Simulating smart pedestrian

Abstract

A method for simulating a smart pedestrian is provided. The method includes obtaining attributes of at least one pedestrian dynamics model. Simulated sensor data associated with the environment is generated. Operation of the autonomous system in the environment is simulated based on the simulated sensor data, wherein the vehicle and pedestrian interactions are modeled as external forces in the environment that affect the behavior of the respective pedestrian using at least one pedestrian kinetic model.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 416,484, filed October 14, 2022, entitled "Simulated Smart Pedestrians", which is hereby incorporated by reference in its entirety. Background of the Invention

[0003] Autonomous systems obtain data from the surrounding environment and use that data to navigate through the environment. Autonomous systems include subsystems, sensors, and devices for processing the data so that the autonomous system can perceive and understand the environment. Based on the outputs of these subsystems, sensors, and devices, the autonomous system makes decisions to navigate through the environment. 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 shows an implementation of a process for simulating smart pedestrians;

[0009] Figure 6 shows a simulation infrastructure;

[0010] Figure 7A is an illustration of a social force model;

[0011] Figure 7B is an illustration of a pedestrian moving through an environment subject to external forces;

[0012] Figure 8A shows different zones for classifying a zone of influence;

[0013] Figure 8B shows a proactive metric associated with a safety assessment of a simulation including a pedestrian dynamics model based on a social force model;

[0014] Figure 9 Illustrate the simulation of intelligent pedestrians according to the social force model in different scenarios;

[0015] Figure 10 Illustrate a flowchart of a first process for simulating intelligent pedestrians; and

[0016] Figure 11 Illustrate a flowchart of a second process for simulating intelligent pedestrians. DETAILED DESCRIPTION

[0017] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that the embodiments described herein 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.

[0018] In the drawings, for ease of description, the specific arrangements or orderings of illustrative elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements, etc.) are illustrated. However, those skilled in the art will understand that unless explicitly described, the specific orderings or arrangements of the illustrative elements in the drawings are not intended to imply a requirement for a particular processing order or sequence, or a separation of processes. Additionally, unless explicitly described, the inclusion of illustrative 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.

[0019] Furthermore, in the drawings, connecting elements (such as solid lines, dashed lines, or arrows, etc.) are used to illustrate connections, relationships, or associations between two or more other illustrative elements or among them. The absence of any such connecting element is not intended to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the 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 signals, data, or instructions (such as "software instructions"), those skilled in the art will understand that such an element may represent one or more signal paths (such as a bus) that may be required to affect the communication.

[0020] 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.

[0021] The terms used in the description of the various embodiments herein are included only for the purpose of describing specific embodiments 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 indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms "comprise", "include", "have", and / or "with" are used in this specification, they 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 their groups.

[0022] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof, etc.) that is to communicate with another unit, this means that the unit can directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This may refer to a direct or indirect connection that is essentially wired and / or wireless. Additionally, even if the information transmitted can be modified, processed, relayed, and / or routed between the first unit and the second unit, the two units can still communicate with each other. For example, even if the first unit receives information passively and does not actively transmit information to the second unit, the first unit can still communicate with the second unit. As another example, if at least one intermediate unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message may refer to a network packet (e.g., a data packet, etc.) that includes data.

[0023] 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 [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 [stated condition or event]" and / or "in response to detecting [stated condition or event]", etc. Additionally, as used herein, terms such as "have", "having", or "possess" are intended to be open-ended terms. Further, unless otherwise explicitly stated, the phrase "based on" is intended to mean "at least partially based on".

[0024] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0025] General Overview

[0026] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement simulated intelligent pedestrians. Pedestrian behavior is modeled as being affected by one or more forces. A simulation including the intelligent pedestrians is performed. In an example, the simulation enables testing, validating, and verifying autonomous system performance based on vehicle-pedestrian interaction.

[0027] By virtue of the implementation of the systems, methods, and computer program products described herein, the technology for simulating intelligent pedestrians enables experimentation, evaluation, and iteration of vehicle behavior solutions. Complex scenarios are replicated during the simulation without posing a danger to humans while enabling the development of autonomous vehicles that are evaluated during collisions with humans.

[0028] Now refer to Figure 1, an exemplary environment 100 is illustrated, in which vehicles including autonomous systems and vehicles not including autonomous systems 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, and V2I system 118. Vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via a wired connection, a wireless connection, or a combination of wired and wireless connections (e.g., establishing a connection for communication, etc.). 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, and V2I system 118 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0029] Vehicles 102a - 102n (individually referred to as vehicle 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or passengers. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, and / or trains, etc. In some embodiments, vehicle 102 is the same as or similar to vehicle 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicle 102 travels along corresponding routes 106a - 106n (individually referred to as route 106 and collectively referred to as routes 106). In some embodiments, one or more than one vehicle 102 includes an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0030] 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., building, sign, 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, object 104 is associated with a corresponding location in region 108.

[0031] Routes 106a - 106n (individually referred to as route 106 and collectively as routes 106) are each associated with (e.g., define) a sequence of actions (also referred to as a trajectory) that a connected AV can navigate along. Each route 106 begins at an initial state (e.g., a state corresponding to a first spatio - temporal location and / or speed, etc.) and ends at a final target state (e.g., a state corresponding to a second spatio - temporal location different from the first spatio - temporal location) 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, route 106 includes multiple acceptable sequences of states (e.g., multiple sequences of spatio - temporal locations) that are associated with (e.g., define) multiple trajectories. In an example, route 106 includes only high - level actions or imprecise state locations, such as a series of connected roads indicating a direction change at a roadway intersection. Additionally or alternatively, route 106 can include more precise actions or states, such as, for example, a specific target lane or precise location within a lane region and a target rate at those locations. In an example, route 106 includes multiple precise state sequences along at least one high - level action with a finite look - ahead horizon having an intermediate target, where the combination of successive iterations of the finite - horizon state sequences cumulatively corresponds to multiple trajectories that together form a high - level route terminating at the final target state or zone.

[0032] Region 108 includes a physical region (e.g., a geographical region) in which 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 arterial road (referred to herein as a "road"), such as a highway, an interstate highway, a parkway, an urban street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a driveway, 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 through which vehicle 102 can pass). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0033] 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 vehicle 102 and / or V2I system 118. In some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, 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, V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, and / or queue management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[0034] Network 112 includes one or more wired and / or wireless networks. In an example, 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-optic-based network, a cloud computing network, etc., and / or a combination of some or all of these networks.

[0035] 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.

[0036] 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.).

[0037] 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.).

[0038] Provide Figure 1 The number and arrangement of the illustrated elements are for example purposes. 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.

[0039] 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 automatic or maneuver-based functions, features, and / or devices, etc., the at least one driving automatic or maneuver-based function, feature, and / or device enabling vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations, such as level 3 ADS-operated vehicles, etc.), etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to enable vehicle 200 to operate in road traffic and continuously perform 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.

[0040] 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 the vehicle 200 has traveled, 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.

[0041] 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 as 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 as the queue management system 116. In such an example, the autonomous vehicle computing 202f determines the depth to one or more objects in the fields 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.

[0042] 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 providing 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 a format (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.

