLI generator 914 can update the traffic light information 915 based on the TLD data 904. In some examples, the TLI generator 914 analyzes the TLD data 904 to determine the actual information of the physical traffic lights associated with the road block (e.g., Figure 1A the seven physical traffic lights of the road block 110) to check / calibrate the traffic light information 915 (e.g., Figure 1B the current state of the logical traffic light 152). If the traffic light information 915 does not match the actual information of the physical traffic lights, the TLI generator 914 updates the traffic light information based on the TLD data 904, for example, by updating the status of the logical traffic lights of the road block and the status of the intersection.

[0133] The perception system 910 provides the traffic light information 915 to the planning system 920. In some embodiments, the planning system 920 updates the route based on the traffic light information 915 and provides the planned route 925 to the perception system 910 (e.g., the map information extractor 912). The perception system 910 can update the area information of one or more intersections obtained from the mapping database 906 based on the planned route from the planning system 920.

[0134] Based on the traffic light information 915, the planning system 920 determines the actions that the vehicle should take when arriving at the intersection, such as stopping, decelerating, or continuing at the current speed, etc. The planning system 920 determines the actions based on the traffic light information 915 and other data (e.g., data from Figure 8 the positioning system 806 and the database 810). The vehicle is operated by a control system (e.g., Figure 8 the control system 808 shown) according to the actions.

[0135] Figure 10A flowchart of process 1000 is shown for managing the traffic light behavior of a vehicle, particularly for managing traffic light behavior using information of logical traffic lights of road segments at a region of interest. In some embodiments, process 1000 is performed (e.g., fully and / or partially) by an autonomous system (e.g., vehicle 600 as shown in Figure 6 ). Additionally or alternatively, in some embodiments, process 1000 is performed (e.g., fully and / or partially) by other devices or groups of devices separate from the autonomous system (e.g., remote AV system 514 as shown in Figure 5 ).

[0136] In some embodiments, the autonomous system includes a perception system (e.g., perception system 802 as shown in Figure 8 or perception system 510 as shown in Figure 5 ), a planning system (e.g., planning system 804 as shown in Figure 8 or planning system 520 as shown in Figure 5 ), and a control system (e.g., control system 808 as shown in Figure 8 ).

[0137] Referring to process 1000, the autonomous system obtains region information of at least one region of interest of the vehicle (1002). The at least one region of interest includes two or more road segments. The region information includes information related to logical traffic lights associated with the road segments in the at least one region of interest.

[0138] In some examples, a road segment (e.g., road segment 110 as shown in Figure 1A ) is associated with a first road (e.g., road 114 as shown in Figure 1A ) having a first road direction (e.g., road direction 113 as shown in Figure 1A ) and a second road (e.g., road 116 as shown in Figure 1A ) having a second road direction different from the first road direction (e.g., road direction 115 as shown in Figure 1A ).

[0139] Each road segment is associated with a corresponding logical traffic light representing the aggregation of one or more corresponding physical traffic lights that control the movement of the vehicle at the road segment. In one example, as shown in Figure 1A , road segment 110 is associated with seven physical traffic lights 112a, 112b, 112c, 132a, 132b, 132c, 132e that regulate the vehicles entering intersection 100 from road segment 110. One or more physical traffic lights are represented by a corresponding logical traffic light (e.g., logical traffic light 152 as shown in Figure 1B ).

[0140] In some embodiments, the vehicle is in motion. The autonomous system also determines that the vehicle is approaching a region of interest in the route that the vehicle traverses, and obtains region information of at least one region of interest in response to determining that the vehicle is approaching a region of interest in the route that the vehicle traverses. In some examples, the at least one region of interest includes a plurality of regions of interest (including the approaching region of interest in the route that the vehicle traverses) and one or more other regions of interest adjacent to the approaching region of interest or in the route that the vehicle traverses.

[0141] The autonomous system obtains region information of at least one region of interest from a mapping database (e.g., Figure 9 the mapping database 906). In some embodiments, the autonomous system filters the plurality of regions of interest in the mapping database to determine at least one region of interest of the vehicle based on the current location of the vehicle or the route of the vehicle. As an example, the at least one region of interest is adjacent to the current location and / or on the incoming route towards the destination.

[0142] In some embodiments, the autonomous system queries the mapping database to obtain a filtered list of logical traffic lights of at least one region of interest based on the current location of the vehicle or the route of the vehicle. Each filtered logical traffic light has a true value of a boolean field.

