Graph exploration forward search

Abstract

A method for a graph forward search exploration may include detecting a plurality of obstacles along a first trajectory of a vehicle. Some described methods also include determining a plurality of effective combinations of the plurality of trajectories to handle the plurality of obstacles. Some described methods further include generating a simplified decision tree based at least on an effective combination of the plurality of trajectories by excluding at least a second trajectory of the plurality of trajectories associated with an obstacle based on a location of the obstacle outside a corridor defined by a spatial range and / or a temporal range. Some described methods also include selecting an optimal trajectory for the vehicle from the plurality of trajectories of the simplified decision tree. A system and a computer program product are also provided.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit and priority of U.S. Provisional Patent Application Serial No. 63 / 416,396, filed on Oct. 14, 2022, and U.S. Patent Application Serial No. 18 / 152,047, filed on Jan. 9, 2023, which are hereby incorporated by reference in their entirety. Background Art

[0003] Generally, a vehicle moves along a trajectory. When an obstacle such as another vehicle is detected along the trajectory, traditional systems consider all possible combinations of maneuvers for approaching the obstacle (e.g., passing the obstacle and / or stopping behind the obstacle). Especially in complex environments, considering a very large number of possible combinations is computationally costly, inefficient, and slow. Brief Description of the Drawings

[0004] Figure 1 is an example environment of a vehicle that can implement one or more components of an autonomous system;

[0005] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0006] Figure 3 is Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0007] Figure 4 is a diagram of certain components of an autonomous system;

[0008] Figure 5 is a flowchart of a process for graph exploration forward search;

[0009] Figure 6 is a diagram of an example decision tree;

[0010] Figure 7 is a diagram of an example simplified decision tree;

[0011] Figure 8 is a diagram of an example of a vehicle navigating on a roadway;

[0012] Figure 9 is a diagram of an example of a vehicle navigating on a roadway;

[0013] Figure 10 is a diagram of an example of an autonomous vehicle navigating on a roadway; and

[0014] Figure 11 is a flowchart of a process for graph exploration forward search. Detailed implementation manners

[0015] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent that the embodiments described in the present disclosure may be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0016] 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 processing order or sequence, or a separation of processes. Additionally, unless explicitly described, including 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.

[0017] 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 mean that connections, relationships, or associations cannot 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 disclosure. Additionally, 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 (such as a "software instruction"), those skilled in the art should understand that such an element may represent one or more than one signal path (such as a bus) that may be required to affect the communication.

[0018] Although terms such as "first", "second", and / or "third", etc. 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.

[0019] The terms used in the description of the various embodiments herein are included for the purpose of describing particular embodiments only 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 encompasses 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.

[0020] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receipt, transmission, conveyance, and / or provision of 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 directly or indirectly receive information from the other unit 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 therebetween 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 intermediary 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.

[0021] As used herein, depending on the context, the term "if" is optionally interpreted to mean "when", "at the time of", "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 "at the time of determining...", "in response to determining that", or "at the time of detecting [the stated condition or event]" and / or "in response to detecting [the stated condition or event]", etc. Further, as used herein, the terms "has", "have", or "having", etc. are intended to be open-ended terms. Additionally, unless otherwise explicitly stated, the phrase "based on" is intended to mean "at least partially based on".

[0022] 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 those 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.

[0023] General Overview

[0024] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement graph exploration forward search. For example, when disposing of obstacles along a vehicle's current trajectory, the system can cause the vehicle to go beyond the obstacles or stop behind at least one of the obstacles. To select an optimal maneuver or combination of maneuvers for indicating whether the vehicle will go beyond a particular obstacle or stop behind the obstacle, the system generates a simplified decision tree that includes all valid combinations of maneuvers. The system further generates the simplified decision tree by at least excluding irrelevant or otherwise inaccessible combinations to the vehicle. This results in a simplified set of possible combinations for faster and more efficient consideration.

[0025] Implementations of the systems, methods, and computer program products described herein provide techniques for forward search graph exploration. Some advantages of the described techniques include a significant reduction in computational requirements compared to traditional systems. In some examples, implementations of the systems and methods described herein reduce the computational resources consumed by the autonomous system of an autonomous vehicle when planning the operation of the autonomous vehicle through an environment. This allows the computational resources that would otherwise be used to be reallocated to other tasks performed by the autonomous system. The system also quickly and efficiently selects an optimal trajectory for a vehicle (e.g., an autonomous vehicle) based on a reduced set of possible combinations of trajectories. This allows the vehicle to quickly and efficiently respond to obstacles in the environment in which the vehicle is operating.

[0026] Now refer to Figure 1 , to illustrate example environment 100, in which vehicles including an autonomous system and vehicles not including an autonomous system operate. As illustrated, environment 100 includes vehicles 102a - 102n, objects 104a - 104n, routes 106a - 106n, area 108, vehicle - to - infrastructure (V2I) devices 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and forward search system 550. Vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and forward search system 550 (described in more detail with respect to Figures 5 to 11 are interconnected (e.g., establish connections for communication, etc.) via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some embodiments, objects 104a - 104n are interconnected with at least one of vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and forward search system 550 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0027] Vehicles 102a - 102n (individually referred to as vehicle 102 and collectively as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116, V2I system 118, and / or forward search system 550 via network 112. In some embodiments, vehicle 102 includes cars, buses, trucks, and / or trains, etc. In some embodiments, vehicle 102 is the same as vehicle 200 described herein (seeFigure 2 ) are the same or similar. In some embodiments, the vehicle 200 in the set of vehicles 200 is associated with an autonomous queue manager. In some embodiments, as described herein, the vehicle 102 travels along corresponding routes 106a - 106n (individually referred to as route 106 and collectively as routes 106). In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same or similar to the autonomous system 202).

[0028] The objects 104a - 104n (individually referred to as object 104 and collectively as objects 104) 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.). Each object 104 (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, the object 104 is associated with a corresponding location in the region 108.

[0029] The routes 106a - 106n (individually referred to as route 106 and collectively as routes 106) are each associated with (e.g., define) a series of actions (also referred to as trajectories) that connect the states along which the AV can navigate. Each route 106 begins with an initial state (e.g., a state corresponding to a first spatio - temporal location and / or speed, etc.) and ends with a final target state (e.g., a state corresponding to a second spatio - temporal location different from the first) or a target zone (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 zone includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, the route 106 includes multiple acceptable state sequences (e.g., multiple spatio - temporal location sequences), which are associated with (e.g., define) multiple trajectories. In an example, the route 106 includes only high - level actions or imprecise state locations, such as a series of connecting roads indicating a direction change at a roadway intersection. Additionally or alternatively, the route 106 can include more precise actions or states, such as, for example, a specific target lane or precise location within a lane area and the target rate at those locations. In an example, the route 106 includes multiple precise state sequences along at least one high - level action with a finite look - ahead horizon to reach an intermediate target, where the combination of consecutive iterations of the finite - horizon state sequences cumulatively corresponds to multiple trajectories that together form a high - level route terminating at the final target state or zone.