[0043] 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 that communicates 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.

[0044] 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.

[0045] 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 than one microphone (e.g., an array microphone and / or an external microphone, etc.) that captures an audio signal and generates 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 than one system described herein can receive the 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.

[0046] 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).

[0047] 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 computer, etc.), and / or a server (e.g., a computing device including one or more than one central processing unit and / or a graphics processing unit, 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)

[0048] 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.

[0049] 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.).

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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 speed sensor, a wheel braking pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor, etc. Although the braking system 208 is illustrated as being located Figure 2 proximal to the vehicle 200 in, the braking system 208 can be located anywhere in the vehicle 200.

[0054] Now refer to Figure 3 , a schematic diagram of the exemplary device 300. As illustrated, the 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, the device 300 corresponds to: at least one device of the vehicle 102 (e.g., at least one device of the system of the vehicle 102); and / or one or more devices of the network 112 (e.g., one or more devices of the system of the network 112). In some embodiments, one or more devices of the vehicle 102 (e.g., one or more devices of the system of the vehicle 102), and / or one or more devices of the network 112 (e.g., one or more devices of the system of the network 112) include at least one device 300 and / or at least one component of the device 300. As Figure 3As shown, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[0055] Bus 302 includes components that permit communication among 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.).

[0056] 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, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and / or another type of computer-readable medium, and corresponding drives.

[0057] Input interface 310 includes components that permit device 300 to receive information such as via a user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, 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.). Output interface 312 includes components for providing output information from device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0058] In some embodiments, communication interface 314 includes transceiver-like components (e.g., a transceiver and / or separate receiver and transmitter, etc.) that permit 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. In some examples, communication interface 314 permits device 300 to receive information from another device and / or provide information to another device. In some examples, 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, an interface and / or a cellular network interface, etc.

[0059] In some embodiments, the device 300 performs one or more processes described herein. The device 300 performs these processes based on software instructions executed by a processor 304 that are stored in a computer-readable medium such as a memory 306 and / or a storage component 308. 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.

[0060] 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 a communication interface 314. When the software instructions stored in the memory 306 and / or the storage component 308 are executed, they cause the processor 304 to perform one or more processes described herein. Additionally or alternatively, instead of software instructions or in combination with software instructions, hardwired circuitry is used to perform one or more processes described herein. Thus, unless otherwise explicitly stated, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0061] The memory 306 and / or the storage component 308 includes a data storage portion or at least one data structure (e.g., a database, etc.). The device 300 is capable of receiving information from the data storage portion or at least one data structure in the memory 306 or the storage component 308, storing the information in the data storage portion or at least one data structure, communicating the information to the data storage portion or at least one data structure, or searching for information stored in the data storage portion or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0062] 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.

[0063] Provide Figure 3 The number and arrangement of the illustrated components are provided as an example. In some embodiments, Figure 3Compared with the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components of device 300 (e.g., one or more than one component) may perform one or more than one function described as being performed by another component or another set of components of device 300.

[0064] Now referring 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 positioning system 406 (sometimes referred to as a positioning 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 positioning system 406, the control system 408, and the database 410 are included in and / or implemented in the automatic navigation system of the vehicle (e.g., the autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more than one independent systems (e.g., one or more than one system the same as or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more than one independent systems located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are 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. 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 the same as or similar to the remote AV system 114, a queue management system the same as or similar to the queue management system 116, and / or a V2I system the same as or similar to the V2I system 118, etc.).

[0065] 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 object by the perception system 402, the perception system 402 transmits data associated with the classification of the physical object to the planning system 404.

[0066] 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 toward 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 object 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.

[0067] 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 connection properties of the road network, a map describing the physical properties of the roadway (such as traffic speed, traffic flow, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various other driving signals, etc.). In some embodiments, the map is generated in real time based on the data received by the perception system.

[0068] 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.

[0069] 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 operational 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.

[0070] 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.).

[0071] 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 (e.g., associated with 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., a country), 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., a single-lane road, a multi-lane road, a highway, a back road, and / or an off-road path, 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.

[0072] 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.

[0073] Now refer to Figure 5 , a diagram illustrating an implementation 500 for simulating the processing of an intelligent pedestrian. In some embodiments, the implementation 500 includes a vehicle 502 that operates based on the outputs generated by an autonomous system 512 and a DBW system 516. In some embodiments, the autonomous system 512 is the same or similar to Figure 2 the autonomous system 202, the control system 514 is the same or similar to Figure 4 the control system 408, and the DBW system 516 is the same or similar to Figure 2 the DBW system 202h. In the example, the control system 514 generates a control signal (504). The autonomous system 512 controls the operation of the vehicle 502 by generating and transmitting a control signal (506) to cause the DBW system 516 to operate.

[0074] In some embodiments, inputs to vehicle 502 are simulated via at least one scenario 510. Scenario 510 includes inputs to vehicle 502 obtained by one or more devices, subsystems, or systems of autonomous system 512 during the simulation. In an example, scenario 510 includes data associated with an environment (such as Figure 1 environment 100, etc.). In an example, the simulation refers to inputting time series data representing the scenario into autonomous system 512. The time series data is, for example, sensor data, data associated with vehicle dynamics, and data associated with pedestrian dynamics, etc. In an example, the time series data includes perception data, sensor data, vehicle dynamics, and environmental data, etc. In an example, the scenario includes data frames, where a frame is a collection of time series data at a specified point in time. In some embodiments, (e.g., during the simulation) the data frames included in the scenario are input into the autonomous system, and the output or response of the autonomous system to the data frames is obtained to inform subsequent data frames in the scenario. The subsequent data frames are executed during the same simulation.

[0075] In the real world, characteristics of the environment (such as objects (e.g., Figure 1 objects 104a - 104n) and other physical properties or events) are represented by data captured by one or more devices of the autonomous system. Output data generated by one or more devices or systems of the autonomous system is used to observe the environment and move through the environment. The devices or systems include, for example, communication devices, autonomous vehicle computing, drive-by-wire (DBW) systems, or safety controllers.

[0076] In the simulation, one or more scenarios 510 (including data associated with the environment) are provided to autonomous system 512 in a controlled environment. The configuration and format of the data included in the scenario are at least partially based on one or more devices or systems of the autonomous system in the tests during the simulation. In an example, during the simulation, the data is input into one or more of a communication device, autonomous vehicle computing, drive-by-wire (DBW) system, and safety controller. The communication device, autonomous vehicle computing, drive-by-wire (DBW) system, and safety controller used during the simulation are respectively the same as or similar to Figure 2 communication device 202e, autonomous vehicle computing 202f, drive-by-wire (DBW) system 202h, and safety controller 202g. Simulation system 520 obtains output 522 from autonomous system 512 in response to scenario 510. In some embodiments, simulation system 520 dynamically and iteratively updates the scenario input into autonomous system 512 based on the output or response 522 of autonomous system 512. The response of one or more devices to the scenario is observed, and one or more devices are iteratively improved or developed based on the evaluation of the response, scenario, or any combination thereof.

