Stop detection system

Abstract

In some aspects, a system may obtain scene information associated with determining stop information associated with a movable object. The system may receive sensor data collected by a set of available sensors. The system may select a stop detection pattern to be used by the system in association with determining the stop information, the stop detection pattern selected based at least in part on the scene information and sensor data collected by the set of available sensors. The system may select one or more selected sensors from the set of available sensors to be used in association with determining the stop information based at least in part on the stop detection pattern. The system may determine stop information according to a stop detection pattern and using sensor data collected by one or more selected sensors. Numerous other aspects are described.

Description

TECHNICAL FIELD

[0001] Aspects of the disclosure relate generally to autonomous driving and / or advanced driver assistance systems (ADAS), and, for example, to stop detection systems that can select a stop detection mode. BACKGROUND

[0002] Autonomous driving systems are an emerging technology that allows vehicles to follow preprogrammed routes or operate in response to real-time environmental conditions without human input. Autonomous driving systems typically use a combination of sensors, cameras, and software algorithms to perceive the environment and make decisions based on the perceived environment. Autonomous driving technology can be designed to create safer, more efficient, and more convenient modes of transportation that reduce the need for human intervention. The development of autonomous driving systems is driven by the convergence of factors such as sensor technology, advances in artificial intelligence and machine learning that enable vehicles to sense and process information from the surrounding environment, e.g., road conditions, traffic, and pedestrians. SUMMARY

[0003] Some aspects described herein relate to a method. The method can include obtaining, by a system, scenario information associated with determining stop information associated with a movable object. The method can include receiving, by the system, sensor data collected by a set of available sensors. The method can include selecting, by the system, a stop detection mode to be used by the system in association with determining the stop information, the stop detection mode being selected based at least in part on the scenario information and the sensor data collected by the set of available sensors. The method can include selecting, by the system and based at least in part on the stop detection mode, one or more selected sensors to be used in association with determining the stop information from the set of available sensors. The method can include determining, by the system, the stop information according to the stop detection mode and using sensor data collected by the one or more selected sensors.

[0004] Some aspects described herein relate to a system for wireless communication. The system can include one or more processors and one or more memories coupled to the one or more processors. The one or more processors can be configured to obtain scenario information associated with determining stop information associated with a movable object. The one or more processors can be configured to cause the system to receive sensor data collected by a set of available sensors. The one or more processors can be configured to cause the system to select a stop detection mode to be used by the system in association with determining the stop information, the stop detection mode selected based at least in part on the scenario information and the sensor data collected by the set of available sensors. The one or more processors can be configured to cause the system to select one or more selected sensors to be used in association with determining the stop information from the set of available sensors based at least in part on the stop detection mode. The one or more processors can be configured to cause the system to determine the stop information in accordance with the stop detection mode and using sensor data collected by the one or more selected sensors.

[0005] Some aspects described herein relate to a non-transitory computer-readable medium storing a set of instructions for wireless communication by a system. The set of instructions, when executed by one or more processors of the system, can cause the system to obtain scenario information associated with determining stop information associated with a movable object. The set of instructions, when executed by one or more processors of the system, can cause the system to receive sensor data collected by a set of available sensors. The set of instructions, when executed by one or more processors of the system, can cause the system to select a stop detection mode to be used by the system in association with determining the stop information, the stop detection mode selected based at least in part on the scenario information and the sensor data collected by the set of available sensors. The set of instructions, when executed by one or more processors of the system, can cause the system to select one or more selected sensors to be used in association with determining the stop information from the set of available sensors based at least in part on the stop detection mode. The set of instructions, when executed by one or more processors of the system, can cause the system to determine the stop information in accordance with the stop detection mode and using sensor data collected by the one or more selected sensors.

[0006] Some aspects described herein relate to an apparatus for wireless communication. The apparatus can include means for obtaining scenario information associated with determining stop information associated with a movable object. The apparatus can include means for receiving sensor data collected by a set of available sensors. The apparatus can include means for selecting a stop detection mode to be used by the apparatus in association with determining the stop information, the stop detection mode being selected based at least in part on the scenario information and the sensor data collected by the set of available sensors. The apparatus can include means for selecting one or more selected sensors to be used in association with determining the stop information from the set of available sensors based at least in part on the stop detection mode. The apparatus can include means for determining the stop information in accordance with the stop detection mode and using sensor data collected by the one or more selected sensors.

[0007] Aspects generally include a method, apparatus, system, computer program product, user equipment, user equipment, wireless communication device, and / or processing system as substantially described with reference to and as illustrated by the drawings and specification.