[0030] Region 108 includes a physical region (e.g., a geographical region) that the vehicle 102 can navigate. In an example, region 108 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 108 includes at least one named thoroughfare (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 108 includes at least one unnamed road, such as a lane, a section of a parking lot, a section of a vacant and / or undeveloped area, a dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0031] A vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure or vehicle-to-everything (V2X) device) includes at least one device configured to communicate with the vehicle 102 and / or the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, the platoon management system 116, and / or the V2I system 118 via the network 112. In some embodiments, the V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), a lane marking, a streetlight, a parking meter, etc. In some embodiments, the V2I device 110 is configured to communicate directly with the vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, and / or the platoon management system 116 via the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the V2I system 118 via the network 112.

[0032] The network 112 includes one or more wired and / or wireless networks. In an example, the network 112 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.

[0033] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the queue management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the queue management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0034] The queue management system 116 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems), etc.).

[0035] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the queue management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection different from the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipal authority or a private institution (e.g., a private institution for maintaining the V2I device 110, etc.).

[0036] Provide Figure 1 The number and arrangement of the illustrated elements are provided as examples. Compared with Figure 1 the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements with different arrangements. Additionally or alternatively, at least one element of the environment 100 may perform one or more functions described as being performed by Figure 1 at least one different element. Additionally or alternatively, at least one set of elements of the environment 100 may perform one or more functions described as being performed by at least one different set of elements of the environment 100.

[0037] Now refer to Figure 2 , vehicle 200 (which may be the same as or similar to Figure 1 vehicle 102) includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208, or is associated with the autonomous system 202, the powertrain control system 204, the steering control system 206, and the braking system 208. In some embodiments, vehicle 200 is the same as or similar to vehicle 102 (see Figure 1 ). In some embodiments, the autonomous system 202 is configured to endow vehicle 200 with autonomous driving capabilities (e.g., implement at least one of the following driving functions, features, and / or devices that are automatic or based on maneuvers, and the at least one driving function, feature, and / or device that is automatic or based on maneuvers enables vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon dependence on human intervention, such as level 5 ADS-operated vehicles), highly autonomous vehicles (e.g., vehicles that abandon dependence on human intervention in certain situations, such as level 4 ADS-operated vehicles), and / or conditionally autonomous vehicles (e.g., vehicles that abandon dependence on human intervention in limited situations, such as level 3 ADS-operated vehicles), etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to operate vehicle 200 in road traffic and continuously perform a part or all of the dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) that includes driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). 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 200 is associated with an autonomous queue manager and / or a ridesharing company.

[0038] The autonomous system 202 includes a sensor suite that includes one or more devices such as a camera 202a, a LiDAR sensor 202b, a Radar sensor 202c, and a microphone 202d. In some embodiments, the autonomous system 202 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 the vehicle 200, etc.). In some embodiments, the autonomous system 202 uses one or more devices included in the autonomous system 202 to generate data associated with the environment 100 described herein. The data generated by one or more devices of the autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which the vehicle 200 is located. In some embodiments, the autonomous system 202 includes a communication device 202e, an autonomous vehicle computing 202f, a drive-by-wire (DBW) system 202h, and a safety controller 202g.

[0039] The camera 202a includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus 302 that is the same or similar to Figure 3 the bus). The camera 202a 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 images including physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, the camera 202a generates camera data as an output. In some examples, the camera 202a 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 202a includes a plurality of independent cameras configured (e.g., positioned) on the vehicle for capturing images for the purpose of stereovision (stereo vision). In some examples, the camera 202a includes generating image data and transmitting the image data to the autonomous vehicle computing 202f and / or a queue management system (e.g., the same as Figure 1a plurality of cameras of the same or similar queue management system 116. In such an example, the autonomous vehicle computing 202f determines the depth to one or more objects in the field of view of at least two of the plurality of cameras based on image data from at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to the camera 202a.

[0040] In an embodiment, the camera 202a 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 202a generates traffic light data associated with one or more images. In some examples, the camera 202a generates TLD (Traffic Light Detection) data associated with one or more images including formats (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data is different from other systems incorporating cameras described herein in that the camera 202a 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.

[0041] The Light Detection and Ranging (LiDAR) sensor 202b includes being configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., with Figure 3at least one device configured to communicate via a bus (e.g., a bus identical or similar to bus 302). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object it encounters. The LiDAR sensor 202b also 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 202b 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 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b 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 202b.

[0042] A Radio Detection and Ranging (Radar) sensor 202c includes at least one device configured to communicate with a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g via a bus (e.g., a bus identical or similar to Figure 3 bus 302). The Radar sensor 202c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the Radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves emitted by the Radar sensor 202c encounter a physical object and are reflected back to the Radar sensor 202c. In some embodiments, the radio waves emitted by the Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the Radar sensor 202c generates a signal representing the objects included in the field of view of the Radar sensor 202c. For example, at least one data processing system associated with the Radar sensor 202c 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 some examples, the image is used to determine the boundary of the physical object in the field of view of the Radar sensor 202c.

[0043] The microphone 202d includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus the same or similar to Figure 3 bus 302). The microphone 202d 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 202d includes a transducer device and / or a similar device. In some embodiments, one or more of the systems described herein can receive data generated by the microphone 202d and determine the position (e.g., distance, etc.) of an object relative to the vehicle 200 based on the audio signal associated with the data.

[0044] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the DBW (drive-by-wire) system 202h. For example, the communication device 202e can include a device the same or similar to Figure 3 communication interface 314. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0045] The autonomous vehicle computing 202f includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the safety controller 202g, and / or the DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as a client device, a mobile device (e.g., a cellular phone and / or a tablet, etc.), and / or a server (e.g., a computing device including one or more central processing units and / or graphics processing units, etc.). In some embodiments, the autonomous vehicle computing 202f is the same or similar to the autonomous vehicle computing 400 described herein. Additionally or alternatively, in some embodiments, the autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to Figure 1 the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 queue management system 116), a V2I device (e.g., a V2I device the same or similar to Figure 1 V2I device 110), and / or a V2I system (e.g., a V2I system the same or similar to Figure 1communicate with a V2I system 118 that is the same as or similar to the V2I system).

[0046] The safety controller 202g includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the autonomous vehicle computing 202f, and / or the DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (such as 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 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). In some embodiments, the safety controller 202g 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 202f.

[0047] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (such as 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 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 200 (such as turn signals, headlights, door locks, and / or windshield wipers, etc.).

[0048] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movements (such as starting to move forward, stopping moving forward, starting to move backward, stopping moving backward, accelerating in a certain direction, decelerating in a certain direction, etc.), or perform lateral vehicle movements (such as making a left turn and / or making a right turn, etc.). In an example, the powertrain control system 204 increases, maintains the same, or decreases the energy (such as fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0049] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates two front wheels and / or two rear wheels of the vehicle 200 left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activities required to regulate the y-axis component of the vehicle's movement.

[0050] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate the vehicle 200 and / or keep it stationary. In some examples, the braking system 208 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 200 on the corresponding rotors of the vehicle 200. Additionally or alternatively, in some examples, the braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0051] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring the nature of the state or condition of the vehicle 200. In some examples, the vehicle 200 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. Although the braking system 208 is illustrated as being located Figure 2 proximal to the vehicle 200 within, the braking system 208 can be located anywhere within the vehicle 200.