[0077] For ease of explanation, specific devices, systems, and subsystems are described as obtaining data during simulation in at least one scenario. However, according to the present technology, any device, system, or subsystem can be used. In an example, the controlled environment associated with the simulation refers to a computational test environment on a server or at a cloud location, in which the software associated with the device, system, or subsystem executes in response to the scenario. An autonomous system executing in the computational test environment can be an "offline" system. In an example, the controlled environment associated with the simulation refers to a physical test environment, in which the software associated with the device, system, and subsystem executes on a vehicle in response to the scenario. An autonomous system executing on the vehicle can be an "online" system.

[0078] Figure 6 The simulation infrastructure 600 is shown. The simulation infrastructure 600 enables the implementation of a simulated intelligent pedestrian. For ease of illustration, an autonomous vehicle is used as the autonomous system 604 to illustrate the present technology. However, according to the present technology, data associated with the environment is generated and iteratively input into any autonomous system. In an example, the simulation enables testing of the autonomous vehicle to ensure that the autonomous vehicle operates in a safe and error-free manner. The simulation infrastructure 600 includes a simulation system 602 and an autonomous system 604. The simulation system 602 is the same as or similar to Figure 5 the simulation system 520. The autonomous system 604 is the same as or similar to Figure 5 the autonomous system 512. As Figure 6 shown, the autonomous system 604 is communicatively coupled to the simulation system 602. In an example, the autonomous system 606 is offline (e.g., not executing on a deployed vehicle).

[0079] The simulation system 602 includes at least one sensor model 612, at least one vehicle dynamics model 614, and at least one pedestrian dynamics model 616. The outputs of the sensor model 612, the vehicle dynamics model 614, and the pedestrian dynamics model 616 are used to update or create at least one scenario 618 (e.g., Figure 5Scenario 510). In some embodiments, the scenario is time series data output by a sensor model 612, a vehicle dynamics model 614, a pedestrian dynamics model 616, or any combination thereof. In an example, the simulation system aggregates the outputs of the sensor model 612, the vehicle dynamics model 614, and the pedestrian dynamics model 616 in a sequence of timestamps. For example, vehicle dynamics data and pedestrian dynamics data are used to constrain sensor data associated with the environment. In an example, the sensor model generates sensor data of the environment based on vehicle dynamics that occur in response to characteristics of the environment. Specific sensor data, such as data from a wheel speed sensor, is simulated according to the vehicle dynamics model or vehicle dynamics data. In an example, the sensor model generates sensor data of the environment based on pedestrian dynamics that occur in response to characteristics of the environment. Specific sensor data, such as data from a camera, is simulated according to the pedestrian dynamics model or pedestrian dynamics data. In this example, the camera data includes the movement of pedestrians across frames of the scenario. In another example, specific sensor data, such as data from LiDAR, is simulated according to the pedestrian dynamics model or pedestrian dynamics data. In this example, the LiDAR data includes point cloud data corresponding to respective pedestrians across frames of the scenario. The aggregated data at respective timestamps forms frames of the scenario.

[0080] The sensor model 612 generates simulated sensor data that is input into the autonomous system 604 during the simulation. For example, the sensor model 612 generates data associated with one or more sensors or devices, such as the camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, and communication device 202e as described above. The sensor model 612 generates sensor data associated with at least one simulated object in the controlled environment. In an example, the sensor model 612 generates sensor data as captured by devices such as a camera, LiDAR sensor, Radar sensor, microphone, communication device, or any combination thereof. In an example, the sensor model 612 generates data as an input to systems including the perception system 402, planning system 404, positioning system 406, control system 408, or database 410 as described above. In some examples, the sensor model 612 generates data as an output of systems including the perception system 402, planning system 404, positioning system 406, control system 408, or database 410 as described above. In an example, the specific type of data generated by the sensor model 612 is at least partially based on the configuration of the autonomous system, where the data generated by the sensor model corresponds to the devices, subsystems, and systems available for simulation. Figure 2 Figure 4 Figure 4

[0081] ​​​The vehicle dynamics model 614 generates data representative of the movement of a vehicle. In an example, vehicle dynamics includes data associated with the movement of an autonomous system. The vehicle dynamics model 614 characterizes how the autonomous system behaves in motion. For example, the vehicle dynamics model 614 generates data output by one or more devices such as a drive-by-wire (DBW) system 202h, a safety controller 202g, a powertrain control system 204, a steering control system 206, and a braking system 208. In an example, during simulation, the output of the autonomous system 604 is obtained and input into the vehicle dynamics model 614. The simulation infrastructure 600 enables simulating vehicle behavior, such as varying steering profiles, acceleration profiles, and tire parameters, in response to the output of the autonomous system 604. Thus, the vehicle dynamics model 614 includes a model that generates an output to the drive-by-wire (DBW) system, a safety controller, a powertrain control system, a steering control system, a braking system, or any combination thereof, taking into account vehicle behavior associated with the autonomous system (e.g., varying steering profiles, acceleration profiles, and tire parameters, etc.). In an example, the vehicle dynamics model 614 mimics the vehicle dynamics associated with a real-world vehicle and iteratively updates the scenario during simulation based on vehicle behavior.

[0082] The pedestrian dynamics model 616 outputs data representative of pedestrian movement. In an example, the pedestrian dynamics model 616 generates data associated with intelligent pedestrians. Intelligent pedestrians are iteratively spawned at the timestamps of the scenario 618. In an example, the intelligent pedestrians are based on at least one behavior model, such as regarding Figure 7AThe above-mentioned social force model, etc., perform in a scenario (e.g., exhibit observable behavior). Inputs to the pedestrian dynamics model 616 include, for example, data associated with an environment that includes physical objects (cars, buses, curbs, cyclists, and / or people, etc.), transportation infrastructure, signals, and signs (e.g., roadways, sidewalks, traffic control lights, traffic control signs), structures (e.g., buildings, signs, fire hydrants, etc.), weather conditions (e.g., temperature, rain, sleet, snow, wind), time of day, terrain or landscape information, or any combination thereof. The pedestrian dynamics model 616 enables the creation of a scenario 618 with pedestrians that interact with the characteristics of the simulated environment. In an example, an intelligent pedestrian is a pedestrian that is aware of the characteristics of the simulated environment and exhibits behavior based on the impact of these characteristics on the corresponding pedestrian. The pedestrian awareness and behavior change in response to the characteristics within the simulated environment. Characteristics of the simulated environment are, for example, other objects (e.g., cars, buses, curbs, and / or people, etc.), transportation infrastructure, signals, and signs (e.g., roadways, sidewalks, traffic control lights, traffic control signs), structures (e.g., buildings, signs, fire hydrants, etc.), weather conditions (e.g., temperature, rain, sleet, snow, wind), time of day, and terrain or landscape information, etc. During the simulation, data associated with the intelligent pedestrian changes in a manner that responds to the characteristics of the simulated environment: the speed, acceleration, travel direction, or other behavior changes output by the pedestrian dynamics model are changed to reflect the pedestrian's reaction to the characteristics (e.g., changes in speed, acceleration, travel direction, or behavior).

