Infrastructure access control using vehicle-based lidar

Abstract

Examples described herein provide a method of infrastructure access control using a vehicle-based lidar. The method includes detecting an infrastructure object in an environment in which the vehicle is operating based at least in part on lidar data collected by a lidar device of the vehicle. The method also includes transmitting a customized lidar scan pattern associated with the infrastructure object, the customized lidar scan pattern being received by a receiver of the infrastructure object and causing the infrastructure object to implement an action. The method also includes autonomously controlling the vehicle to navigate the vehicle relative to the infrastructure object in response to the infrastructure object performing the action.

Description

Technical Field

[0001] This topic relates to vehicles, and more specifically to infrastructure access control using vehicle-based LiDAR. Background Technology

[0002] Modern vehicles (e.g., cars, motorcycles, boats, or any other type of vehicle) may be equipped with one or more cameras that provide reversing assistance, capture images of the vehicle's driver to determine driver drowsiness or attention, provide images of the road while the vehicle is in motion for collision avoidance purposes, provide structure recognition (e.g., road signs, etc.), and include combinations and / or multiple cameras. For example, a vehicle may be equipped with multiple cameras, and images from multiple cameras (referred to as "surround view cameras") can be used to create a "surround" or "bird's-eye view" view of the vehicle. Some of the cameras (referred to as "remote cameras") may be used to capture remote images (e.g., object detection for collision avoidance, structure recognition, etc.).

[0003] Such vehicles can also be equipped with sensors for perception tasks, such as radar devices, lidar devices, and / or the like. Lidar (light detection and ranging) involves using light (e.g., pulsed laser) to measure the distance to an object by emitting laser pulses, detecting the reflection of the emitted laser pulses (e.g., the reflection from the object), and measuring the time between emission and detection. The measured time can be used to determine the distance between the lidar device and the detected object. Perception tasks can include one or more of object detection, classification, tracking, lane detection, road sign recognition, and obstacle avoidance. Perception tasks are particularly useful for autonomous vehicles to provide them with real-time awareness of their environment to make safe and informed driving decisions. Images from one or more cameras on the vehicle can also be used to detect objects, track targets, etc., including combinations and / or multiple combinations thereof. Summary of the Invention

[0004] In one embodiment, a computer-implemented method for infrastructure access control using vehicle-based LiDAR is provided. The method includes detecting infrastructure objects in an environment in which the vehicle is operating, based at least in part on LiDAR data collected by the vehicle's LiDAR equipment. The method also includes transmitting a customized LiDAR scan pattern associated with the infrastructure object, the customized LiDAR scan pattern being received by a receiver on the infrastructure object and causing the infrastructure object to perform an action. The method further includes autonomously controlling the vehicle to navigate relative to the infrastructure object in response to the infrastructure object performing an action.

[0005] In addition to one or more features described herein, or as an alternative, another implementation of the method may include an infrastructure object being a passageway door, and wherein the action is to open the passageway door.

[0006] In addition to one or more features described herein, or as an alternative, other embodiments of the method may include determining whether the infrastructure object has successfully performed an action after emitting a customized lidar scanning pattern associated with the infrastructure object and before autonomously controlling the vehicle.

[0007] In addition to one or more features described herein, or as an alternative, other embodiments of the method may include: autonomously controlling the vehicle in response to determining that the infrastructure object has successfully performed an action.

[0008] In addition to one or more features described herein, or as an alternative, other embodiments of the method may include: in response to determining that the infrastructure object has failed to perform an action, initiating a remote system to acquire an image of the infrastructure object while emitting a customized LiDAR scanning pattern, receiving the image of the infrastructure object from the remote system, verifying that the brightness changes in the image of the infrastructure object match the expected brightness changes based on the customized LiDAR scanning pattern of the infrastructure object, and in response to verifying that the brightness changes in the image of the infrastructure object match the expected brightness changes, causing the infrastructure object to perform an action by the remote system.

[0009] In addition to one or more features described herein, or alternatively, other embodiments of the method may include emitting a custom lidar scanning pattern associated with an infrastructure object, including: acquiring a point cloud of the infrastructure object by a lidar device using a standard lidar scanning pattern; normalizing the standard lidar scanning pattern based on the position of a vehicle relative to the location of the infrastructure object; selecting a custom lidar scanning pattern from a plurality of custom lidar scanning patterns based on the infrastructure object; and causing the lidar device to emit the custom lidar scanning pattern.

[0010] In addition to one or more features described herein, or as an alternative, other embodiments of the method may include enabling infrastructure objects to act in response to a custom lidar scanning pattern matching an expected custom lidar scanning pattern.

[0011] In addition to one or more of the features described herein, or as an alternative, other implementations of the method may include a customized lidar scanning pattern defined by a customized frequency.

[0012] In addition to one or more of the features described herein, or as an alternative, further implementations of the method may include a customized lidar scanning mode defined by a customized lidar pulse sequence.