[0052] Now refer to Figure 3, a schematic diagram of an exemplary device 300. As illustrated, device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of the system of vehicle 102); at least one device of forward search system 550 (e.g., at least one device of the system of forward search system 550); and / or one or more than one device of network 112 (e.g., one or more than one device of the system of network 112). In some embodiments, one or more than one device of vehicle 102 (e.g., at least one device of the system of vehicle 102), at least one device of forward search system 550 (e.g., at least one device of the system of forward search system 550), and / or one or more than one device of network 112 (e.g., one or more than one device of the system of network 112) includes at least one device 300 and / or at least one component of device 300. As Figure 3 shown, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[0053] Bus 302 includes components that permit communication between the components of device 300. In some cases, processor 304 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 306 includes random access memory (RAM), 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.).

[0054] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 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.

[0055] The input interface 310 includes components of the enabling device 300 that receive information via a user input (e.g., a touchscreen 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 310 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 312 includes components for providing output information from the device 300 (e.g., a display, a speaker, and / or one or more Light Emitting Diodes (LEDs), etc.).

[0056] In some embodiments, the communication interface 314 includes transceiver-like components that enable the device 300 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 receiver and transmitter, etc.). In some examples, the communication interface 314 enables the device 300 to receive information from another device and / or provide information to another device. In some examples, the communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a Radio Frequency (RF) interface, a Universal Serial Bus (USB) interface, a Wi-Fi interface, and / or a cellular network interface, etc.

[0057] In some embodiments, the device 300 performs one or more of the processes described herein. The device 300 performs these processes based on software instructions stored by a computer-readable medium such as the memory 306 and / or the storage component 308 and executed by the processor 304. 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.

[0058] In some embodiments, software instructions are read into the memory 306 and / or the storage component 308 from another computer-readable medium or from another device via the communication interface 314. When executed, the software instructions stored in the memory 306 and / or the storage component 308 cause the processor 304 to perform one or more of the processes described herein. Additionally or alternatively, hardwired circuitry is used instead of or in combination with software instructions to perform one or more of the processes described herein. Thus, unless otherwise explicitly stated, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0059] The memory 306 and / or the storage component 308 includes a data store or at least one data structure (e.g., a database, etc.). The device 300 is capable of receiving information from the data store or at least one data structure in the memory 306 or the storage component 308, storing the information in the data store or at least one data structure, communicating the information to the data store or at least one data structure, or searching for the 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.

[0060] In some embodiments, the device 300 is configured to execute software instructions stored in the memory 306 and / or the memory of another device (e.g., another device that is the same as or similar to the device 300). As used herein, the term "module" refers to at least one instruction stored in the memory 306 and / or the memory of another device, which when executed by the processor 304 and / or the processor of another device (e.g., another device that is the same as or similar to the device 300), causes the device 300 (e.g., at least one component of the device 300) to perform one or more processes described herein. In some embodiments, the module is implemented in software, firmware, and / or hardware, etc.

[0061] Provide Figure 3 The number and arrangement of the illustrated components are provided as an example. In some embodiments, compared to Figure 3 the illustrated components, the device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components of the device 300 (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 the device 300.

[0062] Now refer to Figure 4, an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack") is illustrated. As illustrated, the autonomous vehicle computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a localization system 406 (sometimes referred to as a localization module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in and / or implemented in the vehicle's automated navigation system (e.g., the autonomous vehicle computing 202f of the vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in one or more standalone systems (e.g., one or more systems identical or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in one or more standalone 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 400 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 400 is configured to communicate with remote systems (e.g., an autonomous vehicle system identical or similar to the remote AV system 114, a queue management system identical or similar to the queue management system 116, and / or a V2I system identical or similar to the V2I system 118, etc.).

[0063] In some embodiments, the perception system 402 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 402 to detect at least one physical object), and classifies the at least one physical object. In some examples, the perception system 402 receives image data captured by at least one camera (e.g., camera 202a), 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 402 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 402, the perception system 402 transmits data associated with the classification of the physical objects to the planning system 404.

[0064] In some embodiments, the planning system 404 receives data associated with a destination, and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel towards the destination. In some embodiments, the planning system 404 periodically or continuously receives data from the perception system 402 (e.g., the data associated with the classification of the physical objects described above), and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to the tactical functions required to operate the vehicle 102 in road traffic. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate speed, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with the updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0065] In some embodiments, the positioning system 406 receives data associated with (e.g., representing) the location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In certain examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on the respective point clouds. In these examples, the positioning system 406 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 410. Then, based on the positioning system 406 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 406 determines the location 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 types of other driving signals, etc.). In some embodiments, the map is generated in real time based on the data received by the perception system.

[0066] In another example, the positioning system 406 receives global navigation satellite system (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, the positioning system 406 receives GNSS data associated with the location of a vehicle in an area, and the positioning system 406 determines the latitude and longitude of the vehicle in the area. In such examples, the positioning system 406 determines the location of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, the positioning system 406 generates data associated with the location of the vehicle. In some examples, based on the positioning system 406 determining the location of the vehicle, the positioning system 406 generates data associated with the location of the vehicle. In such examples, the data associated with the location of the vehicle includes data associated with one or more semantic properties corresponding to the location of the vehicle.

[0067] In some embodiments, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle. In some examples, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle by generating and transmitting control signals to cause the powertrain control system (e.g., the DBW system 202h and / or the powertrain control system 204, etc.), the steering control system (e.g., the steering control system 206), and / or the braking system (e.g., the braking system 208) to operate. For example, the control system 408 is configured to perform operating functions such as lateral vehicle motion control or longitudinal vehicle motion control. Lateral vehicle motion control causes activities required to regulate the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes activities required to regulate the x-axis component of the vehicle motion. In an example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change states.

[0068] In some embodiments, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 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 402, the planning system 404, the positioning system 406, and / or the control system 408 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 402, the planning system 404, the positioning system 406, and / or the control system 408 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.).

[0069] The database 410 stores data transmitted to, received from, and / or updated by the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408. In some examples, the database 410 includes a storage component for storing data and / or software related to operations and using the autonomous vehicle computing 400 of at least one system (e.g., related to Figure 3the same or similar storage components as the storage component 308). In some embodiments, the database 410 stores data associated with 2D and / or 3D maps of at least one area. In some examples, the database 410 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 the vehicle 102 and / or the vehicle 200) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, backroads, and / or off-road paths, etc.), and cause at least one LiDAR sensor (e.g., a LiDAR sensor the same or similar to the LiDAR sensor 202b) to generate data associated with an image representing the objects included in the field of view of the at least one LiDAR sensor.

[0070] In some embodiments, the database 410 can be implemented across multiple devices. In some examples, the database 410 is included in a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 the queue management system 116), and / or a V2I system (e.g., a V2I system the same or similar to Figure 1 the V2I system 118), etc.

[0071] Reference Figure 5 , illustrates a flowchart of a process 500 for graph exploration forward search. In some embodiments, one or more of the steps described with respect to the process 500 are performed by a forward search system 550 (see Figure 1 )(e.g., fully and / or partially, etc.). In an embodiment, the forward search system 550 is included in the autonomous vehicle computing 400 and / or one or more other systems described with respect to the environment 100, etc. The forward search system 550 can be implemented in software (e.g., software instructions stored in a 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.