[0083] In an example, the simulation system aggregates the outputs of the sensor model 612, the vehicle dynamics model 614, and the pedestrian dynamics model 616 at a sequence of timestamps to form a scenario 618. For example, the sensor model generates sensor data based on the outputs of the vehicle dynamics model 614, the pedestrian dynamics model 616, or any combination thereof. In an example, the sensor model 612, the vehicle dynamics model 614, and the pedestrian dynamics model 616 update their respective outputs during the simulation in response to the outputs of the autonomous system 604. Inserting intelligent pedestrians into the scenario during the simulation enables the development of autonomous system solutions that account for realistic pedestrian behavior without endangering humans and without real-world pedestrian and vehicle collisions. According to the present technology, scenarios including contact and close contact between vehicles and pedestrians (which are not possible in the real world due to the danger to human life that would result in such contact) are realized in the scenario. As described herein, the present technology improves simulation techniques by modeling vehicle and pedestrian interactions.

[0084] Figure 7Ais an illustration of the social force model 700. The social force model 700 responds to forces acting on the intelligent pedestrian 702 and includes other parameters that govern the movement of the pedestrian, such as the path the pedestrian traverses and the speed of the pedestrian. In an example, the social force model 700 describes and models pedestrian behavior as if the pedestrian moves through an environment that is subject to external forces. The modeled pedestrian behavior is based on the optimal movement direction of the intelligent pedestrian from the current location to the target location, taking into account the capabilities of the intelligent pedestrian, while maintaining a level of safety and / or comfort when navigating the environment. In an example, the pedestrian dynamics model (e.g., Figure 6 the pedestrian dynamics model 614) is the same as or similar to Figure 7A the social force model 700. The pedestrian dynamics model is associated with the corresponding pedestrian. In an example, the pedestrian dynamics model outputs the travel direction and speed associated with the corresponding pedestrian at each timestamp of the scenario based on the attributes implemented by the pedestrian dynamics model.

[0085] In Figure 7A the example, the forces include the structural force 704 from structures in the environment, the driving force 706 towards the desired movement direction of the pedestrian, the forces from other pedestrians 708A and 708B, and the force from the vehicle 710. Regarding Figure 7B to illustrate additional forces. The magnitude of the force associated with the pedestrian is based on the corresponding pedestrian model that determines the pedestrian's reaction to the force. In an example, the reaction or behavior of the intelligent pedestrian is determined based on one or more attributes of the corresponding pedestrian dynamics model. Various attributes are used to describe the behavior of the intelligent pedestrian. For example, the comfort attribute defines how the pedestrian behaves considering the difficulty and length of the route. The pedestrian seeks a comfortable route and will move along a route that provides physical ease to reach the corresponding destination as comfortably as possible. However, the pedestrian has to balance physical ease with the length of the route. In an example, the pedestrian takes the shortest possible path to reach the corresponding destination. The physical trust attribute defines the comfort or discomfort of the pedestrian in terms of the interaction with the characteristics of the environment. Regarding Figure 8A to further illustrate physical trust.

[0086] In an example, the undisturbed movement attribute defines how the pedestrian behaves when the movement of the pedestrian along the route is not disturbed. For example, if there is no interruption along the route, the pedestrian will walk towards the desired movement direction at a predetermined rate, which results in the driving force 706 towards the desired movement direction. In an example, the pedestrian attribute defines the influence of other pedestrians on the movement of this pedestrian. In Figure 7AIn the example, the intelligent pedestrian 702 maintains the private domain of the personal space 712. When other pedestrians 708A and 708B enter the domain of the personal space 712, a repulsive force occurs between the intelligent pedestrian 702 and the other pedestrians 708A and 708B. In the example, the structural property defines how the intelligent pedestrian responds to structures 704 (such as buildings, walls, streets, obstacles, etc.). The structure represents a repulsive force that pushes the intelligent pedestrian away from certain structures. In the example, the structure represents an attractive force that pulls the pedestrian closer when moving along the route in the simulation. For example, during adverse weather conditions, pedestrians travel along a route close to the structure to avoid the adverse weather conditions. The group property defines the attraction that occurs between a pedestrian and one or more pedestrians or objects. Pedestrians are sometimes attracted by other people or objects when moving through the environment.

[0087] Figure 7B is an illustration of a pedestrian moving through an environment 720 that is subject to external forces. In Figure 7B the example, the environment is shown at a single frame of the scenario. In the example, the data frame includes sensor data, vehicle dynamics data, and pedestrian dynamics data associated with the objects and features of the environment 720. In some embodiments, the sensor data, vehicle dynamics data, and pedestrian dynamics data associated with the environment are used to iteratively update the scenario at future timestamps. In this way, the scenario is dynamic based on the responses of the pedestrian dynamics model and the vehicle dynamics model during the simulation. For ease of explanation, specific forces are illustrated in Figure 7B However, according to the present technology, any force that affects the movement of a pedestrian due to interpersonal or personal behavior associated with the corresponding pedestrian can be modeled.

[0088] In Figure 7B the example, the pedestrian 731 crosses the street onto the sidewalk 768A. The pedestrian group 732 and the pedestrian group 734 are located on the sidewalk 768A. Additionally, the pedestrian 733 is located on the sidewalk 768A. The pedestrian group 735 uses the crosswalk 760 to cross the street in the direction of the ramp 762B where the sidewalk 768B and the sidewalk 768C meet. The pedestrian 736 is located on the sidewalk 768B near the curb 764B. The pedestrian 737 walks along the sidewalk 768C, and the pedestrian group 738 is located near the traffic light pole 772. The environment 720 also includes vehicles 742, 744, 746, 748, 750, and 752.

[0089] Vehicles 742, 744, 746, 748, 750, and 752 are located along a street that includes a crosswalk 760. Sidewalks 768A and 768B are shown along the street that includes the crosswalk 760. Sidewalk 768A is connected to the street by a curb 764A; sidewalk 768B is connected to the street by a curb 764B. Sidewalk 768C is perpendicular to sidewalk 768B. As Figure 7B shown in the example of, ramp 762A enables easy travel when moving to / from crosswalk 760 across sidewalk 768A. Similarly, ramp 762B enables easy travel when moving to / from crosswalk 760 across sidewalk 768B or 768C. Other features of environment 720 include window signs 766, area lighting 770, traffic lights 772, pedestrian street signals 774, benches 776, trash cans 778, and awnings 780.

[0090] In an example, the output of a pedestrian dynamics model for a corresponding pedestrian is at least partially based on how the pedestrian reacts to one or more classes of forces taking into account a predetermined property. In an example, a force is an influence that can affect the trajectory of a pedestrian. For example, a force causes a pedestrian to change their speed (e.g., accelerate or decelerate) or direction of travel. A force can be counteracted by other forces or properties associated with the corresponding pedestrian dynamics model.

[0091] As Figure 7B shown in the example of, pedestrian 731 jaywalks across the street towards sidewalk 768A. Sidewalk 768A represents an attraction associated with pedestrian 731. Vehicle 746 changes lanes near pedestrian 731 and represents a repulsion associated with pedestrian 731. Additionally, in Figure 7B the example of, a group of pedestrians 732 is located along sidewalk 768A. An attraction is associated with the members of the group of pedestrians 732, where each corresponding pedestrian dynamics model includes a property that describes the likelihood of the members of the group of pedestrians staying near the group. Figure 9 Further describe the forces between groups of pedestrians.