[0013] In another embodiment, a vehicle is provided. The vehicle includes a driver monitoring system with a camera. The vehicle also includes a lidar device and a processing system for infrastructure access control using vehicle-based lidar. The processing system includes a memory with computer-readable instructions and processing means for executing the computer-readable instructions, which control the processing system to perform operations. The operations include detecting infrastructure objects in an environment in which the vehicle is operating, based at least in part on lidar data collected by the vehicle's lidar device. The operations also include using an image of the vehicle's operator captured by a camera to determine whether the vehicle's operator is an authorized operator. The operations further include: in response to determining that the vehicle's operator is an authorized operator, transmitting a customized lidar scanning pattern associated with the infrastructure object, the customized lidar scanning pattern being received by a receiver of the infrastructure object and causing the infrastructure object to perform an action. The operations also include: in response to the infrastructure object performing an action, autonomously controlling the vehicle to navigate relative to the infrastructure object.

[0014] In addition to one or more of the features described herein, or as an alternative, other embodiments of the vehicle may include: the camera being a first camera, and the vehicle further including a second camera.

[0015] In addition to one or more features described herein, or alternatively, other embodiments of the vehicle may include a Global Positioning System (GPS) device, and the operation may also include determining the location of infrastructure objects based at least in part on information received from the GPS device.

[0016] In addition to one or more features described herein, or alternatively, other embodiments of the vehicle may include: emitting a custom lidar scanning pattern associated with the infrastructure object, at least in part based on the location of the infrastructure object.

[0017] In addition to one or more features described herein, or as an alternative, another implementation of the vehicle may include: the operation further includes: generating an alarm in response to determining that the operator of the vehicle is not the authorized operator of the vehicle.

[0018] In addition to one or more features described herein, or alternatively, other embodiments of the vehicle may include emitting a custom lidar scanning pattern associated with an infrastructure object, including: acquiring a point cloud of the infrastructure object by a lidar device using a standard lidar scanning pattern; normalizing the standard lidar scanning pattern based on the vehicle's position relative to the location of the infrastructure object; selecting a custom lidar scanning pattern from a plurality of custom lidar scanning patterns based on the infrastructure object; and causing the lidar device to emit the custom lidar scanning pattern.

[0019] In addition to one or more features described herein, or alternatively, other embodiments of the vehicle may include: the operation further includes determining whether the infrastructure object has successfully performed an action after emitting a custom lidar scanning pattern associated with the infrastructure object and before autonomously controlling the vehicle.

[0020] In addition to one or more features described herein, or alternatively, other embodiments of the vehicle may include: autonomously controlling the vehicle in response to determining that an infrastructure object has successfully performed an action.

[0021] In addition to one or more features described herein, or alternatively, other embodiments of the vehicle may include: the operation further includes: in response to determining that the infrastructure object has failed to perform an action, activating a remote system to acquire an image of the infrastructure object while emitting a customized lidar scanning pattern, receiving the image of the infrastructure object from the remote system, verifying that the brightness change in the image of the infrastructure object matches the expected brightness change based on the customized lidar scanning pattern of the infrastructure object, and in response to verifying that the brightness change in the image of the infrastructure object matches the expected brightness change, causing the infrastructure object to perform an action by the remote system.

[0022] In another embodiment, a computer program product is provided. The computer program product includes a collection of one or more computer-readable storage media and program instructions co-stored in the collection of one or more storage media for causing a set of processors to perform computer operations for infrastructure access control using vehicle-based LiDAR. The computer operations include detecting infrastructure objects in an environment in which the vehicle is operating, based at least in part on LiDAR data collected by the vehicle's LiDAR equipment. The operations also include transmitting a customized LiDAR scan pattern associated with the infrastructure object, the customized LiDAR scan pattern being received by a receiver on the infrastructure object and causing the infrastructure object to perform an action. The operations further include autonomously controlling the vehicle to navigate relative to the infrastructure object in response to the infrastructure object performing an action.

[0023] In addition to one or more features described herein, or as an alternative, other embodiments of the computer program product may include emitting a custom lidar scanning pattern associated with an infrastructure object, including: acquiring a point cloud of the infrastructure object by a lidar device using a standard lidar scanning pattern; normalizing the standard lidar scanning pattern based on the position of a vehicle relative to the infrastructure object; selecting a custom lidar scanning pattern from a plurality of custom lidar scanning patterns based on the infrastructure object; and causing the lidar device to emit the custom lidar scanning pattern.