[0143] In some embodiments, information of each logical traffic light is obtained based on information of a plurality of respective physical traffic lights each configured to control (e.g., regulate or manage) traffic (e.g., vehicles or pedestrians coming from or going to) of a corresponding road segment associated with the logical traffic light. In some examples, each logical traffic light includes a plurality of logical bulbs (e.g., red, yellow, green), and information of each logical traffic light includes information of each logical bulb among the plurality of logical bulbs, and the information includes at least one of shape (e.g., circular, right arrow, left arrow, up arrow, down arrow, unknown), color (e.g., red, yellow, green, unknown), and status (e.g., lit, extinguished, flashing, unknown). In some examples, each logical bulb among the plurality of logical bulbs corresponds to a corresponding physical bulb having the same shape, the same color, and the same status among the plurality of respective physical traffic lights.

[0144] In some embodiments, the area information of at least one area of interest includes at least one of an identifier of the area of interest, identifiers of respective road blocks in the area of interest, and a list of logical traffic lights. The area information does not include a list of physical traffic lights. In the area information, the identifier of at least one area of interest is associated with multiple states (e.g., a finite number of states). Each state among the multiple states is associated with: a respective duration of the state (e.g., 20 seconds, 10 seconds, or 5 seconds), identifiers of multiple road blocks (e.g., an entry road block, an exit road block, and / or adjacent road blocks), and information related to the logical traffic lights associated with each of the multiple road blocks in the state.

[0145] In some embodiments, the autonomous system uses a finite state machine (FSM) defined by multiple states to determine the state of the area of interest. The autonomous system determines that at least one area of interest is in a specific state among the multiple states at a specific moment, and transitions the area of interest from a first state to a second state in the state cycle formed by the multiple states when the duration of the first state ends. The FSM maintains the area of interest in a consistent state. Thus, the autonomous system transitions the logical traffic lights associated with the area of interest to a state corresponding to the second state of the area of interest while the area of interest transitions from the first state to the second state.

[0146] In some embodiments, in the area information, the identifier of at least one area of interest is associated with one or more trigger events. The autonomous system transitions at least one area of interest to a respective specific state in response to the occurrence of each of the one or more trigger events. In some examples, the trigger event is associated with the distance between the vehicle and the area of interest. In some examples, the trigger event is associated with the expiration of a time period.

[0147] Continuing to refer to process 1000, the autonomous system uses the area information of at least one area of interest to determine traffic light information associated with the route of the vehicle (1004). The route includes at least one road block of at least one area of interest.

[0148] In some examples, the traffic light information includes at least one of the following: the current state of at least one area of interest including at least one road block in the route and the remaining time of the current state of at least one area of interest, or the current state of the logical traffic lights associated with at least one road block in at least one area of interest included in the route, and the remaining time of the current state of the logical traffic lights.

[0149] In some embodiments, the autonomous system determines the traffic light information associated with the route of the vehicle by simulating the area information of at least one area of interest at the current time point, e.g., based on historical data or other real-time data.

[0150] In some embodiments, the autonomous system obtains traffic light detection (TLD) data from a traffic light detection system of a vehicle (e.g., camera 602a as shown Figure 6 ). The autonomous system analyzes the TLD data to determine information about physical traffic lights associated with a road segment and to check or calibrate information about logical traffic lights corresponding to the physical traffic lights or the status of regions of interest. The autonomous system updates traffic light information based on the TLD data.

[0151] In some embodiments, the autonomous system provides data associated with a graphical interface for visualization, e.g., to a visualizer, based on at least one of region information or traffic light information. The graphical interface can be an interface for a map. The graphical interface can display logical traffic lights with associated current states, e.g., as shown Figures 2A to 2C .

[0152] Continuing to refer to process 1000, the autonomous system uses the traffic light information to operate the vehicle (1006) along a route. Based on the traffic light information, the autonomous system determines an action for the vehicle to take when the vehicle reaches an intersection, such as stop, slow down, or continue at the current rate, etc. The autonomous system determines the action based on the traffic light information and other data (e.g., data from Figure 8 the positioning system 806 and database 810). The vehicle is operated by a control system (e.g., control system 808 as shown Figure 8 ) according to the action.