[0008] The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows can be better understood. Additional features and advantages will be described hereinafter. The disclosed concepts and specific examples can be readily utilized as bases for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions are not to be considered as departing from the scope of the appended claims. The BRIEF DESCRIPTION OF DRAWINGS

[0009] In order that the foregoing aspects can be understood in detail, a more particular description will be rendered by reference to various aspects, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings are not intended to be exhaustive or limiting of the scope of the disclosure. The disclosure can admit to other equally effective aspects not expressly described or shown. Like reference numerals can be used in the various drawings to indicate like components.

[0010] Figure 1 is a diagram of an example environment in which an autonomous vehicle or an advanced driver assistance system (ADAS) equipped vehicle according to the present disclosure can operate.

[0011] Figure 2is a diagram of an example on-board system of an autonomous vehicle or a vehicle equipped with ADAS according to the present disclosure.

[0012] Figure 3 is a diagram of example components of a device according to the present disclosure.

[0013] Figures 4A to 4E is a diagram illustrating an example associated with a stop detection system according to the present disclosure.

[0014] Figure 5 is a flowchart of an example process associated with a stop detection system according to the present disclosure. DETAILED DESCRIPTION

[0015] Various aspects of the disclosure are more fully described below with reference to the figures. The disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. One skilled in the art will appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure disclosed herein, whether implemented independently of or combined with any other aspect of the disclosure. For example, an apparatus can be implemented or a method can be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to encompass other structures, functionality, or structures and functionality not expressly set forth herein. It will be appreciated that any aspect of the disclosure disclosed herein can be embodied by one or more elements of a claim.

[0016] High definition (HD) maps are highly accurate maps that can be used in autonomous driving. HD can be accurate to, for example, centimeter level. HD maps can be created, for example, based on sensor data collected by a variety of sensors, such as one or more positioning sensors (e.g., global navigation satellite system (GNSS) sensors and / or global positioning system (GPS) sensors), one or more cameras, a lidar system, or one or more other ranging systems (e.g., radar systems and / or sonar systems), etc. One aspect for enabling generation of HD maps is to merge data provided by multiple vehicles (e.g., one or more autonomous vehicles), such as simultaneous localization and mapping (SLAM) data provided by the multiple vehicles. Thus, the quality of the HD map depends on the quality of the SLAM data provided by these vehicles. Autonomous vehicles typically include multiple sensors, and one or more algorithms can be utilized to configure the system of the vehicle to process the sensor data, thereby generating SLAM data to be provided in association with generating the HD map. Notably, identifying when a vehicle stops is important to ensure that the SLAM data determined by a given vehicle is accurate and reliable.

[0017] Additionally, for some vehicles, such as unmanned aerial vehicles (UAVs), robotic vehicles (e.g., robots), or another type of vehicle that requires motion control, understanding the motion state of the vehicle (i.e., whether the vehicle is stopped or in motion) is important to enable motion control. Thus, processing sensor data to determine whether a vehicle is stopped is an important problem.

[0018] One conventional technique for performing stop detection in association with generating HD maps utilizes machine vision, i.e., image-based and / or video-based stop detection. However, this technique requires a large amount of processing time and is susceptible to noise, which compromises the accuracy and reliability of stop detection. Thus, such techniques are typically not utilized, or are only used as a backup or fallback. Another technique for performing stop detection is to use sensor data with high accuracy, such as sensor data provided by on-board diagnostics (OBD) sensors or via sensors that communicate over a controller area network (CAN) bus. However, while the sensor data provided by such sensors can accurately represent the motion state of a vehicle, these sensors are expensive, and in some cases, users do not have easy access to the sensor data. Thus, in some cost-controlled systems, these highly accurate sensors will not be used.

[0019] Some implementations described herein provide a stop detection system that can be used to determine stop information associated with a movable object, such as a vehicle. In some aspects, the system can obtain scene information associated with determining stop information associated with a movable object. In some aspects, the system can select, based at least in part on the scene information and sensor data collected by a set of available sensors, a stop detection mode to be used in association with determining stop information. In some aspects, the system can then select, based at least in part on the stop detection mode, one or more selected sensors to be used in association with determining stop information from the set of available sensors, and can determine stop information according to the stop detection mode and using sensor data collected by the one or more selected sensors.