[0072] Additionally or alternatively, in some embodiments, one or more steps described with respect to process 500 are performed by another device or group of devices (such as vehicles 102a - 102n and / or vehicle 200, vehicle - to - infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and / or planning system 404, etc.) that are separate from or include the forward search system 550 (e.g., fully and / or partially, etc.). In some embodiments, the forward search system 550 includes vehicles 102a - 102n and / or vehicle 200, objects 104a - 104n, routes 106a - 106n, regions 108, vehicle - to - infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and / or planning system 404, forms part of vehicles 102a - 102n and / or vehicle 200, objects 104a - 104n, routes 106a - 106n, regions 108, vehicle - to - infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and / or planning system 404, is coupled to vehicles 102a - 102n and / or vehicle 200, objects 104a - 104n, routes 106a - 106n, regions 108, vehicle - to - infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and / or planning system 404, and / or uses vehicles 102a - 102n and / or vehicle 200, objects 104a - 104n, routes 106a - 106n, regions 108, vehicle - to - infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and / or planning system 404. In some embodiments, the forward search system 550 is the same as or similar to vehicles 102a - 102n and / or vehicle 200, objects 104a - 104n, routes 106a - 106n, regions 108, vehicle - to - infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, V2I system 118, and / or planning system 404.

[0073] In an embodiment, the forward search system 550 detects a plurality of obstacles along a trajectory of a particular vehicle (e.g., a sequence of actions that connect the states along which the vehicle can navigate). Although with respect to Figures 5 to 11Reference is made to vehicle 804, although vehicle 804 may also include vehicles 102a - 102n, vehicle 200, other vehicles described herein, and / or portions thereof.

[0074] Figure 8 FIG. 800 is an example of vehicle 804 navigating on a road lane along trajectory 806. As Figure 8 shown, trajectory 806 for vehicle 804 includes a plurality of obstacles 802. The plurality of obstacles 802 includes at least one, two, three, four, five, ten, or more than ten obstacles. The plurality of obstacles 802 are located at any point along trajectory 806. For example, the plurality of obstacles 802 may be located behind, to the left, and / or to the right of vehicle 804. The plurality of obstacles 802 includes at least one other vehicle on the road lane. In an embodiment, the plurality of obstacles 802 are objects 104a - 104n and / or include objects 104a - 104n.

[0075] Generally, when vehicle 804 approaches the plurality of obstacles 802, the forward search system 550 determines an optimal trajectory from a plurality of trajectories for dealing with the plurality of obstacles 802. Figure 9 FIG. 900 is an example of vehicle 804 navigating on a road lane, and Figure 10 FIG. 1000 is another example of vehicle 804 navigating on a road lane. Referring to Figure 9 , vehicle 804 follows along an initial trajectory 906, and the forward search system 550 determines an optimal trajectory 908 based at least on obstacles 802. Referring to Figure 10 , vehicle 804 follows along an initial trajectory 1006, and the forward search system 550 determines an optimal trajectory 1008 based at least on obstacles 802 and within a corridor 1010 (described in more detail below).

[0076] The multiple trajectories include at least one trajectory representing a maneuver (e.g., ignore, overtake, pass in front, pass behind, etc.) for each of the multiple obstacles 802 and / or at least one trajectory representing a lateral (e.g., side) maneuver (e.g., ignore, undefined, cross left lane boundary, cross right lane boundary, left within lane, right within lane, etc.). In other words, as the vehicle 804 approaches each of the multiple obstacles 802, the forward search system 550 determines whether the vehicle 804 should pass or overtake the obstacle, stop behind the obstacle, follow the obstacle, and / or move to the left or right of the obstacle, etc. When there are many obstacles 802 along the trajectory 806 of the vehicle 804, for example, the number of decisions made by the forward search system 550 can expand rapidly. The forward search system 550 efficiently and quickly reduces the number of decisions and trajectories to be considered when determining the optimal trajectory for the vehicle 804.

[0077] In an embodiment, the ignore-pass maneuver includes the vehicle 804 not overtaking the obstacle 802, staying behind the obstacle, and continuing on the current trajectory 806. In some embodiments, the ignore-pass maneuver involves the vehicle 804 identifying the obstacle 802 and operating along the trajectory 806 without overtaking the obstacle 802 for a period of time or a certain distance.

[0078] The overtake-pass maneuver includes the vehicle 804 overtaking the obstacle 802. For example, in some embodiments, the overtake-pass maneuver involves the vehicle 804 identifying the obstacle 802 and operating along the trajectory 806 by moving around the obstacle 802 (such as moving to the left or right of the obstacle 802, etc.). After that, after a period of time or a certain distance, for example, the vehicle 804 moves in front of the obstacle 802.

[0079] The pass-in-front-pass maneuver includes the vehicle 804 passing the obstacle 802 before reaching a certain location along the current trajectory 806. In some embodiments, the pass-in-front-pass maneuver involves the vehicle 804 identifying the obstacle 802 (such as the distance between the vehicle 804 and the obstacle 802) and identifying a certain location along the current trajectory 806 (such as the distance between the vehicle 804 and the certain location). The vehicle 804 determines the amount of time until the vehicle 804 will reach the certain location based on, for example, the distance between the vehicle 804 and the certain location and / or the speed of the vehicle 804. The vehicle 804 identifies the time and / or location along the current trajectory 806 before the certain location for the vehicle 804 to pass the obstacle 802 along the current trajectory and pass the obstacle 802 at the identified time and / or location.

[0080] Subsequent passage through a maneuver includes the vehicle 804 passing through the obstacle 802 after reaching a certain location along the current trajectory 806. In some embodiments, the subsequent passage through a maneuver involves: the vehicle 804 identifying the obstacle 802 (such as the distance between the vehicle 804 and the obstacle 802, etc.), and identifying a certain location along the current trajectory 806 (such as the distance between the vehicle 804 and the certain location, etc.). In some examples, the vehicle 804 determines when it has passed through a certain location. The vehicle 804 operates along the current trajectory 806 due to passing through the obstacle 802, at least based on determining that the vehicle 804 has passed through a certain location (such as after a period of time or a certain distance after passing through the certain location).

[0081] Ignoring a lateral maneuver includes the vehicle 804 ignoring the obstacle 802, staying behind the obstacle, and continuing on the current trajectory 806. In some embodiments, ignoring a lateral maneuver involves: the vehicle 804 identifying the obstacle 802 and operating along the trajectory 806 without exceeding the obstacle 802 or moving to the left or right of the obstacle 802 for a period of time or a certain distance.

[0082] Left lane boundary crossing lateral maneuver includes crossing the left lane boundary to move to the left of the obstacle 802. In some embodiments, the left lane boundary crossing lateral maneuver involves: the vehicle 804 identifying the obstacle 802 and operating along the trajectory 806 by crossing the left lane boundary to move to the left of the obstacle 802 after a period of time or a distance relative to the obstacle 802, or within a certain distance or time relative to the obstacle 802.

[0083] Right lane boundary crossing lateral maneuver includes crossing the right lane boundary to move to the right of the obstacle 802. In some embodiments, the right lane boundary crossing lateral maneuver involves: the vehicle 804 identifying the obstacle 802 and operating along the trajectory 806 by crossing the right lane boundary to move to the right of the obstacle 802 after a period of time or a distance relative to the obstacle 802, or within a certain distance or time relative to the obstacle 802.