[0092] In Figure 7BIn the example, pedestrian 733 and pedestrian group 734 are located near the fancy and eye-catching window sign 766 and the pedestrian street signal light 774. The window sign 766 and the pedestrian street signal light 774 represent forces that affect pedestrians. The window sign 766 can be an attractive force on pedestrians and pulls the pedestrians closer according to their respective attributes. In the example, the pedestrian street signal light 774 is an attractive force on pedestrians traveling along the route controlled by the pedestrian street signal light 774, such as when the signal light indicates that the pedestrians should stop. In the example, the pedestrian street signal light 774 is a repulsive force on pedestrians traveling along the route controlled by the pedestrian street signal light 774. For example, when pedestrians stop near the pedestrian street signal light 774 and the pedestrian street signal light 774 signals the pedestrians that they can "walk" or "go", the pedestrians near the pedestrian street signal light 774 leave. In the example, when the signal light indicates that the pedestrians can "walk" or "go", pedestrians beyond a threshold distance relative to the pedestrian street signal light 774 are attracted to the pedestrian street signal light 774. In the example, according to the time of day, weather conditions or lighting conditions, the street lamp 770 can generate an attractive force that attracts pedestrians to the lamp.

[0093] As Figure 7B shown in the example, the pedestrian group 735 includes a person and an animal connected by a leash. The force associated with the boundary of the crosswalk 760 is modeled as a wall that encourages pedestrians to travel within the boundary of the crosswalk. The response or behavior of the intelligent pedestrian to the force associated with the boundary of the crosswalk 760 is determined according to one or more attributes of the corresponding pedestrian dynamics model. In Figure 7B the example, the pedestrian 736 is located near the curb 764B. Based on the corresponding attribute, the curb 764B can be associated with an attractive force that attracts the pedestrian to the curb. It is observed that the pedestrian 737 crosses the ramp 762B and continues along the sidewalk 768C. In the example, the ramp 762B represents an attractive force that attracts pedestrians from the sidewalk or crosswalk to the ramp. In Figure 7B the example, the pedestrian group is shown near the awning 780. In the example, the awning 780 is an attractive force for pedestrians traveling along the route under adverse weather conditions. For example, when pedestrians are near the awning 780, the pedestrians can walk or stop under the awning for protection.

[0094] Forces are modeled throughout the environment at each timestamp of the scenario, and the response for each pedestrian is determined at each timestamp of the scenario. For example, the response for each pedestrian is generated by iteratively updating the travel direction and speed of the pedestrian by taking into account the forces that affect the pedestrian. The force is applied to the pedestrian dynamics model for the corresponding pedestrian, and an output is generated. In Figure 7BIn the example, the social force model 700 further includes attributes responsive to forces representative of vehicle and pedestrian interactions. For example, vehicle 742 is associated with a force that is largely based on the speed of the vehicle. The force associated with vehicle and pedestrian interactions is at least partially based on proxemic utility. Proxemic utility refers to the interpersonal distance zones characterizing each pedestrian. In some embodiments, the present technique models the force representative of vehicle and pedestrian interactions based on the proxemic utility associated with the respective pedestrian. The proxemic utility is at least partially defined by attributes of the respective pedestrian dynamics model.

[0095] Figure 8A A plot 801 showing the zones of interpersonal distance. The x-axis represents time t 802, and the y-axis represents the distance d between the pedestrian and the vehicle. In plot 801, the respective pedestrian represented by line 808 is associated with a pedestrian dynamics model that causes the respective pedestrian to exhibit behavior during the simulation in response to at least one force. The pedestrian travels along the length of crosswalk 806, where the length of crosswalk 806 is shown along the x-axis. In the example, the pedestrian associated with line 808 is the same as or similar to Figure 7B pedestrian group 735. In the example, crosswalk 806 is the same as or similar to Figure 7B crosswalk 760. Line 808 represents the movement of the pedestrian across crosswalk 806. The vehicle associated with line 810 approaches across crosswalk 806, where the length of crosswalk 806 spans the street traveled by the vehicle associated with line 810. In the example, the vehicle associated with line 810 is the same as or similar to Figure 7B vehicle 742. Line 810 represents the movement of the vehicle across (e.g., perpendicular to) crosswalk 806.

[0096] In Figure 8AIn the example, the physical trust attributes associated with each respective pedestrian partition the set of possible states of the world (e.g., physical locations) during the vehicle and pedestrian interaction into three sub-spaces. In the collision zone 816, a vehicle and pedestrian collision will occur, and neither the vehicle nor the pedestrian can prevent the vehicle and pedestrian collision. In the trust zone 814, a vehicle and pedestrian collision may occur, however, the vehicle or the pedestrian can take actions to prevent the vehicle and pedestrian collision. In the escape zone 812, a vehicle and pedestrian collision may occur, but the pedestrian can take actions to prevent the vehicle and pedestrian collision without trusting the vehicle. As the distance d between the pedestrian 808 and the vehicle 810 decreases, the physical trust attributes associated with the pedestrian 808 transition from the escape zone 812 to the trust zone 814, and to the collision zone 816. The response of the pedestrian during the simulation changes depending on whether the pedestrian is located in the escape zone 812, the trust zone 814, or the collision zone 816.

[0097] In Figure 8A In the example of the plot 801 in, it is assumed that the vehicle 810 is approaching the crosswalk 806 where the pedestrian 808 is located. However, the pedestrian can interact with the front, rear, or side areas of the vehicle. The locations of the escape zone 812, the trust zone 814, and the collision zone 816 are based on the heading and speed of the vehicle relative to the pedestrian. In the example, when the vehicle 810 is moving at a low rate, the force caused by the vehicle varies based on the position of the intelligent pedestrian relative to the vehicle. When moving forward, the front end of the vehicle is associated with the influence zone. As the vehicle speed (e.g., longitudinal speed) increases, a longer influence zone is created in front of the vehicle. In the escape zone 812, due to the vehicle's speed, the distance between the pedestrian and the vehicle seems to provide enough space for the pedestrian to escape, so the pedestrian is most comfortable at the front end of the vehicle. In the trust zone 814, due to the vehicle's speed, the distance between the pedestrian and the vehicle may not provide enough space for the pedestrian to avoid a collision with the vehicle, so the pedestrian at the front end of the vehicle shows trust in the vehicle. In the collision zone 816, due to the distance between the pedestrian and the vehicle may not provide enough space for the pedestrian to avoid a collision with the vehicle, so the pedestrian at the front end of the vehicle anticipates a collision with the vehicle based on the vehicle's speed.

[0098] In Figure 8AIn the example, the plot 821 shows zones based on the variation of the speed of the vehicle. The speed of the vehicle is shown along the x-axis 822, and the distance is shown along the y-axis 824. In the example, the vehicle is the same as or similar to vehicle 810. The escape zone 826, the trust zone 828, and the collision zone 830 are shown as functions of the vehicle speed and the distance to the pedestrian. In the example, when the vehicle approaches the pedestrian, the vehicle induces a repulsive force that affects the intelligent pedestrian, where the repulsive force increases as the speed of the vehicle increases. In the scenario where the vehicle and the pedestrian are approaching contact, the repulsive force generated by the vehicle changes to a direction perpendicular to the direction of motion of the car, such that the pedestrian slides away from the path of the vehicle rather than being pushed off course by the vehicle.