[0024] The above-described features and advantages, as well as other features and advantages, of this disclosure will become apparent when taken in conjunction with the accompanying drawings and the following detailed description. Attached Figure Description

[0025] Other features, advantages, and details appear by way of example only in the following detailed description, which is described in detail with reference to the accompanying drawings, wherein:

[0026] Figure 1 A vehicle with a processing system and sensors according to one or more embodiments is shown;

[0027] Figure 2 The illustration is based on one or more embodiments. Figure 1 The processing system;

[0028] Figure 3 A flowchart is shown for a method of infrastructure access control using vehicle-based lidar according to one or more embodiments;

[0029] Figure 4 A flowchart is shown for a method of infrastructure access control using vehicle-based lidar according to one or more embodiments;

[0030] Figure 5A A block diagram depicts a standard lidar scanning mode of a lidar device according to one or more embodiments;

[0031] Figure 5B A block diagram depicts a customized lidar scanning pattern for a lidar device according to one or more embodiments; and

[0032] Figure 6 A block diagram of an impedance balancing processing system for a battery monitoring circuit for a vehicle, according to one or more embodiments, is shown. Detailed Implementation

[0033] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or use. It should be understood that throughout the drawings, corresponding reference numerals denote the same or corresponding parts and features. As used herein, the term "module" refers to processing circuitry that may include application-specific integrated circuits (ASICs), electronic circuitry, processor (shared, dedicated, or group) and memory executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.

[0034] One or more embodiments described herein relate to infrastructure access control using vehicle-based lidar.

[0035] Infrastructure access control refers to the systems, devices, and processes used to regulate and manage the entry and exit of vehicles or individuals into secure or restricted areas. This involves using various technologies and methods, such as authentication, automated doors, and security protocols, to ensure that authorized entities can access specific infrastructure objects, such as toll roads, border crossings, gated communities, and security facilities, while restricting access to unauthorized entities. The goals of infrastructure access control are to enhance security, improve operational efficiency, and provide real-time monitoring and management of access points.

[0036] Modern infrastructure access control systems typically rely on physical security objects (called "infrastructure objects"), such as gates, toll booths, and border crossings, to regulate vehicle entry. These systems usually require drivers to interact with physical devices, such as RFID tags, badges, or manual input systems, to gain access. This interaction is cumbersome and time-consuming, especially for drivers facing the challenge of leaving their vehicles or for fleet operations requiring efficient and secure access control.

[0037] Existing solutions for infrastructure access control have several drawbacks. Physical security elements typically involve manual intervention, which can lead to delays and inefficiencies, especially in high-traffic areas. Furthermore, reliance on physical devices such as RFID tags or badges introduces the risk of loss, theft, or damage, compromising security. Additionally, these systems may not provide real-time monitoring and verification of driver identity, resulting in potential unauthorized access and security vulnerabilities.

[0038] One or more embodiments described herein address these and other problems by leveraging advancements in LiDAR technology to enable real-time, customized scanning patterns for infrastructure access control. One or more embodiments dynamically customize and deploy customized LiDAR scanning patterns. This method enhances security and accessibility by allowing vehicle identification through customizable scanning patterns observed by receivers associated with infrastructure objects such as gates and / or toll roads. One or more embodiments provide continuous fleet monitoring and logistics management, thereby ensuring secure and efficient access to infrastructure without requiring additional physical installations.

[0039] Figure 1A vehicle 100 with a processing system 102 and sensors 104 is shown according to one or more embodiments. The vehicle 100 may be a car, truck, van, bus, motorcycle, boat, or any other type of vehicle. According to one embodiment, the vehicle 100 is a hybrid electric vehicle, such as a plug-in hybrid electric vehicle (PHEV) that is partially or fully powered by electricity. According to another embodiment, the vehicle 100 is an electric vehicle powered by electricity. Batteries are used to provide power to components of the vehicle 100, such as electric motors (not shown), electrical components (not shown), etc., including combinations thereof and / or multiples thereof. According to one or more embodiments, the vehicle 100 is an autonomous or semi-autonomous vehicle. An autonomous vehicle is a vehicle with autonomous driving capabilities. A semi-autonomous vehicle is a vehicle with some autonomous features (e.g., automatic parking, lane keeping, etc.) but lacks full autonomous control.

[0040] The processing system 102 is located within the vehicle and is responsible for managing and processing the data collected by the sensors 104. The sensors 104 are strategically positioned on the vehicle to collect various types of data from the vehicle's environment. Arrows between the sensors 104 and the processing system 102 indicate the data flow from the sensors 104 to the processing system 102, highlighting the interaction between these components. This setup enables the vehicle 100 to perform tasks related to infrastructure access control, such as detecting infrastructure objects and emitting customized LiDAR scanning patterns. Examples of sensors 104 include, but are not limited to, LiDAR devices, camera devices, Global Positioning System (GPS) devices, Driver Monitoring System (DMS) camera devices, and combinations thereof and / or multiple of them.

[0041] Now for reference Figure 2-5B Further features of the processing system 102 and the sensor 104 are described.

[0042] In particular, Figure 2 The illustration is based on one or more embodiments. Figure 1 The processing system 102 includes a processing device 202, a memory 204, a scan mode engine 210, and an autonomous driving engine 212, according to one or more embodiments. It should be understood that the processing system 102 can be any device suitable for performing or supporting infrastructure access control using vehicle-based LiDAR. For example, the processing system 102 can be a device implemented in or otherwise associated with vehicle 100, such as an electronic control unit (also referred to as an electronic control module). As another example, the processing system 102 can be a smartphone, tablet computer, laptop computer, desktop computer, wearable computing device, etc., including combinations and / or multiples thereof. As yet another example, the processing system 102 can be... Figure 6 The processing system 600 and / or may include Figure 6 One or more components of the processing system 600.