[0020] In some aspects, the techniques and apparatuses described herein provide a reduced cost solution for determining stop information associated with a vehicle (e.g., performing stop detection associated with a vehicle) while improving accuracy and reliability. The reduced cost implemented herein can take the form of, for example, reduced processing resource usage (e.g., by enabling the use of low complexity signal processing algorithms in some scenarios), reduced latency (e.g., by enabling the use of faster signal processing algorithms in some scenarios), or reduced monetary cost (e.g., by enabling the use of relatively low cost sensors). Moreover, the techniques and apparatuses described herein enable stop information to be determined in a flexible (e.g., user configurable) manner that allows stop detection modes and sets of sensors to be selected according to the application and available resources (e.g., available sensors, available processing resources, etc.). Furthermore, the techniques and apparatuses described herein can improve reliability and improve functional safety by enabling stop information to be corrected using sensor data provided by multiple (e.g., diverse) sensors. Additional details are described below.

[0021] Figure 1 is a diagram of an example environment 100 in which an autonomous vehicle or an advanced driver assistance system (ADAS) equipped vehicle according to the present disclosure can operate. As shown, the environment 100 can include, for example, a vehicle 110, an on-board system 120 of the vehicle 110, a remote device 130, a network node 150, and a network 160. The devices of the environment 100 can be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections. As further shown, the vehicle 110 can be configured to detect one or more objects 140 (e.g., using the on-board system 120). Figure 1 Figure 1

[0022] ​​In some aspects, the vehicle 110 can include any form of conveyance that is capable of carrying one or more human occupants and / or cargo and is powered by any suitable energy source. For example, the vehicle 110 can include a land vehicle (e.g., a car, truck, van, or train), an aerial vehicle (e.g., a UAV), and / or a watercraft. In Figure 1 In the depicted example, the vehicle 110 is a land vehicle, and is shown as a car. Further, in Figure 1 In the depicted example, the vehicle 110 is an autonomous vehicle. For example, an autonomous vehicle (AV) is a vehicle that has a processor, programmed instructions, and drivetrain components that can be controlled by the processor without a human operator. An autonomous vehicle can be fully autonomous, i.e., the autonomous vehicle does not require a human operator for most or all driving conditions and functions, or the autonomous vehicle can be semi-autonomous, i.e., a human operator can be required under certain conditions or for certain operations, or can override the autonomous driving system of the autonomous vehicle and control the autonomous vehicle. Additionally or alternatively, the vehicle 110 can be equipped with an ADAS that supports one or more safety features and / or technologies to help a driver avoid collisions and / or accidents (e.g., adaptive cruise control, lane departure warning, automatic emergency braking) or to otherwise make driving the vehicle 110 safer and / or more efficient.

[0023] As shown in Figure 1 The vehicle 110 can include an on-board system 120 integrated into and / or coupled with the vehicle 110. Generally, the on-board system 120 can be used to control the vehicle 110, sense information about the vehicle 110 and / or the environment in which the vehicle 110 operates, detect one or more objects 140 in the vicinity of the vehicle, provide output to or receive input from an occupant of the vehicle 110, and / or communicate with one or more devices that are remote from the vehicle 110, such as another vehicle and / or a remote device 130.

[0024] In some aspects, vehicle 110 may travel along a road in a semi-autonomous or autonomous manner. Vehicle 110 may be configured to detect objects 140 in the vicinity of vehicle 110. Object 140 may include, for example, another vehicle (e.g., an autonomous vehicle or a non-autonomous vehicle that requires a human operator for most or all driving conditions and functions), cyclists (e.g., riders of bicycles, e-scooters, or motorcycles), pedestrians, road features (e.g., road boundaries, lane markings, sidewalks, median strips, guardrails, roadblocks, signs, traffic signals, railroad crossings, or bicycle paths) and / or another object that may be on or near the road, such as a tree or an animal. In some aspects, to detect object 140, vehicle 110 may be equipped with a camera-based vision system and / or one or more sensors, such as a lidar system. In some aspects, the camera-based vision system and / or one or more sensors may be included in another system other than vehicle 110 (such as a robot, satellite, and / or traffic lights).

[0025] In some aspects, one or more sensors may provide sensor data, such as information about a detected object 140 (e.g., information about the distance to the object 140, the speed of the object 140, and / or the direction of movement of the object 140), to one or more other components of the onboard system 120. Additionally or alternatively, the vehicle 110 may transmit sensor data to a remote device 130 (e.g., a server, cloud computing system, and / or database) via a network 160 (e.g., via network node 150). The remote device 130 may be configured to process the sensor data and / or transmit the results of processing the sensor data to the vehicle 110 or another device (e.g., another vehicle 110) via the network 160 (e.g., via network node 150).