[0084] In-lane left lateral maneuver includes moving to the left of the obstacle 802 without crossing the left lane boundary. In some embodiments, the in-lane left lateral maneuver involves: the vehicle 804 identifying the obstacle 802 and operating along the trajectory 806 by moving to the left of the obstacle 802 without crossing the left lane boundary after a period of time or a distance relative to the obstacle 802, or within a certain distance or time relative to the obstacle 802.

[0085] Lane-inward right lateral maneuvering actions include moving to the right side of the obstacle 802 without crossing the right lane boundary. In some embodiments, the lane-inward right lateral maneuvering actions involve: the vehicle 804 identifying the obstacle 802 and operating along a trajectory 806 by moving to the right side of the obstacle 802 without crossing the right lane boundary after a period of time or distance relative to the obstacle 802 or within a certain distance or time relative to the obstacle 802.

[0086] As an example, in Figure 9 , the vehicle 804 performs a stay-behind or ignore-pass maneuver where the vehicle 804 stays behind the obstacle 802. As another example, in Figure 10 , the vehicle 804 performs a pass-beyond maneuver where the vehicle 804 passes beyond the obstacle 802.

[0087] To help determine the optimal trajectory for the vehicle 804, the forward search system 550 generates a simplified decision tree (e.g., the simplified decision tree 700), which improves the computational speed and efficiency when determining the optimal trajectory. The forward search system 550 generates the simplified decision tree by including multiple valid combinations of multiple trajectories to handle multiple obstacles 802. As described herein, the simplified decision tree is a graph and / or a model, etc. The forward search system 550 performs a forward graph exploration search of the simplified decision tree to determine the optimal trajectory from multiple trajectories.

[0088] The simplified decision tree can be simplified compared to a decision tree that includes all combinations of trajectories and / or some combinations of trajectories, etc. Figure 6 is an example graph of a decision tree 600. The decision tree 600 includes all combinations of trajectories.

[0089] Referring to Figure 6 , the decision tree 600 includes one or more obstacles (e.g., multiple obstacles 802 such as obstacles 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, etc.), multiple pass maneuvers and / or lateral maneuvering actions (e.g., pass options and / or lateral options) 652, and branches 650 representing various trajectories. Referring again to Figure 6 , the trajectories are represented as branches 650 of the decision tree 600.

[0090] Each branch in branch 650 includes a subset of multiple combinations of passing maneuvers and / or lateral maneuvers for maneuvering the vehicle along each branch 650 relative to multiple obstacles 802 (e.g., performing maneuvering actions). In this example, decision tree 600 includes multiple columns corresponding to multiple obstacles and decisions (e.g., passing maneuvers and / or lateral maneuvers) corresponding to each obstacle. Each column can be assigned a specific identifier such as a numerical identifier or an alphanumeric identifier. Referring again to Figure 6 , the multiple columns of decision tree 600 corresponding to each of the multiple obstacles 802 include obstacles 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, etc. In decision tree 600, multiple combinations of passing maneuvers and / or lateral maneuvers 652 are shown as multiple decisions.

[0091] Return reference Figure 5 , at 502, forward search system 550 verifies possible lateral maneuvers and / or passing maneuvers. For example, forward search system 550 individually determines whether a passing maneuver and / or a lateral maneuver can be performed based on the position of the vehicle relative to the position of a specific obstacle. Thus, forward search system 550 independently (e.g., separately from each other) verifies each passing maneuver and each lateral maneuver for each obstacle to determine whether a passing maneuver and / or a lateral maneuver can be performed for each obstacle. Passing maneuvers and / or lateral maneuvers that are verified as being able to be performed are considered valid passing maneuvers and / or lateral maneuvers. Conversely, passing maneuvers and / or lateral maneuvers that are determined to be unable to be performed are considered invalid passing maneuvers and / or lateral maneuvers.

[0092] Forward search system 550 determines whether each passing maneuver and / or lateral maneuver is valid based at least on the position of vehicle 804 relative to the positions of multiple obstacles 802 (e.g., obstacles 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624). As an example, if another vehicle (e.g., an obstacle among multiple obstacles 802) is positioned directly to the left of vehicle 804, forward search system 550 will determine that a lateral maneuver of crossing the left lane boundary cannot be performed.

[0093] Reference Figure 5, at 504, the forward search system 550 validates the combined lateral maneuvering actions and passing maneuvers. In other words, the possible lateral maneuvering actions and passing maneuvers for each obstacle (e.g., the valid remaining options) can be combined to form the complete possible actions of the vehicle 804 relative to each of the multiple obstacles 802. For example, the forward search system 550 generates multiple combinations (e.g., two or more combinations) of independently validated passing maneuvers and / or independently validated lateral maneuvering actions.

[0094] Then, the forward search system 550 evaluates the multiple combinations of independently validated passing maneuvers and / or independently validated lateral maneuvering actions to define multiple valid combinations. The forward search system 550 generates multiple valid combinations by at least removing the invalid combinations of passing maneuvers and lateral maneuvering actions from the multiple combinations. For example, impossible combinations in the multiple combinations are removed from the multiple combinations.

[0095] As an example, a first vehicle (e.g., the first obstacle 602) can be positioned on the roadway directly in front of the vehicle 804. In this example, a "ignore" (e.g., stay behind) passing maneuver can be performed, and a "left lane boundary crossing" lateral maneuvering action can be performed. Thus, the forward search system 550 will independently validate each of the "ignore" passing maneuver and the "left lane boundary crossing" lateral maneuvering action as possible options. Then, the forward search system 550 will evaluate the combination of the "ignore" passing maneuver and the "left lane boundary crossing" lateral maneuvering action. In this case, since the vehicle 804 cannot both stay behind the first vehicle and drive to the left of the first vehicle, the "left lane boundary crossing" lateral maneuvering action cannot be combined with the "ignore" passing maneuver. Thus, the combination of the "ignore" passing maneuver and the "left lane boundary crossing" lateral maneuvering action will be removed from the multiple combinations of independently validated passing maneuvers and / or lateral maneuvering actions.

[0096] At 506, the forward search system 550 arranges decisions for a plurality of obstacles 802 to generate a decision tree 600. For example, the forward search system 550 sorts the plurality of obstacles (and corresponding valid combinations) based on the distance (e.g., spatial location) between the vehicle 804 and the plurality of obstacles 802. In some examples, the obstacle (e.g., obstacle 602) positioned at the shortest distance relative to the vehicle 804 among the plurality of obstacles 802 is sorted as the first in the first column of the decision tree 600. In some examples, an obstacle (e.g., obstacle 604) among the plurality of obstacles 802 is positioned along the roadway as the next shortest distance relative to the vehicle 804, and as a result, is sorted as the second in the second column of the decision tree 600, and so on. Thus, the plurality of obstacles 802 are classified in the decision tree 600 based on the order of appearance of the plurality of obstacles 802 relative to the vehicle 804. In other examples, the plurality of obstacles 802 are classified in the decision tree 600 based on the time distance relative to the vehicle 804 (e.g., a time amount based on the speed of the vehicle and / or the obstacle).