[0043] Processing device 202 is responsible for executing instructions and managing the overall operation of processing system 102. Processing device 202 can be any suitable processing circuitry used for executing instructions and processing data. For example, processing device 202 can be a microcontroller, microprocessor, application-specific integrated circuit (ASIC), or any other type of processing unit capable of handling the computational needs of processing system 102. Processing device 202 is... Figure 6 Examples of one or more of the processing devices 621 are described in more detail herein.

[0044] Memory 204 stores data useful for the operation of processing system 102 (e.g., sensor data 214), computer-readable instructions, and algorithms. This may include real-time data processing, historical data analysis, and storage of firmware or software programs. Memory 204 is any suitable device for storing data (such as sensor data 214) and / or instructions. For example, memory 204 may be a combination of volatile memory (e.g., random access memory) and non-volatile memory (e.g., read-only memory, flash memory). Memory 204 is Figure 6 Examples of one or more of system memory 622, random access memory 623, and / or read-only memory 624 are described in more detail herein.

[0045] The scan pattern engine 210 is a dedicated component for generating and managing custom LiDAR scan patterns for infrastructure access control. For example, the scan pattern engine 210 processes scan patterns from LiDAR devices (e.g., Figure 3 The sensor data 214 of the LiDAR device 104a) is used to create a point cloud of the environment in which the vehicle 100 is operating using a standard LiDAR scan pattern. The scan pattern engine 210 then normalizes the standard LiDAR scan pattern based on the position of the vehicle 100 relative to an infrastructure object (e.g., a door). The scan pattern engine 210 selects an appropriate custom LiDAR scan pattern from a plurality of predefined custom LiDAR scan patterns stored in memory 204. These custom LiDAR scan patterns are tailored for specific infrastructure objects (such as specific doors or toll booths) and are designed to be recognized by receivers associated with those objects. By transmitting these custom LiDAR scan patterns, the scan pattern engine 210 ensures that infrastructure objects can accurately identify and respond to the vehicle 100, thereby facilitating secure and efficient access control.

[0046] The autonomous driving engine 212 controls the autonomous navigation capability of vehicle 100, allowing the vehicle to navigate relative to detected infrastructure objects. Once the scanning pattern engine 210 has successfully emitted a customized LiDAR scanning pattern and the infrastructure object has performed the desired action (e.g., opening a door), the autonomous driving engine 212 enables vehicle 100 to navigate relative to the infrastructure object (e.g., driving through an open door). The autonomous driving engine 212 processes sensor data 214 received from sensors 104 (e.g., LiDAR equipment, camera equipment, and GPS equipment) to determine the precise position and orientation of vehicle 100. The autonomous driving engine 212 then generates control signals to steer, accelerate, or brake the vehicle as needed to safely and efficiently navigate through open doors or other infrastructure objects. The autonomous driving engine 212 ensures that vehicle 100 can autonomously perform complex maneuvers, thereby reducing the need for manual intervention and enhancing the overall efficiency of the access control process.

[0047] exist Figure 2 In the embodiments shown, processing system 102 can communicate with remote system 220 and infrastructure object system 221. Remote system 220 can communicate with the vehicle's processing system to provide additional data or receive alerts, such as those to or from police or fleet management systems. Infrastructure object system 221 represents the system associated with infrastructure objects (such as doors or toll booths) that interact with vehicle 100. Dashed arrows indicate data flow and communication between these components, highlighting the interconnected nature of the systems for effective infrastructure access control. Remote system 220 and infrastructure object system 221 can be any suitable computing system, including combinations thereof and / or multiple thereof, for collecting, analyzing, storing, and communicating with other systems (such as processing system 102), etc.

[0048] Figure 3 A flowchart is shown of a method 300 for infrastructure access control using vehicle-based LiDAR according to one or more embodiments. Method 300 can be implemented using any suitable system or device. For example, method 300 and its steps can be used... Figure 1 and Figure 2 The processing system 102 is composed of Figure 6 This is implemented using processing systems such as 600 (including combinations thereof and / or multiple thereof). Now refer to... Figure 1 and Figure 2 Method 300 is described, but is not limited to this.

[0049] Vehicle 100 uses sensors 104 to collect data. For example, vehicle 100 may be equipped with various sensors 104, such as a lidar device 104a, a camera device 104b, a GPS device 104c, and a driver monitoring system (DMS) camera device 104d. Sensors 104 collect data from the vehicle's environment and provide the data to processing system 102. Specifically, method 300 begins at box 302, where lidar device 104a acquires a point cloud of the environment. At box 304, camera device 104b acquires an image of the environment. At box 306, data from both sensors is used to detect the presence of infrastructure objects (such as doors). If no infrastructure object is detected (box 308, "No"), method 300 returns to box 306 to continue attempting to detect infrastructure objects using the data collected at boxes 302 and 304. If an infrastructure object is detected (box 308, "Yes"), method 300 moves to box 312, where processing system 102 checks whether the GPS location of vehicle 100 substantially matches the known location of a known infrastructure object. At box 310, the GPS location of vehicle 100 is obtained, wherein GPS device 104c obtains the location of the vehicle (e.g., the GPS coordinates of the vehicle).