[0099] Figure 8B Shows active metrics associated with a safety assessment of a simulation that includes a pedestrian dynamics model based on a social force model. In the example, the system in the assessment is an autonomous system, such as Figure 6 autonomous system 604 and the like. In Figure 8B the example, the vehicle shown includes an autonomous system, and the assessment of the vehicle or vehicle metrics refers to the assessment of the autonomous system. In the example, the vehicle metrics used for the assessment are based on the influence zone shown at reference numeral 830. The pedestrian metrics used for the assessment are based on the pedestrian zone shown at reference numeral 840. Active vehicle movement is achieved by defining the influence zone as a 180-degree zone in the same orientation as the vehicle. The radius of the influence zone is proportional to the linear speed of the vehicle, which results in a greater margin of influence at higher speeds. In the example, the influence zone defines the area where vehicle-pedestrian interaction occurs.

[0100] In Figure 8B , the influence zone of the vehicle is shown at varying speeds, where α ∈ R + . At reference numerals 832, 834, 836, and 838, the influence zone associated with the vehicle varies according to the respective speed and orientation / course of the vehicle. Similarly, the pedestrian zone shown at reference numeral 840 includes a personal zone 846 and a cooperation zone 848. The personal zone 846 is defined by the personal zone radius 844. The cooperation zone 848 is defined by the cooperation zone radius 842. In the example, the personal zone 846 is the space around the pedestrian where any intrusion causes discomfort. In the example, the cooperation zone 848 is the space around the pedestrian where cooperation between the vehicle and the pedestrian can occur without discomfort. For ease of illustration, the zones as described herein are shown using a specific shape, however any shape can be used according to the present technology.

[0101] When a pedestrian tends to clear the personal zone to avoid intrusion by others, (s)he tends to clear the cooperation zone of any vehicle intrusion.

[0102] In an example, safety is evaluated at least in part based on the average number of personal zone violations and cooperative zone violations performed at various rates by a simulated autonomous system in a given scenario. For example, a safety index is determined within an influence zone to ensure pedestrian safety while invoking more cooperative pedestrian behavior. The pedestrian zone is used to evaluate the safety index. In an example, violating (e.g., entering) a pedestrian's personal zone is a failed navigation; entering a cooperative zone with SI < 1 is a possible pedestrian discomfort. A larger SI value equals better navigation. In an example, the safety index is calculated as follows:

[0103]

[0104] where D j is the minimum distance j between pedestrian j and the vehicle body; R S is the radius of the personal zone; and R C is the radius of the cooperative zone. In this way, the present technology enables scenarios including contact and close contact between a vehicle and a pedestrian to be simulated and evaluated. In an example, an autonomous system is trained, updated, modified, or developed based on the results of the simulation.

[0105] Figure 9 Shows a simulation of an intelligent pedestrian taking into account a group of pedestrians or other pedestrians. In an example, Figure 9 the intelligent pedestrian shown adjusts the corresponding path and / or speed considering social force parameters based on the proximity of other objects, traffic infrastructure / signals / signs, structures, weather conditions, time of day, terrain, landscape, or any combination thereof. In some embodiments, the simulation infrastructure (e.g., Figure 6 the simulation infrastructure 600) enables a user to assign a social force model with predetermined parameters as pedestrian behavior. The parameters include, for example, pedestrian groups, pedestrian types, and pedestrian obstacles. At reference numeral 902, a single pedestrian is shown moving in a random direction in the environment. In the example at reference numeral 902, there is no pedestrian group. This may occur, for example, when simulating pedestrians commuting to work. During the morning rush hour, pedestrians tend to walk alone when going to their workplaces. During the morning rush hour, pedestrians also tend to walk alone when leaving their workplaces. At reference numeral 904, a dense group of single pedestrians is shown moving in substantially the same direction in the environment. In the example at reference numeral 904, there is no pedestrian group. For example, this may occur, for example, in a large crowd going to an upcoming event such as a concert, a sports event, or other attractions.

[0106] At reference numeral 906, a group of pedestrians is shown moving in the same direction in the environment. In the example at reference numeral 906, the group of pedestrians occupies more physical space. At reference numeral 908, a group of pedestrians is shown moving in different directions in the environment. In the example at reference numeral 908, the group of pedestrians consumes less physical space when compared to the group of pedestrians at reference numeral 906. In some embodiments, the attributes of each respective pedestrian in the group of pedestrians are determined by a respective pedestrian dynamics model.

[0107] In an example, when compared to individual pedestrians, multiple pedestrians exhibit more confident behavior when interacting with a vehicle. When specifying the scenario for the simulation, pedestrians can be assigned a classification, for example, to form groups of pedestrians. In an example, the classification is used to determine the attraction towards other pedestrians having the same class. For example, pedestrian classes include groups of pedestrians such as couples (e.g., a group of 2), friends (e.g., a group of 2 or more), family members (e.g., a group of 2 or more), and colleagues (e.g., a group of 2 or more), etc. In an example, a pedestrian is assigned a pedestrian type (e.g., adult, child, elderly), a pedestrian purpose (e.g., work, leisure), or a pedestrian obstacle, disability, or impairment status. In an example, the pedestrian classification, pedestrian type, pedestrian purpose, pedestrian obstacle, or any combination thereof is assigned to each respective pedestrian based on the test objective (including the behavior of the autonomous system in the test). Additionally, in an example, the pedestrian classification, pedestrian type, pedestrian purpose, pedestrian obstacle, or any combination thereof is assigned to achieve a distribution of agents across classes.

[0108] In some embodiments, visual attributes associated with each respective pedestrian are defined in the pedestrian dynamics model by specifying a perspective and a distance associated with the simulated pedestrian. In an example, the visual attributes govern how an intelligent pedestrian perceives an autonomous vehicle and other pedestrians during the simulation. The pedestrian reacts to the vehicle by adjusting its speed and / or direction of travel based on the time to collision between the pedestrian and the vehicle and the agent's danger radius and risk radius (i.e., the personal zone and the cooperation zone). For example, the pedestrian stops, decelerates, accelerates, and retreats (i.e., moves backward) in response to the vehicle. The time to collision parameter, the danger radius parameter, and the risk radius parameter are adjustable attributes of the social force model. In an example, when the vehicle has the same speed as the pedestrian, the simulated pedestrian behaves as if the vehicle were a pedestrian (i.e., the zone matches the interpersonal distance of person-to-person interaction). In some embodiments, the pedestrian behavior is defined as complying with / ignoring traffic light signals. Additionally, in an embodiment, the speed and start / end pose of the pedestrian are manually defined before the simulation.

[0109] Now refer to Figure 10 , a flowchart of a first process 1000 for simulating intelligent pedestrians is illustrated. In some embodiments, one or more of the steps described with respect to process 1000 are performed byFigure 6 with the simulation infrastructure 600 (e.g., fully and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more steps described with respect to the processing 600 are performed by another device or group of devices separate from or including the simulation infrastructure 600 (e.g., Figure 3 device 300) (e.g., fully and / or partially, etc.).