[0026] In some aspects, network node 150 includes one or more devices configured to receive, generate, store, process, and / or provide information relating to one or more aspects described herein. For example, network node 150 may include a base station (Node B, gNB, and / or 5G Node B (NB), etc.), user equipment (UE), relay equipment, network controller, access point, transmit / receive point (TRP), apparatus, device, computing system, and / or another suitable processing entity configured to perform one or more aspects described herein. For example, in some aspects, network node 150 may include one or more components of a decomposed base station (e.g., a central unit, distributed unit, and / or radio unit) and / or a clustered base station, which one or more components enable onboard system 120 to communicate over network 160 (e.g., invoke or otherwise utilize processing capabilities associated with remote device 130).

[0027] Network 160 includes one or more wired and / or wireless networks. For example, network 160 may include cellular networks (e.g., Long Term Evolution (LTE) networks, Code Division Multiple Access (CDMA) networks, 3G networks, 4G networks, 5G networks, another type of next-generation network, etc.), Public Land Mobile Networks (PLMNs), Local Area Networks (LANs), Wide Area Networks (WANs), Metropolitan Area Networks (MANs), telephone networks (e.g., Public Switched Telephone Networks (PSTN)), private networks, self-organizing networks, intranets, the Internet, fiber-optic networks, cloud computing networks, etc., and / or combinations of these or other types of networks. In some aspects, network 160 enables communication between devices in environment 100.

[0028] In some aspects, as described herein, devices of environment 100 (e.g., onboard system 120, remote device 130, etc.) may be configured to acquire scene information associated with determining stopping information associated with a movable object (e.g., vehicle 110); receive sensor data collected by a set of available sensors; select a stopping detection mode to be used in connection with determining the stopping information, wherein the stopping detection mode is selected at least in part based on the scene information and the sensor data collected by the set of available sensors; select one or more selected sensors from the set of available sensors to be used in connection with determining the stopping information, at least in part based on the stopping detection mode; and determine the stopping information according to the stopping detection mode and using the sensor data collected by the one or more selected sensors.

[0029] As indicated above, Figure 1 This is provided as an example. Other examples are available with reference to [the relevant information]. Figure 1 The examples described are different. Figure 1 The number and arrangement of devices shown are provided as an example. In reality, similar arrangements are possible. Figure 1 The equipment shown is compared to additional equipment, fewer equipment, different equipment, or equipment arranged in a different manner. Furthermore, Figure 1 The two or more devices shown can be implemented within a single device, or Figure 1 The single device shown can be implemented as multiple distributed devices. Additionally or alternatively, Figure 1 The set of devices shown (e.g., one or more devices) can be executed as described by Figure 1 The other set of devices shown performs one or more functions.

[0030] Figure 2 This is a diagram illustrating an example onboard system 200 of an automated driving vehicle or a vehicle equipped with ADAS according to this disclosure. In some aspects, onboard system 200 may correspond to onboard system 120 included in vehicle 110, as described above. Figure 1As described. Figure 2 As shown, the onboard system 200 may include one or more of the illustrated components 202-256. The onboard system 200 may include, for example, a power subsystem 202, a sensor subsystem 204, a control subsystem 206, and / or an onboard device 208. Components of the onboard system 200 may communicate via a bus (e.g., one or more wired and / or wireless connections) such as a CAN bus.

[0031] The power subsystem 202 may be configured to generate mechanical energy for the vehicle 110 to move the vehicle 110. For example, the power subsystem 202 may include an engine that converts fuel into mechanical energy (e.g., via combustion) and / or a motor that converts electrical energy into mechanical energy.

[0032] The sensor subsystem 204 may include one or more sensors configured to detect operating parameters of the vehicle 110 and / or environmental conditions in the environment in which the vehicle 110 operates (e.g., around the vehicle 110). For example, sensor subsystem 204 may include engine temperature sensor 210, battery voltage sensor 212, engine revolutions per minute (RPM) sensor 214, throttle position sensor 216, battery sensor 218 (e.g., for measuring battery current, voltage and / or temperature), motor current sensor 220, motor voltage sensor 222, motor position sensor 224 (e.g., resolver and / or encoder), motion sensor 226 (e.g., accelerometer, gyroscope and / or inertial measurement unit (IMU)), speed sensor 228, odometer sensor 230, clock 232, positioning sensor 234 (e.g., GNSS sensor and / or GPS sensor), one or more cameras 236, lidar system 238, one or more other ranging systems 240 (e.g., radar system and / or sonar system) and / or environmental sensor 242 (e.g., precipitation sensor and / or ambient temperature sensor).