[0050] If the location does not match (box 314, "No"), method 300 returns to box 310 to reacquire the location. If the location matches (box 314, "Yes"), then the DMS camera device 104d acquires an image of the driver at box 316. Processing system 102 then performs driver authentication at box 318 by comparing it with the pre-authorized authentication image stored at box 320. If the driver's identity is not verified (box 322, "No"), an alarm is generated at box 324. The alarm can be sent to remote system 220, fleet manager 360, and / or law enforcement agency 362.

[0051] If the driver's identity is confirmed (box 322, "Yes"), the scan pattern engine 210 warns the lidar device 104a to begin scanning the environment in box 326. In box 328, the lidar device 104a acquires a point cloud of infrastructure objects using a standard lidar scan pattern. A standard lidar scan pattern refers to a predefined and consistent sequence of laser pulses emitted by the lidar device 104a to map the surrounding environment. This pattern typically involves the lidar device 104a rotating or oscillating to cover a specific field of view, thereby emitting laser pulses at regular intervals and angles. The returned laser pulses are measured to create a point cloud, which is a three-dimensional representation of the environment. The standard lidar scan pattern is designed to provide comprehensive and uniform coverage of the area around the vehicle 100, thereby allowing the detection and identification of objects, obstacles, and infrastructure objects.

[0052] At box 330, the scan pattern engine 210 normalizes the standard LiDAR scan pattern based on the vehicle's position (determined at box 310) relative to the position of a known infrastructure object. For example, different vehicles may approach the same infrastructure object differently (e.g., a first vehicle may stop at a certain distance from a door, while a second vehicle may stop at a different distance; the same vehicle may approach the door at different times from slightly different angles, and / or combinations thereof and / or multiple thereof). This variation results in differences in the point cloud captured at box 326. Therefore, the scan pattern engine 210 normalizes the standard LiDAR scan pattern by taking into account the variation in the position of vehicle 100 relative to the infrastructure object.

[0053] At box 332, the scan pattern engine 210 selects a customized LiDAR scan pattern from multiple customized patterns based on the detected infrastructure object. A customized LiDAR scan pattern refers to a customized sequence of laser pulses emitted by the LiDAR device, specifically designed to identify the source of the customized LiDAR scan pattern. Unlike standard LiDAR scan patterns, which provide uniform coverage of the environment, customized scan patterns are optimized for identifying the source of the customized LiDAR scan pattern (e.g., vehicle 100). This customization can involve changing the frequency, intensity, angle, or sequence of the laser pulses to create a unique pattern that can be recognized by a receiver on an infrastructure object, such as a door or tollbooth. According to one or more embodiments, a customized LiDAR scan pattern is selected from multiple predefined patterns stored in the system's memory based on the type and characteristics of the detected infrastructure object. By using customized LiDAR scan patterns, accurate identification and interaction with the infrastructure are provided, enabling actions such as opening a door or authorizing access, thereby enhancing the security and efficiency of the access control process.

[0054] At box 334, the scan pattern engine 210 triggers the lidar device 104a to emit a customized lidar scan pattern. Then, at box 336, a system or device associated with the infrastructure object determines whether the customized lidar scan pattern matches the expected lidar scan pattern associated with the infrastructure object. If the customized lidar scan pattern does not match (box 336, "No"), an alarm is generated at box 324. If the customized lidar scan pattern matches (box 336, "Yes"), the infrastructure object is triggered at box 338 to perform an action, such as opening a door. The action is recorded at box 340.

[0055] In box 342, processing system 102 checks whether the infrastructure action was successfully completed. If the action is successful (box 342, "Yes"), then in box 344, autonomous driving engine 212 enables vehicle 100 to be autonomously controlled for navigation (e.g., driving through an open door). If the action is unsuccessful (box 342, "No"), infrastructure object system 221 is activated to perform secondary authentication using a custom LiDAR scanning pattern. Specifically, infrastructure object system 221 triggers infrastructure cameras in box 346 and acquires images using one or more infrastructure cameras associated with the infrastructure object in box 350, while simultaneously emitting a custom LiDAR scanning pattern in box 348. For example, the LiDAR device emits a low-voltage scanning pattern in box 348, which represents the custom LiDAR scanning pattern, and the infrastructure cameras acquire images in box 350 to capture the emitted custom LiDAR scanning pattern from vehicle 100.