[0110] At block 1002, develop vehicle behavior, where the vehicle behavior is a function or ability that an expected vehicle (e.g., an autonomous vehicle) will perform. In an example, the expected vehicle will perform the behavior while ensuring the safety of pedestrians.

[0111] At block 1004, select at least one scenario. In an example, select or specify at least one scenario such that during the simulation of the scenario (e.g., the simulation of the scenario during the testing, validation, or verification of an AV), the vehicle should exhibit the developed behavior.

[0112] At block 1006, select the starting and ending postures of the pedestrian.

[0113] At block 1008, construct a social force model that governs pedestrian behavior as the pedestrian traverses a path from the starting posture to the ending posture.

[0114] At block 1010, simulate pedestrian behavior according to the social force model in at least one scenario, where in the scenario the pedestrian reacts to the simulated vehicle. For example, adjust the pedestrian speed and / or travel direction based on the social force model. The social force model includes pedestrian attribute adjustable parameters based on the time to collision between the pedestrian and the vehicle and the pedestrian's danger radius and risk radius (i.e., personal zone and cooperation zone). In an example, the data associated with the intelligent pedestrian changes in such a way that the pedestrian's speed, acceleration, or travel direction changes to reflect the pedestrian's reaction to the characteristics of the simulated environment (e.g., a change in behavior).

[0115] At block 1012, evaluate the performance of the behavior made by the vehicle during the simulation. In some embodiments, compare the performance of the behavior with the expected behavior or known standards to determine whether the performance of the behavior made by the vehicle is satisfactory. Additionally, in some embodiments, iteratively evaluate and refine the performance of the behavior made by the vehicle during the simulation until the performance is satisfactory. For example, refine, update, and evaluate the vehicle behavior considering scenarios including intelligent pedestrians until the performance of the behavior is satisfactory.

[0116] Now refer to Figure 11, a flowchart illustrating a second process 1100 for simulating intelligent pedestrians. In some embodiments, one or more of the steps described with respect to process 1100 are performed by Figure 6 the simulation infrastructure 600 (e.g., fully and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more of the steps described with respect to process 600 are performed by another device or group of devices separate from or including the simulation infrastructure 600 (e.g., Figure 3 device 300)(e.g., fully and / or partially, etc.).

[0117] At block 1102, properties of at least one pedestrian dynamics model are specified. In some embodiments, at least one pedestrian dynamics model is a social force model. In a social force model, the properties describe pedestrian behavior in response to external forces in the environment. These properties govern the behavior of the corresponding pedestrians in response to the characteristics of the environment. In an example, at least one pedestrian dynamics model outputs a travel direction and speed associated with a corresponding pedestrian at each timestamp of a scenario.

[0118] At block 1104, simulated sensor data associated with the environment is generated. The simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model.

[0119] At block 1106, the operation of an autonomous system in the environment is simulated based on the simulated sensor data associated with the environment. Vehicle and pedestrian interactions are modeled by at least one pedestrian dynamics model as external forces in the environment that affect the behavior of the corresponding pedestrians. In an example, vehicle and pedestrian interactions are defined according to spatial relational effects. Additionally, in an example, at least one vehicle dynamics model and at least one pedestrian dynamics model are iteratively executed during the simulation to generate the simulated sensor data. In an example, at least one vehicle dynamics model and at least one pedestrian dynamics model are iteratively executed during the simulation to generate the simulated sensor data in response to the output of the autonomous system.

[0120] In some embodiments, the output or response of an autonomous system is evaluated during a simulation within an influence zone. In an example, the influence zone varies based on the speed associated with the autonomous system during the simulation. A safety index associated with the autonomous system is at least partially based on the average number of personal zone violations and cooperative zone violations performed by the simulated autonomous system at various rates in a given scenario. In an example, entering a pedestrian's personal zone represents a failure to achieve a safe operation. The present technology enables scenarios including collisions and near collisions between a vehicle and a pedestrian. Including realistic pedestrian behavior in the scenarios used in the simulation improves the quality of the information learned from the simulation. Robust autonomous systems are further developed and / or tested based on the information of such quality.

[0121] Clause

[0122] According to some non - limiting embodiments or examples, a method is provided, comprising: using at least one processor, obtaining attributes of at least one pedestrian dynamics model, wherein the attributes govern the behavior of a simulated pedestrian in response to characteristics of an environment; using the at least one processor, generating simulated sensor data associated with the environment, wherein the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and using the at least one processor, simulating the operation of an autonomous vehicle in the environment based on the simulated sensor data, wherein the at least one pedestrian dynamics model is used to model the interaction between the vehicle and the pedestrian as an external force in the environment that affects the behavior of the simulated pedestrian.

[0123] According to some non - limiting embodiments or examples, a system is provided, comprising: at least one processor, and at least one non - transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: obtain attributes of at least one pedestrian dynamics model, wherein the attributes govern the behavior of a simulated pedestrian in response to characteristics of an environment; generate simulated sensor data associated with the environment, wherein the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and simulate the operation of an autonomous vehicle in the environment based on the simulated sensor data, wherein the at least one pedestrian dynamics model is used to model the interaction between the vehicle and the pedestrian as an external force in the environment that affects the behavior of the simulated pedestrian.

[0124] 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: obtain attributes of at least one pedestrian dynamics model, where the attributes govern the behavior of simulated pedestrians in response to characteristics of the environment; generate simulated sensor data associated with the environment, where the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and simulate the operation of an autonomous vehicle in the environment based on the simulated sensor data, where the at least one pedestrian dynamics model models the interaction between the vehicle and pedestrians as external forces in the environment that affect the behavior of the simulated pedestrians.

[0125] Further non - limiting aspects or embodiments are set forth in the following numbered clauses:

[0126] Clause 1: A method, comprising: using at least one processor, obtaining attributes of at least one pedestrian dynamics model, where the attributes govern the behavior of simulated pedestrians in response to characteristics of the environment; using the at least one processor, generating simulated sensor data associated with the environment, where the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and using the at least one processor, simulating the operation of an autonomous vehicle in the environment based on the simulated sensor data, where the at least one pedestrian dynamics model models the interaction between the vehicle and pedestrians as external forces in the environment that affect the behavior of the simulated pedestrians.

[0127] Clause 2: The method according to Clause 1, wherein the at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during the simulation to generate simulated sensor data associated with the environment.

[0128] Clause 3: The method according to Clause 1 or 2, wherein the at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed in response to the output of the autonomous vehicle during the simulation.

[0129] Clause 4: The method according to any one of Clauses 1 to 3, wherein the at least one pedestrian dynamics model includes a social force model.

[0130] Clause 5: The method according to any one of Clauses 1 to 4, wherein the attributes describe pedestrian behavior in response to the external forces in the environment.

[0131] Clause 6: The method according to any one of Clauses 1 to 5, wherein the at least one pedestrian dynamics model outputs, at each time stamp of the scenario during the simulation, a travel direction and a speed associated with the simulated pedestrian.