[0097] In some embodiments, the forward search system 550 generates a reduced decision tree (e.g., reduced relative to the decision tree 600) based at least on a plurality of valid combinations by at least excluding a trajectory (e.g., branch 650) among the plurality of trajectories (e.g., from a reduced decision tree). For example, at 508 (see Figure 5 ), the forward search system 550 performs a tree search of a plurality of possible or valid combinations. During this tree search, the forward search system 550 evaluates the reachability of the obstacles within each trajectory relative to the previous obstacles (e.g., spatial and / or temporal reachability). Based on determining that at least one obstacle among the plurality of obstacles 802 included in a particular trajectory is unreachable, the forward search system 550 excludes that trajectory when generating the reduced decision tree.

[0098] In some embodiments, the forward search system 550 determines that at least one obstacle is unreachable based on the position of the at least one obstacle among the plurality of obstacles 802 being outside a corridor defined by a spatial range and / or a temporal range. The corridor may include at least one (e.g., one, two, three, four, or more than four) trajectories. For example, the corridor includes at least one trajectory retained in the reduced decision tree.

[0099] Spatial extent and / or temporal extent delimit the corridor. The spatial extent includes a range of linear distances away from the vehicle 804 (e.g., at least one linear distance). In an embodiment, the spatial extent is a spatial horizon that can be reached by the vehicle 804 based on a plurality of obstacles 802 within the current position and trajectory of the vehicle 804 (e.g., branch 650). The temporal extent includes a range of temporal distances away from the vehicle 804 (e.g., at least one temporal distance). In an embodiment, the temporal extent is a temporal horizon that can be reached by the vehicle 804 based on a plurality of obstacles 802 within the current position and trajectory of the vehicle 804 (e.g., branch 650). In an embodiment, the spatial extent and / or temporal extent includes a linear combination of the spatial extent and the temporal extent. The spatial extent and / or temporal extent can be pre-determined. The spatial extent and / or temporal extent can be dynamically adjusted by the forward search system 550. Thus, when generating the simplified decision tree, trajectories having at least one obstacle outside the spatial extent and / or temporal extent (e.g., greater than or equal to the spatial extent and / or temporal extent) can be excluded.

[0100] Figure 7 is a diagram of an example simplified decision tree 700. After excluding trajectories having at least one obstacle outside the corridor delimited by the spatial extent and / or temporal extent, the simplified decision tree 700 includes two trajectories. For example, the simplified decision tree 700 includes a first trajectory 750A that includes a first obstacle 702 among the plurality of obstacles 802 (corresponding to a column of the decision tree 600), a second obstacle 704 among the plurality of obstacles 802 (corresponding to a column of the decision tree 600), a third obstacle 706 among the plurality of obstacles 802 (corresponding to a column of the decision tree 600), a fourth obstacle 708 among the plurality of obstacles 802 (corresponding to a column of the decision tree 600), a fifth obstacle 710 among the plurality of obstacles 802 (corresponding to a column of the decision tree 600), and a sixth obstacle 712 among the plurality of obstacles 802 (corresponding to a column of the decision tree 600). The simplified decision tree 700 further includes a second trajectory 750B that includes the first obstacle 702, the second obstacle 704, the third obstacle 706, the fourth obstacle 708, and the fifth obstacle 710.

[0101] As Figure 7As shown, in the first trajectory 750A, the first obstacle 702 corresponds to a first effective combination (e.g., option) of a maneuver 752 (e.g., ignore) and a lateral maneuver 754 (e.g., left within the lane), the second obstacle 704 corresponds to a second effective combination (e.g., option) of a maneuver 752 (e.g., ignore) and a lateral maneuver 754 (e.g., left within the lane), the third obstacle 706 corresponds to a third effective combination (e.g., option) of a maneuver 752 (e.g., overtake) and a lateral maneuver 754 (e.g., right within the lane), the fourth obstacle 708 corresponds to a fourth effective combination (e.g., option) of a maneuver 752 (e.g., pass behind) and a lateral maneuver 754 (e.g., not defined), and the fifth obstacle 710 corresponds to a fifth effective combination (e.g., option) of a maneuver 752 (e.g., lane keeping) and a lateral maneuver 754 (e.g., lane keeping). Additionally, as Figure 7 shown, in the second trajectory 750B, the first obstacle 702 corresponds to a first effective combination (e.g., option) of a maneuver 752 (e.g., ignore) and a lateral maneuver 754 (e.g., left within the lane), the second obstacle 704 corresponds to a second effective combination (e.g., option) of a maneuver 752 (e.g., ignore) and a lateral maneuver 754 (e.g., left within the lane), the third obstacle 706 corresponds to a third effective combination (e.g., option) of a maneuver 752 (e.g., pass behind) and a lateral maneuver 754 (e.g., not defined), and the fourth obstacle 708 corresponds to a fourth effective combination (e.g., option) of a maneuver 752 (e.g., lane keeping) and a lateral maneuver 754 (e.g., not defined).

[0102] The forward search system 550 selects an optimal trajectory for the vehicle 804 from among multiple trajectories of the simplified decision tree 700. Referring to Figure 7 , the forward search system 550 selects an optimal trajectory for the vehicle between the first trajectory 750A and the second trajectory 750B. Thus, the forward search system 550 quickly and efficiently selects an optimal trajectory for the vehicle without evaluating every possible trajectory for the vehicle 804. This reduces the computational resource requirements and improves the processing efficiency.

[0103] Now referring to Figure 11, A flowchart illustrating process 1100 for forward search in graph exploration. In some embodiments, one or more of the steps described with respect to process 1100 are performed by forward search system 550 (e.g., fully and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more of the steps described with respect to process 1100 are performed by another device or group of devices separate from or including forward search system 550 (e.g., fully and / or partially, etc.).

[0104] At 1102, at least one processor (e.g., forward search system 550) detects a plurality of obstacles (e.g., obstacle 802 and / or object 104a - 104n) along a first trajectory of a vehicle (e.g., vehicle 102 and / or vehicle 200). As described herein, the plurality of obstacles includes at least one obstacle, such as a vehicle on a road, etc. In an embodiment, the first trajectory is one of a plurality of trajectories for the vehicle. In other embodiments, the first trajectory is the current trajectory along which the vehicle is currently traveling.

[0105] At 1104, at least one processor determines a plurality of valid combinations of a plurality of trajectories (e.g., branch 650) to handle the plurality of obstacles. The plurality of trajectories may correspond to the plurality of obstacles. The plurality of trajectories includes at least one passing maneuver (e.g., ignore, overtake, pass in front, pass behind, etc.) and / or at least one lateral maneuver (e.g., ignore, not defined, cross left lane boundary, cross right lane boundary, left within lane, right within lane, etc.) for each of the plurality of obstacles. The plurality of valid combinations includes combinations of at least two trajectories that may exist based on the position of the vehicle relative to the obstacles.

[0106] In an embodiment, at least one processor independently validates the passing maneuver and / or the lateral maneuver for each of the plurality of obstacles. For example, at least one processor determines whether a passing maneuver and / or a lateral maneuver can be performed based on the position of the vehicle relative to a particular obstacle. In an embodiment, at least one processor removes (e.g., excludes) the passing maneuver from the plurality of passing maneuvers and / or removes (e.g., excludes) the lateral maneuver from the plurality of lateral maneuvers based on independently validating the passing maneuver and / or the lateral maneuver for each obstacle. In other words, at least one processor removes passing maneuvers and / or lateral maneuvers that are not valid options. In an embodiment, as described herein, at least one processor excludes passing maneuvers and / or lateral maneuvers that are not valid options from a simplified decision tree.