[0056] At box 352, infrastructure device system 211 verifies whether the brightness changes in the acquired image match the expected changes based on a customized LiDAR scanning pattern. If the brightness does not match (e.g., not within a threshold amount of brightness) (box 354, "No"), an alarm is generated at box 324. If the brightness matches (box 354, "Yes"), the infrastructure object is triggered to perform an action (e.g., open a door) at box 356, and entry is recorded at box 358. At box 344, autonomous driving engine 212 enables vehicle 100 to be autonomously controlled for navigation (e.g., driving through an open door).

[0057] Additional processes may also be included, and it should be understood that... Figure 3 The processes described herein are illustrative, and other processes may be added, or existing processes may be removed, modified, or rearranged without departing from the scope of this disclosure. It should also be understood that... Figure 3 The process described herein can be implemented as programming instructions stored on a non-transitory computer-readable storage medium, when executed by a computing system (e.g., Figure 1 and Figure 2 Processing system 102 Figure 6 Processing systems such as 600, including combinations thereof and / or multiple thereof, include processors (e.g., Figure 2 Processing equipment 202 Figure 6 When the processor 621, etc., including combinations thereof and / or multiple thereof, is executed, the processor performs the process described herein.

[0058] Figure 4 A flowchart is shown of a method 400 for infrastructure access control using vehicle-based LiDAR according to one or more embodiments. Method 400 can be implemented using any suitable system or device. For example, method 400 and its steps can be used... Figure 1 and Figure 2 The processing system 102 is composed of Figure 6 This is implemented using processing systems such as 600 (including combinations thereof and / or multiple thereof). Now refer to... Figure 1-3 Method 400 is described, but is not limited to this.

[0059] In box 402, processing system 102 detects infrastructure objects in the environment in which vehicle 100 is operating. This detection is based at least in part on data collected by sensor 104. For example, lidar device 104a may collect lidar data (e.g., sensor data 214) about the environment in which vehicle 100 is operating, including infrastructure objects. The lidar device scans the environment around vehicle 100 and identifies infrastructure objects that may be relevant to access control, such as doors, toll booths, or other infrastructure objects. The detection process involves analyzing point cloud data generated by lidar device 104a to identify the presence and characteristics of infrastructure objects.

[0060] At box 404, processing system 102 uses scan pattern engine 210 and lidar device 104a to emit a customized lidar scan pattern associated with a detected infrastructure object. This customized lidar scan pattern is specifically designed to be recognized by a receiver on the infrastructure object. According to one or more embodiments, the customized lidar scan pattern is unique for vehicle 100 and infrastructure object. The customized lidar scan pattern is selected from multiple predefined patterns based on the identified infrastructure object. Infrastructure devices can be identified by the location, type, and characteristics of the detected infrastructure object. When a receiver on the infrastructure object detects the customized lidar scan pattern, the infrastructure object performs a specific action, such as opening a door or allowing access through a tollbooth.

[0061] At box 406, in response to an action performed by an infrastructure object, processing system 102 autonomously controls vehicle 100 to navigate relative to the infrastructure object using autonomous driving engine 212. This involves generating control signals to appropriately steer, accelerate, or brake vehicle 100 to effectively navigate vehicle 100 through open doors or other infrastructure objects. Autonomous control ensures that vehicle 100 can navigate infrastructure objects seamlessly and efficiently without manual intervention from the driver and / or other personnel, enhancing the overall efficiency and security of the access control process.

[0062] Additional processes may also be included, and it should be understood that... Figure 4 The processes described herein are illustrative, and other processes may be added, or existing processes may be removed, modified, or rearranged without departing from the scope of this disclosure. It should also be understood that... Figure 4The process described herein can be implemented as programming instructions stored on a non-transitory computer-readable storage medium, when executed by a computing system (e.g., Figure 1 and Figure 2 Processing system 102 Figure 6 Processing systems such as 600, including combinations thereof and / or multiple thereof, include processors (e.g., Figure 2 Processing equipment 202 Figure 6 When the processor 621, etc., including combinations thereof and / or multiple thereof, is executed, the processor performs the process described herein.

[0063] Figure 5A A block diagram depicts a standard lidar scanning mode 500 of a lidar device (e.g., lidar device 104a) according to one or more embodiments. Figure 5B A block diagram depicts a customized lidar scanning mode 510 for a lidar device (e.g., lidar device 104a) according to one or more embodiments. In these examples, each point 501, 502, 503, 504, 505, 506, 507, 508, 509 represents a point captured by lidar device 104a as part of a lidar scan. For a standard lidar scanning mode 500, a scan is performed using a predefined and consistent sequence of laser pulses (represented by points 501-509) emitted by lidar device 104a to map the surrounding environment. For example, lidar device 104a may first emit a laser point at point 501, then at point 502, then at point 503, and so on, continuing until point 509. In such examples, each pulse can be performed using the same parameters (e.g., substantially the same amount of time, substantially the same frequency, etc., including combinations thereof and / or multiples thereof). However, in the case of customized lidar scanning mode 510, the sequence and attributes of the scans performed by lidar device 104a differ from those of standard lidar scanning mode 500. For example, in Figure 5B In this diagram, the lidar device 104a scans points 501-507 in the sequence shown. It should also be understood that the lidar device 104a can change the properties of the lidar scan at each point. For example, the duration of the laser pulses, the frequency of the laser pulses, etc. (including combinations thereof and / or multiple thereof) can be varied from point to point. Considering the large number of points performed in a lidar scan (e.g., hundreds of thousands or even millions of points), and the opportunity to change parameters at each point, the number of possibilities for customizing lidar scanning patterns is enormous, and it is possible to make custom scanning patterns unique (e.g., unique for users, vehicles, and infrastructure objects). It should be understood that the standard lidar scanning pattern 500 ( Figure 5A ) and customized LiDAR scanning mode 510 ( Figure 5BThis is merely a simplified example, and many different arrangements of standard and custom lidar scanning modes are possible.