[0153] In some embodiments, the autonomous system updates a route based on the traffic light information and updates region information for one or more intersections obtained from a mapping database based on the planned route.

[0154] In the foregoing description, aspects and embodiments of the invention have been described with reference to numerous specific details, which may vary depending on implementation. Accordingly, the specification and drawings are to be regarded as illustrative rather than in a limiting sense. The sole and exclusive indication of the scope of the invention, and what the applicant desires to be the scope of the invention, is the literal and equivalent scope of the claims issued from this application in the specific form of the issued claims, including any subsequent amendments. Any definitions expressly set forth herein for terms to be recited in such claims shall govern the meaning of such terms as used in the claims. Additionally, when the term "further comprises" is used in the foregoing specification or the appended claims, the text following that phrase can be additional steps or entities, or sub-steps / sub-entities of the previously recited steps or entities.

Claims

1. A method for a vehicle, comprising: using at least one processor to obtain information corresponding to a region of interest including two or more road segments, wherein each road segment is associated with a plurality of physical traffic lights configured to control traffic movement associated with that road segment; for each of the two or more road segments, using the at least one processor to generate a logical traffic light representing a grouping of the plurality of physical traffic lights; and using the at least one processor to determine one or more characteristics of each logical traffic light based on the information corresponding to the region of interest.

2. The method according to claim 1, further comprising: obtaining a current state of at least one of the plurality of physical traffic lights; and determining a current state of the remaining physical traffic lights of the plurality of physical traffic lights associated with the logical traffic light based on the obtained current state of the at least one physical traffic light.

3. The method according to claim 1 or 2, wherein One or more characteristics of the logical traffic light include at least one of the behavior of the logical traffic light, the number of states, and the duration of each state, and the behavior of the logical traffic light includes an interaction with one or more other logical traffic lights at the region of interest.

4. The method according to claim 1 or 2, wherein The logical traffic light includes a plurality of logical bulbs, and wherein one or more characteristics of the logical traffic light include one or more characteristics of each logical bulb of the plurality of logical bulbs, and one or more characteristics of each logical bulb include at least one of shape, color, and state.

5. The method according to claim 4, wherein, Each logical bulb of the plurality of logical bulbs corresponds to a corresponding physical bulb of the plurality of physical traffic lights having the same shape, the same color, and the same state.

6. The method according to claim 1 or 2, wherein The information corresponding to the region of interest includes information of physical traffic lights in the region of interest, and the information of the physical traffic lights includes at least one of location, orientation, type, shape, color, and state.

7. The method according to claim 1 or 2, further comprising: storing information related to the logical traffic lights associated with two or more road segments at the region of interest in a database, the information including one or more characteristics of the determined logical traffic lights.

8. The method according to claim 1 or 2, further comprising: generating an identifier for the region of interest, the identifier being associated with a plurality of states, wherein each of the plurality of states is associated with: a corresponding duration of the state; identifiers of two or more road segments in the region of interest; and information of the logical traffic lights associated with each of the two or more road segments in the state.

9. The method according to claim 8, further comprising: determining that the region of interest is in a specific state of the plurality of states at a specific moment.

10. The method according to claim 8, further comprising: when the known duration of the first state ends, transitioning the region of interest from the first state to a second state in a state cycle formed by the plurality of states.

11. The method according to claim 10, further comprising: In connection with transitioning the area of interest from the first state to the second state, a logical traffic light associated with the area of interest is transitioned to a state corresponding to the second state of the area of interest.

12. The method according to claim 8, further comprising: associating an identifier of the area of interest with one or more trigger events; and in response to the occurrence of a trigger event among the one or more trigger events, transitioning the area of interest to a corresponding specific state.

13. The method according to claim 8, further comprising: using the at least one processor to generate a representation of a vehicle; using the at least one processor to configure the vehicle to approach and pass through the area of interest; and using the at least one processor and based on a change in the state of a logical traffic light at the area of interest to determine the behavior of the vehicle when the vehicle is approaching or passing through the area of interest.

14. The method according to claim 13, further comprising: using the at least one processor to adjust a corresponding duration of at least one of the plurality of states based on a result of determining the behavior of the vehicle.

15. The method according to claim 8, further comprising: using the at least one processor to change one or more characteristics of at least one logical traffic light associated with the area of interest based on a change in the state of the area of interest; and using the at least one processor to change one or more properties of at least one physical traffic light corresponding to the at least one logical traffic light based on a change in the one or more characteristics of the at least one logical traffic light.