[0107] Additionally and / or alternatively, at least one processor generates a plurality (e.g., at least two) of combinations of maneuvering actions and lateral maneuvering actions that are independently verified. The plurality of combinations can be independently verified (e.g., by at least one processor) to define a plurality of valid combinations. Additionally, at least one processor can remove (e.g., exclude) at least one of the plurality of combinations based on independently verifying the plurality of combinations.

[0108] At 1106, at least one processor generates a reduced decision tree (e.g., reduced decision tree 700) based at least on a plurality of valid combinations by at least excluding a second trajectory associated with an obstacle of the plurality of obstacles outside a corridor defined by a spatial range and / or a temporal range based on the position of the obstacle of the plurality of obstacles. The spatial range includes a range of distances relative to the vehicle. For example, the spatial range includes one or more threshold distances relative to the vehicle. The temporal range includes an amount of time away from the vehicle. Thus, the corridor can be defined by a range of distances relative to the vehicle and / or an amount of time away from the vehicle. The temporal range and / or the spatial range can be adjusted to limit the number of combinations remaining in the reduced decision tree. This allows the at least one processor to more quickly process obstacles and determine an optimal trajectory along which the vehicle travels.

[0109] At least one processor can sort the plurality of obstacles in the reduced decision tree based on the distance between the vehicle and the plurality of obstacles. In other words, the obstacle closest to the vehicle is sorted first.

[0110] At 1108, at least one processor selects an optimal trajectory (e.g., optimal trajectories 908, 1008) of the vehicle from the plurality of trajectories in the reduced decision tree. This allows the vehicle to quickly and efficiently respond to obstacles by considering fewer possible trajectories for the vehicle.

[0111] According to some non - limiting embodiments or examples, a system is provided that includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause operations that include: detecting a plurality of obstacles along a first trajectory of a vehicle; determining a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, where the plurality of trajectories correspond to the plurality of obstacles; generating a reduced decision tree based at least on the plurality of valid combinations by at least excluding a second trajectory associated with an obstacle of the plurality of obstacles outside a corridor defined by a spatial range and / or a temporal range based on the position of the obstacle of the plurality of obstacles; and selecting an optimal trajectory of the vehicle from the plurality of trajectories of the reduced decision tree.

[0112] According to some non - limiting embodiments or examples, at least one non - transitory computer - readable medium is provided, which includes one or more instructions that, when executed by at least one processor, cause the at least one processor to: detect a plurality of obstacles along a first trajectory of a vehicle; determine a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; at least exclude a second trajectory associated with an obstacle among the plurality of trajectories by a position of the obstacle among the plurality of obstacles being outside a corridor defined by a spatial range and / or a temporal range, generate a simplified decision tree based at least on the plurality of valid combinations; and select an optimal trajectory of the vehicle from the plurality of trajectories of the simplified decision tree.

[0113] According to some non - limiting embodiments or examples, a method is provided, including: detecting a plurality of obstacles along a first trajectory of a vehicle; determining a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; at least excluding a second trajectory associated with an obstacle among the plurality of trajectories by a position of the obstacle among the plurality of obstacles being outside a corridor defined by a spatial range and / or a temporal range, generate a simplified decision tree based at least on the plurality of valid combinations; and select an optimal trajectory of the vehicle from the plurality of trajectories of the simplified decision tree.

[0114] Further non - limiting aspects or embodiments are set forth in the numbered clauses below:

[0115] Clause 1: A system includes: at least one processor; and at least one memory storing instructions thereon that, when executed by the at least one processor, result in operations including: detecting a plurality of obstacles along a first trajectory of a vehicle; determining a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; at least excluding a second trajectory associated with an obstacle among the plurality of trajectories by a position of the obstacle among the plurality of obstacles being outside a corridor defined by a spatial range and / or a temporal range, generate a simplified decision tree based at least on the plurality of valid combinations; and select an optimal trajectory of the vehicle from the plurality of trajectories of the simplified decision tree.

[0116] Clause 2: The system according to Clause 1, wherein the plurality of valid combinations include combinations of at least two trajectories among the plurality of trajectories that can exist based on a position of the vehicle relative to a position of the obstacle.

[0117] Clause 3: The system according to Clause 1 or 2, wherein the simplified decision tree is further generated by including at least one trajectory associated with at least one of the plurality of obstacles in the corridor based on the position of at least one of the plurality of obstacles within the corridor.

[0118] Clause 4: The system according to any one of Clauses 1 to 3, wherein the plurality of trajectories include passing maneuvers and / or lateral maneuvers for each of the plurality of obstacles; and wherein determining the plurality of valid combinations includes: independently verifying the passing maneuvers and / or lateral maneuvers for each of the plurality of obstacles.

[0119] Clause 5: The system according to Clause 4, wherein determining the plurality of valid combinations further includes: removing the passing maneuver from the plurality of passing maneuvers and / or removing the lateral maneuver from the plurality of lateral maneuvers based on independently verifying the passing maneuvers and / or lateral maneuvers for each obstacle.

[0120] Clause 6: The system according to Clause 4 or 5, wherein determining the plurality of valid combinations further includes: generating a plurality of combinations of the independently verified passing maneuvers and lateral maneuvers.

[0121] Clause 7: The system according to Clause 6, wherein determining the plurality of valid combinations further includes: independently verifying the plurality of combinations to define the plurality of valid combinations.

[0122] Clause 8: The system according to Clause 7, wherein determining the plurality of valid combinations further includes: removing combinations from the plurality of combinations based on independently verifying the plurality of combinations.

[0123] Clause 9: The system according to any one of Clauses 1 to 8, wherein determining the plurality of valid combinations further includes: sorting the plurality of obstacles in the simplified decision tree based on the distance between the vehicle and the plurality of obstacles.

[0124] Clause 10: A method includes: detecting a plurality of obstacles along a first trajectory of a vehicle; determining a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; at least excluding a second trajectory associated with an obstacle from the plurality of trajectories by being outside a corridor defined by a spatial range and / or a time range based on the position of the obstacle, generating a simplified decision tree at least based on the plurality of valid combinations; and selecting an optimal trajectory of the vehicle from the plurality of trajectories of the simplified decision tree.

[0125] Clause 11: The method according to Clause 10, wherein the plurality of valid combinations includes combinations of at least two trajectories among the plurality of trajectories that can exist based on the position of the vehicle relative to the obstacle.

[0126] Clause 12: The method according to Clause 10 or 11, wherein the simplified decision tree is further generated by including at least one trajectory among the plurality of trajectories associated with at least one of the plurality of obstacles in the simplified decision tree based on the position of at least one of the plurality of obstacles within the corridor.

[0127] Clause 13: The method according to any one of Clauses 10 to 12, wherein the plurality of trajectories includes a passing maneuver and / or a lateral maneuver for each of the plurality of obstacles; and wherein determining the plurality of valid combinations includes: independently verifying the passing maneuver and / or the lateral maneuver for each of the plurality of obstacles.

[0128] Clause 14: The method according to Clause 13, wherein determining the plurality of valid combinations further includes: removing the passing maneuver from the plurality of passing maneuvers and / or removing the lateral maneuver from the plurality of lateral maneuvers based on independently verifying the passing maneuver and / or the lateral maneuver for each obstacle.