[0064] It should be understood that one or more embodiments described herein can be implemented in conjunction with any other type of computing environment now known or developed in the future. For example, Figure 6 A block diagram of a processing system 600 for implementing the techniques described herein is depicted. According to one or more embodiments described herein, the processing system 600 is an example of a cloud computing node in a cloud computing environment. In the example, the processing system 600 has one or more central processing units (also referred to as “processors”, “processing resources”, or “processing devices”) 621a, 621b, 621c, etc. (collectively or generally referred to as processor 621 and / or processing device 621). In aspects of this disclosure, each processor 621 may include a Reduced Instruction Set Computer (RISC) microprocessor. The processor 621 is coupled to system memory 622 and / or various other components via a system bus 633. System memory 622 may include one or more temporary and / or permanent memory devices, such as random access memory (RAM) 623, read-only memory (ROM) 624, etc., including combinations and / or multiples thereof. System bus 633 may include a Basic Input / Output System (BIOS) that controls certain basic functions of the processing system 600.

[0065] Further depictions include an input / output (I / O) adapter 627 and a network adapter 626 coupled to the system bus 633. The I / O adapter 627 may be a Small Computer System Interface (SCSI) adapter that communicates with a hard disk 635 and / or storage device 636 or any other similar component. The I / O adapter 627, hard disk 635, and storage device 636 are collectively referred to herein as mass storage 634. An operating system 640 for execution on the processing system 600 may be stored in the mass storage 634. The network adapter 626 interconnects the system bus 633 with an external network 638, enabling the processing system 600 to communicate with other such systems.

[0066] A display (e.g., a display monitor) 639 is connected to the system bus 633 via a display adapter 632, which may include a graphics adapter to improve the performance of graphics-intensive applications and video controllers. In one aspect of this disclosure, adapters 626, 627, and / or 632 may be connected to one or more I / O buses connected to the system bus 633 via an intermediate bus bridge (not shown). Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols such as Peripheral Component Interconnect (PCI). Additional input / output devices are shown connected to the system bus 633 via a user interface adapter 628 and the display adapter 632. A keyboard 629, a mouse 630, and a speaker 631 may be interconnected to the system bus 633 via a user interface adapter 628, which may include, for example, a super I / O chip integrating multiple device adapters into a single integrated circuit.

[0067] In some aspects of this disclosure, the processing system 600 includes a graphics processing unit (GPU) 637. The GPU 637 is a dedicated electronic circuit designed to manipulate and modify memory to accelerate the creation of images in a frame buffer intended for output to a display. Typically, the GPU 637 is highly efficient in manipulating computer graphics and image processing, and has a highly parallel architecture that makes it more efficient than general-purpose CPUs for parallel processing of algorithms handling large blocks of data.

[0068] Therefore, as configured herein, the processing system 600 includes processing power in the form of a processor 621, storage capacity including system memory 622 and mass storage 634, input devices such as a keyboard 625 and a mouse 630, and output capacity including a speaker 631 and a display 639. In some aspects of this disclosure, a portion of the system memory 622 and the mass storage 634 jointly store the operating system 640 to coordinate the functions of the various components shown in the processing system 600.

[0069] One or more embodiments provide several significant benefits and advantages over existing methods of infrastructure access control, including (but not limited to) the following.

[0070] By granting secure access to vehicles through a customized LiDAR scanning pattern, the need for additional hardware, such as RFID transmitters or badges, is eliminated. This reduces the risk of physical security equipment being lost, stolen, or damaged.

[0071] One or more embodiments provide robust protection against unauthorized entry into a secure location by using existing vehicle hardware (such as a DMS) to verify the driver's identity. This ensures that only authorized drivers can access the secure area.

[0072] One or more embodiments reduce subjective decision-making by security personnel and decrease door congestion by automating the access control process. This results in faster and more efficient access for authorized vehicles.

[0073] One or more embodiments enable vehicle-to-infrastructure (V2X) logistics management by continuously monitoring infrastructure objects (such as gates, stalls, and other infrastructure objects) along the route. This ensures real-time tracking and management of vehicles, including, for example, fleet operations.

[0074] One or more embodiments increase accessibility for users who require additional assistance when encountering infrastructure objects such as gates. By automating the access control process, it reduces the need for drivers to leave their vehicles to interact with physical security elements.