[0132] Clause 7: The method according to any one of Clauses 1 to 6, wherein the vehicle and pedestrian interaction is defined according to spatial relational utility.

[0133] Clause 8: The method according to any one of Clauses 1 to 7, comprising: evaluating, within an impact zone for evaluation that is an area where vehicle and pedestrian interaction occurs, a response of the autonomous vehicle to the simulated sensor data.

[0134] Clause 9: A system, comprising: at least one processor, and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: obtain attributes of at least one pedestrian dynamics model, wherein the attributes govern the behavior of a simulated pedestrian in response to characteristics of the environment; generate simulated sensor data associated with the environment, wherein the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and simulate the operation of an autonomous vehicle in the environment based on the simulated sensor data, wherein the vehicle and pedestrian interaction is modeled using the at least one pedestrian dynamics model as an external force in the environment that affects the behavior of the simulated pedestrian.

[0135] Clause 10: The system according to Clause 9, wherein the at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during the simulation to generate simulated sensor data associated with the environment.

[0136] Clause 11: The system according to Clause 9 or 10, wherein the at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during the simulation in response to an output of the autonomous vehicle.

[0137] Clause 12: The system according to any one of Clauses 9 to 11, wherein the at least one pedestrian dynamics model includes a social force model.

[0138] Clause 13: The system according to any one of Clauses 9 to 12, wherein the attributes describe pedestrian behavior in response to the external forces in the environment.

[0139] Clause 14: The system according to any one of Clauses 9 to 13, wherein the at least one pedestrian dynamics model outputs a travel direction and speed associated with the simulated pedestrian at each time stamp of the scenario during the simulation.

[0140] Clause 15: The system according to any one of Clauses 9 to 14, wherein the vehicle and pedestrian interaction is defined according to spatial relation utility.

[0141] Clause 16: At least one non-transitory computer-readable medium comprising one or more instructions that, when executed by at least one processor, cause the at least one processor to: obtain attributes of at least one pedestrian dynamics model, where the attributes govern the behavior of the simulated pedestrian in response to characteristics of the environment; generate simulated sensor data associated with the environment, where the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and simulate the operation of an autonomous vehicle in the environment based on the simulated sensor data, where the vehicle and pedestrian interaction is modeled using the at least one pedestrian dynamics model as an external force in the environment that affects the behavior of the simulated pedestrian.

[0142] Clause 17: The at least one non-transitory storage medium according to Clause 16, wherein the at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during the simulation to generate simulated sensor data associated with the environment.

[0143] Clause 18: The at least one non-transitory storage medium according to Clause 16 or 17, wherein the at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed in response to an output of the autonomous vehicle during the simulation.

[0144] Clause 19: The at least one non-transitory storage medium according to any one of Clauses 16 to 18, wherein the at least one pedestrian dynamics model includes a social force model.

[0145] Clause 20: The at least one non-transitory storage medium according to any one of Clauses 16 to 19, wherein the vehicle and pedestrian interaction is defined according to spatial relation utility.

[0146] Industrial Applicability

[0147] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details, which may vary depending on the 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 that are 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 controlling as to the meaning of such terms as used in the claims. Additionally, when the term "further comprises" is used in the foregoing specification or the appended claims, the text following such phrase may be additional steps or entities, or sub-steps / sub-entities of the previously recited steps or entities.

Claims

1. A method, comprising: obtaining, by using at least one processor, attributes of at least one pedestrian dynamics model, wherein the attributes govern the behavior of simulated pedestrians in response to characteristics of an environment; generating, by using the at least one processor, simulated sensor data associated with the environment, wherein the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and simulating, by using the at least one processor, the operation of an autonomous vehicle in the environment based on the simulated sensor data, wherein the at least one pedestrian dynamics model is used to model vehicle-pedestrian interactions as external forces in the environment that affect the behavior of the simulated pedestrians.

2. The method according to claim 1, wherein, The at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during simulation to generate simulated sensor data associated with the environment.

3. The method according to claim 1 or 2, wherein The at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during simulation in response to an output of the autonomous vehicle.

4. The method according to any one of claims 1 to 3, wherein The at least one pedestrian dynamics model includes a social force model.

5. The method according to any one of claims 1 to 4, wherein, The attributes describe pedestrian behavior in response to the external forces in the environment.

6. The method according to any one of claims 1 to 5, wherein The at least one pedestrian dynamics model outputs a travel direction and speed associated with the simulated pedestrians at each timestamp of a scenario during simulation.

7. The method according to any one of claims 1 to 6, wherein The vehicle-pedestrian interactions are defined according to spatial relational utility.

8. The method according to any one of claims 1 to 7, comprising: Evaluating, within an impact zone for evaluation that is an area where vehicle-pedestrian interactions occur, the response of the autonomous vehicle to the simulated sensor data.

9. A system, comprising: at least one processor, and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: obtain attributes of at least one pedestrian dynamics model, wherein the attributes govern the behavior of simulated pedestrians in response to characteristics of an environment; generate simulated sensor data associated with the environment, wherein the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and simulate the operation of an autonomous vehicle in the environment based on the simulated sensor data, wherein the at least one pedestrian dynamics model is used to model vehicle-pedestrian interactions as external forces in the environment that affect the behavior of the simulated pedestrians.

10. The system according to claim 9, wherein, The at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during simulation to generate simulated sensor data associated with the environment.

11. The system according to claim 9 or 10, wherein, The at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during simulation in response to an output of the autonomous vehicle.

12. The system according to any one of claims 9 to 11, wherein, The at least one pedestrian dynamics model includes a social force model.

13. The system according to any one of claims 9 to 12, wherein The attribute describes pedestrian behavior in response to the external force in the environment.

14. The system according to any one of claims 9 to 13, wherein The at least one pedestrian dynamics model outputs a travel direction and speed associated with the simulated pedestrian at each timestamp of the scenario during the simulation.

15. The system according to any one of claims 9 to 14, wherein, The vehicle and pedestrian interaction is defined according to spatial relation utility.

16. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to: Obtain attributes of at least one pedestrian dynamics model, where the attributes govern the behavior of the simulated pedestrian in response to characteristics of the environment; Generate simulated sensor data associated with the environment, where the simulated sensor data includes aggregated data from at least one sensor model, at least one vehicle dynamics model, and at least one pedestrian dynamics model; and Simulate the operation of an autonomous vehicle in the environment based on the simulated sensor data, where the vehicle and pedestrian interaction is modeled using the at least one pedestrian dynamics model as an external force in the environment that affects the behavior of the simulated pedestrian.

17. The at least one non-transitory storage medium according to claim 16, wherein, The at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during the simulation to generate simulated sensor data associated with the environment.

18. The at least one non-transitory storage medium according to claim 16 or 17, wherein, The at least one sensor model, the at least one vehicle dynamics model, and the at least one pedestrian dynamics model are iteratively executed during the simulation in response to the output of the autonomous vehicle.

19. The at least one non-transitory storage medium according to any one of claims 16 to 18, wherein, The at least one pedestrian dynamics model includes a social force model.

20. The at least one non-transitory storage medium according to any one of claims 16 to 19, wherein, The vehicle and pedestrian interaction is defined according to spatial relation utility.

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