[0129] Clause 15: The method according to any one of Clauses 10 to 14, wherein determining the plurality of valid combinations further includes: generating a plurality of combinations of the independently verified passing maneuvers and lateral maneuvers.

[0130] Clause 16: The method according to Clause 15, wherein determining the plurality of valid combinations further includes: independently verifying the plurality of combinations to define the plurality of valid combinations.

[0131] Clause 17: The method according to Clause 16, wherein determining the plurality of valid combinations further includes: removing combinations from the plurality of combinations based on independently verifying the plurality of combinations.

[0132] Clause 18: The method according to any one of Clauses 10 to 17, wherein determining the plurality of valid combinations further includes: sorting the plurality of obstacles in the simplified decision tree based on the distance between the vehicle and the plurality of obstacles.

[0133] Clause 19: At least one non-transitory storage medium storing instructions which, when executed by at least one processor, cause the at least one processor to: Detect a plurality of obstacles along a first trajectory of a vehicle; Determine a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; At least exclude a second trajectory associated with an obstacle among the plurality of trajectories by the position of the obstacle among the plurality of obstacles being outside a corridor defined by a spatial extent and / or a temporal extent, Generate a simplified decision tree based at least on the plurality of valid combinations; and Select an optimal trajectory of the vehicle from the plurality of trajectories of the simplified decision tree.

[0134] Clause 20: The at least one non-transitory storage medium according to Clause 19, wherein the plurality of trajectories include a passing maneuver and / or a lateral maneuver for each of the plurality of obstacles; and wherein the instructions that cause the at least one processor to determine the plurality of valid combinations cause the at least one processor to: Independently verify a passing maneuver and / or a lateral maneuver for each of the plurality of obstacles.

[0135] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details, which may vary according to 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 as issued from this application in the specific form of the issued claims, including any subsequent amendments. Any definition of terms expressly set forth herein for inclusion in such claims shall be construed to have the meaning such terms have as used in the claims. Additionally, when the term "further comprises" is used in the foregoing specification or the appended claims, the text following such phrase may be additional steps or entities, or sub-steps / sub-entities of the previously recited steps or entities.

Claims

1. A system, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause operations including: detecting a plurality of obstacles along a first trajectory of a vehicle; determining a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; generating a simplified decision tree at least based on the plurality of valid combinations by at least excluding, based on positions of obstacles among the plurality of obstacles, a second trajectory associated with an obstacle from among the plurality of trajectories that is outside a corridor defined by a spatial extent and / or a temporal extent; and selecting an optimal trajectory of the vehicle from among the plurality of trajectories of the simplified decision tree.

2. The system according to claim 1, wherein, The plurality of valid combinations include combinations of at least two trajectories among the plurality of trajectories that can exist based on a position of the vehicle relative to a position of an obstacle.

3. The system according to claim 1 or 2, wherein The simplified decision tree is further generated by at least including, based on a position of at least one obstacle among the plurality of obstacles being within the corridor, at least one trajectory associated with the at least one obstacle from among the plurality of trajectories in the simplified decision tree.

4. The system according to any one of claims 1 to 3, wherein, The plurality of trajectories include passing maneuvers and / or lateral maneuvers for each of the plurality of obstacles; and wherein determining the plurality of valid combinations includes: independently verifying passing maneuvers and / or lateral maneuvers for each of the plurality of obstacles.

5. The system according to claim 4, wherein, Determining the plurality of valid combinations further includes: removing the passing maneuver from among a plurality of passing maneuvers and / or removing the lateral maneuver from among a plurality of lateral maneuvers based on independently verifying the passing maneuver and / or the lateral maneuver for each obstacle.

6. The system according to claim 5, wherein, Determining the plurality of valid combinations further includes: generating a plurality of combinations of independently verified passing maneuvers and lateral maneuvers.

7. The system according to claim 6, wherein, Determining the plurality of valid combinations further includes: independently verifying the plurality of combinations to define the plurality of valid combinations.

8. The system according to claim 7, wherein, Determining the plurality of valid combinations further includes: removing combinations from among the plurality of combinations based on independently verifying the plurality of combinations.

9. The system according to claim 1, wherein Determining the plurality of valid combinations further includes: sorting the plurality of obstacles in the simplified decision tree based on a distance between the vehicle and the plurality of obstacles.

10. A method, comprising: detecting a plurality of obstacles along a first trajectory of a vehicle; determining a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; generating a simplified decision tree at least based on the plurality of valid combinations by at least excluding, based on positions of obstacles among the plurality of obstacles, a second trajectory associated with an obstacle from among the plurality of trajectories that is outside a corridor defined by a spatial extent and / or a temporal extent; and selecting an optimal trajectory of the vehicle from among the plurality of trajectories of the simplified decision tree.

11. The method according to claim 10, wherein, The plurality of valid combinations include combinations of at least two trajectories among the plurality of trajectories that can exist based on a position of the vehicle relative to a position of an obstacle.

12. The method according to claim 10 or 11, wherein The simplified decision tree is further generated by including at least one trajectory associated with at least one of the plurality of obstacles in the corridor based on the position of at least one of the plurality of obstacles within the corridor.

13. The method according to any one of claims 10 to 12, wherein The plurality of trajectories include a passing maneuver and / or a lateral maneuver for each of the plurality of obstacles; and wherein determining the plurality of valid combinations includes: independently verifying the passing maneuver and / or the lateral maneuver for each of the plurality of obstacles.

14. The method according to claim 13, wherein, Determining the plurality of valid combinations further includes: removing the passing maneuver from the plurality of passing maneuvers and / or removing the lateral maneuver from the plurality of lateral maneuvers based on independently verifying the passing maneuver and / or the lateral maneuver for each obstacle.

15. The method according to claim 14, wherein Determining the plurality of valid combinations further includes: generating a plurality of combinations of the independently verified passing maneuvers and lateral maneuvers.

16. The method according to claim 15, wherein, Determining the plurality of valid combinations further includes: independently verifying the plurality of combinations to define the plurality of valid combinations.

17. The method according to claim 16, wherein Determining the plurality of valid combinations further includes: removing combinations from the plurality of combinations based on independently verifying the plurality of combinations.

18. The method according to claim 10, wherein Determining the plurality of valid combinations further includes: sorting the plurality of obstacles in the simplified decision tree based on the distance between the vehicle and the plurality of obstacles.

19. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to: Detect a plurality of obstacles along a first trajectory of a vehicle; Determine a plurality of valid combinations of a plurality of trajectories to handle the plurality of obstacles, wherein the plurality of trajectories correspond to the plurality of obstacles; Generate a simplified decision tree based at least on the plurality of valid combinations by at least excluding a second trajectory associated with an obstacle from the plurality of trajectories based on the position of the obstacle among the plurality of obstacles outside a corridor defined by a spatial range and / or a temporal range; and Select an optimal trajectory of the vehicle from the plurality of trajectories of the simplified decision tree.

20. The at least one non-transitory storage medium according to claim 19, wherein, The plurality of trajectories include a passing maneuver and / or a lateral maneuver for each of the plurality of obstacles; and wherein the instructions that cause the at least one processor to determine the plurality of valid combinations cause the at least one processor to: Independently verify the passing maneuver and / or the lateral maneuver for each of the plurality of obstacles.

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