[0075] One or more embodiments dynamically customize and deploy LiDAR scanning patterns based on specific infrastructure objects, enhancing the flexibility and adaptability of the access control process.

[0076] Once access is granted, one or more embodiments can autonomously control the vehicle to navigate relative to infrastructure objects, thereby further simplifying the entry process and reducing the need for manual intervention.

[0077] In the event of unauthorized access attempts or failures during the access control process, one or more embodiments can generate real-time alerts and report to third parties (such as remote systems, fleet managers, or law enforcement agencies), thereby ensuring a rapid response to security vulnerabilities.

[0078] These and other benefits are possible in the various embodiments described herein.

[0079] The terms “a” and “an” do not indicate a limitation of quantity, but rather that at least one of the referenced items is present. Unless the context clearly indicates otherwise, the term “or” means “and / or”. Throughout the specification, the reference to “aspect” means that a particular element described in connection with that aspect (e.g., a feature, structure, step, or characteristic) is included in at least one aspect described herein and may or may not be present in other aspects. Furthermore, it should be understood that the described elements may be combined in any suitable manner in the aspects.

[0080] When an element, such as a layer, film, region, or substrate, is referred to as being “on” another element, it can be directly on the other element, or there may be intermediate elements present. Conversely, when an element is referred to as being “directly” on another element, there are no intermediate elements present.

[0081] Unless otherwise stated herein, all test standards are the most recent standards in force as of the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which a test standard appears.

[0082] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0083] While the foregoing disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and elements can be substituted with equivalents without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from the basic scope of this disclosure. Therefore, it is intended that this disclosure be limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A computer-implemented method for infrastructure access control using vehicle-based lidar, the method comprising: The detection of infrastructure objects in the environment in which the vehicle is operating is based at least in part on lidar data collected by the vehicle's lidar equipment. A customized lidar scanning pattern associated with the infrastructure object is emitted, which is received by the infrastructure object's receiver and causes the infrastructure object to perform an action; and In response to the infrastructure object performing the action, the vehicle is autonomously controlled to navigate relative to the infrastructure object.

2. The computer-implemented method according to claim 1, wherein, The infrastructure object is a passageway door, and the action described therein is opening the passageway door.

3. The computer-implemented method according to claim 1 further includes: After activating the customized lidar scanning pattern associated with the infrastructure object and before autonomously controlling the vehicle, it is determined whether the infrastructure object has successfully performed the action.

4. The computer-implemented method according to claim 3, wherein, In response to determining that the infrastructure object has successfully performed the action, autonomous control of the vehicle is executed.

5. The computer-implemented method of claim 3, further comprising, in response to determining that the infrastructure object has failed to successfully perform the action: The remote system is activated to acquire images of the infrastructure objects while simultaneously emitting the customized lidar scanning pattern; Receive an image of the infrastructure object from the remote system; Verify that the brightness changes in the image of the infrastructure object match the expected brightness changes based on the customized LiDAR scanning pattern of the infrastructure object; and In response to verifying that the brightness change in the image of the infrastructure object matches the expected brightness change, the remote system causes the infrastructure object to perform the action.

6. The computer-implemented method according to claim 1, wherein, The transmission of the customized lidar scanning pattern associated with the infrastructure object includes: The point cloud of the infrastructure object is acquired by the lidar device using a standard lidar scanning mode; The standard lidar scanning pattern is normalized based on the vehicle's position relative to the location of the infrastructure object; Based on the infrastructure object, the customized LiDAR scanning mode is selected from multiple customized LiDAR scanning modes; and The lidar device is made to emit the customized lidar scanning mode.

7. The computer-implemented method according to claim 1, wherein, The customized lidar scanning pattern enables the infrastructure object to perform the action in response to the customized lidar scanning pattern matching the expected customized lidar scanning pattern.

8. The computer-implemented method according to claim 1, wherein, The customized lidar scanning mode is limited by a customized frequency.

9. The computer-implemented method according to claim 1, wherein the customized lidar scanning mode is defined by a customized sequence of lidar pulses.

10. A vehicle comprising: A driver monitoring system, the driver monitoring system including a camera; LiDAR equipment; and A processing system for infrastructure access control using vehicle-based lidar, the processing system comprising: Memory including computer-readable instructions; and A processing apparatus for executing the computer-readable instructions, the computer-readable instructions controlling the processing system to perform operations, the operations including: The detection of infrastructure objects in the environment in which the vehicle is operating is based at least in part on lidar data collected by the vehicle's lidar equipment. The image of the vehicle operator captured by the camera is used to determine whether the vehicle operator is an authorized operator of the vehicle; In response to determining that the operator of the vehicle is an authorized operator of the vehicle, a customized lidar scanning pattern associated with the infrastructure object is emitted, the customized lidar scanning pattern being received by the receiver of the infrastructure object and causing the infrastructure object to perform an action; and In response to the infrastructure object performing the action, the vehicle is autonomously controlled to navigate relative to the infrastructure object.

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