16. The method according to claim 15, wherein, Changing one or more properties of at least one physical traffic light corresponding to the at least one logical traffic light includes at least one of the following: changing a duration of each state of the at least one physical traffic light; changing a location of the at least one physical traffic light; and changing a number of the at least one physical traffic light.

17. The method according to claim 1 or 2, further comprising: providing data associated with the logical traffic light including one or more characteristics of the logical traffic light for visualization using a graphical interface.

18. A method for a vehicle, comprising: using at least one processor to obtain area information of at least one area of interest of a vehicle, wherein the at least one area of interest includes two or more road blocks, and each road block is associated with a corresponding logical traffic light representing an aggregation of a plurality of corresponding physical traffic lights controlling the movement of the vehicle at the road block, and wherein the area information includes information related to the logical traffic lights associated with the road blocks in the at least one area of interest, and wherein the information related to each of the logical traffic lights is obtained based on information of the plurality of corresponding physical traffic lights each configured to control traffic at the corresponding road block associated with the logical traffic light; Using the at least one processor, determining traffic light information associated with a route of the vehicle using region information of the at least one region of interest, the route including at least one road segment of the at least one region of interest; and Using the at least one processor, operating the vehicle along the route using the traffic light information.

19. The method according to claim 18, wherein, The traffic light information includes at least one of the following:[[]]END]] A current state of at least one region of interest including the at least one road segment in the route and a remaining time of the current state of the at least one region of interest; and A current state of a logical traffic light associated with at least one road segment in at least one region of interest included in the route and a remaining time of the current state of the logical traffic light.

20. The method according to claim 18 or 19, wherein The vehicle is in motion, and the method further includes:[[]]END]] Using the at least one processor to determine that the vehicle is approaching a region of interest in a route traversed by the vehicle,[[]]END]] wherein obtaining the region information of the at least one region of interest is in response to determining that the vehicle is approaching a region of interest in a route traversed by the vehicle, and wherein the at least one region of interest includes a plurality of regions of interest and one or more other regions of interest, the plurality of regions of interest including the approaching region of interest in the route traversed by the vehicle, and the one or more other regions of interest being adjacent to the approaching region of interest or in the route traversed by the vehicle.

21. The method according to claim 18 or 19, further comprising:[[]]END]] Obtaining traffic light detection data, i.e., TLD data, from a traffic light detection system of the vehicle; and Updating the traffic light information based on the TLD data.

22. The method according to claim 19, wherein, Each logical traffic light includes a plurality of logical bulbs, wherein information corresponding to each logical traffic light includes information of each of the plurality of logical bulbs, the information of each logical bulb including at least one of shape, color, and state, and wherein each of the plurality of logical bulbs corresponds to a corresponding physical bulb having the same shape, the same color, and the same state among the plurality of corresponding physical traffic lights.

23. The method according to claim 18 or 19, wherein The region information of the at least one region of interest includes at least one of an identifier of the region of interest, identifiers of each road segment in the region of interest, and a list of logical traffic lights, wherein in the region information, the identifier of the at least one region of interest is associated with a plurality of states, and wherein each of the plurality of states is associated with a corresponding duration of the state, identifiers of a plurality of road segments, and information related to a logical traffic light associated with each of the plurality of road segments in the state.

24. The method according to claim 23, further comprising:[[]]END]] Determining that the at least one region of interest is in a specific state among the plurality of states at a specific moment, wherein the at least one region of interest is configured to transition from the first state to the second state in a state cycle formed by the plurality of states at the end of a duration of the first state; and Convert the logical traffic light associated with the region of interest to a state corresponding to a second state of the region of interest while transitioning the region of interest from the first state to the second state.

25. The method according to claim 23, wherein In the region information, the identifier of the at least one region of interest is associated with one or more trigger events, and wherein the method includes: in response to the occurrence of each of the one or more trigger events, transitioning the at least one region of interest to a corresponding specific state, and wherein each of the one or more trigger events is associated with at least one of the following: the distance between the vehicle and the region of interest; and the expiration of a time period.

26. A system for a vehicle, comprising: at least one processor, and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method according to any one of claims 1 to 25.

27. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 1 to 25.

28. A computer program product comprising a computer program that, when run by a processor, executes the method according to any one of claims 1 to 25.

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