[0033] The control subsystem 206 may include one or more controllers configured to control the operation of the vehicle 110. For example, the control subsystem 206 may include a brake controller 244 for controlling the braking of the vehicle 110, a steering controller 246 for controlling the steering and / or direction of the vehicle 110, a throttle controller 248 and / or a speed controller 250 for controlling the speed and / or acceleration of the vehicle 110, a gear controller 252 for controlling the shifting of the vehicle 110, a route selection controller 254 for controlling the navigation and / or route selection of the vehicle 110 (e.g., using map data), and / or an auxiliary equipment controller 256 for controlling one or more auxiliary devices associated with the vehicle 110, such as testing equipment, auxiliary sensors, and / or mobile equipment transported by the vehicle 110.

[0034] Onboard device 208 may be configured to receive sensor data from one or more sensors included in sensor subsystem 204 and / or provide commands to one or more controllers included in control subsystem 206. For example, onboard device 208 may control the operation of vehicle 110 by providing commands to controllers included in control subsystem 206 based on sensor data received from sensors included in sensor subsystem 204. In some aspects, onboard device 208 may be configured to process sensor data to generate commands. Onboard device 208 may include memory, one or more processors, input components, output components, and / or communication components, as described elsewhere herein.

[0035] As an example, onboard device 208 may receive navigation data, such as information associated with a navigation route from the starting point of vehicle 110 to its destination. In some aspects, the navigation data is accessed and / or generated by route selection controller 254. For example, route selection controller 254 may access map data and identify possible routes and / or segments that vehicle 110 can travel to move from the starting point to the destination. In some aspects, route selection controller 254 may identify preferred routes, such as by scoring multiple possible routes, applying one or more route selection techniques (e.g., minimum Euclidean distance, Dijkstra's algorithm, and / or Bellman-Ford algorithm), taking into account traffic data, and / or receiving user route selections. Onboard device 208 may use the navigation data to control the operation of vehicle 110. As the vehicle travels along the route, onboard device 208 may receive sensor data from various sensors in sensor subsystem 204. For example, the positioning sensor 234 can provide geographic location information to the onboard device 208, which can then access a map associated with the geographic location information to determine known fixed features associated with the geographic location that can be used to control the operation of the vehicle 110, such as streets, buildings, stop signs, and / or traffic signals.

[0036] In some aspects, onboard device 208 may receive one or more images captured by one or more cameras 236, analyze one or more images (e.g., to detect object data), and control the operation of vehicle 110 based on the analyzed images (e.g., to avoid detected objects). For example, onboard device 208 may obtain from camera 236 a series of images depicting a reference vehicle traveling along a road segment in front of vehicle 110, and onboard device 208 may analyze the series of images to estimate the size of the reference vehicle and / or the position of the reference vehicle relative to vehicle 110. Onboard device 208 may track the trajectory of the reference vehicle along the road segment in front of vehicle 110 based on the estimated size and / or estimated position of the reference vehicle on the series of images, and may estimate the surface geometry associated with the road segment in front of vehicle 110 based on the tracked trajectory of the reference vehicle. Therefore, the onboard device 208 can generate one or more control signals (e.g., to control the vehicle 110, stay in a designated lane, avoid obstacles, and / or plan a route) based on the estimated surface geometry associated with the road segment in front of the vehicle 110.

[0037] In some aspects, onboard device 208 may receive object data associated with one or more objects detected in the vicinity of vehicle 110, and / or may generate object data based on sensor data. Object data may indicate the presence or absence of an object, the object's location, the distance between the object and vehicle 110, the object's speed, the object's direction of movement, the object's acceleration, the object's trajectory (e.g., direction of travel), the object's shape, the object's size, the area occupied by the object, and / or the object's type (e.g., vehicle, pedestrian, cyclist, stationary object, or moving object). Object data may be detected, for example, by one or more cameras 236 (e.g., as image data), a lidar system 238 (e.g., as lidar data), and / or one or more other ranging systems 240 (e.g., as radar or sonar data). Onboard device 208 may process object data to detect objects near vehicle 110 and / or control the operation of vehicle 110 based on the object data (e.g., to avoid detected objects).

[0038] In some aspects, onboard device 208 can use object data (e.g., current object data) to predict future object data for one or more objects. For example, onboard device 208 can predict the future position of an object, the future distance between the object and vehicle 110, the future speed of the object, the future direction of movement of the object, the future acceleration of the object, and / or the future trajectory of the object (e.g., future direction of travel). For example, if the object is a vehicle and map data indicates that the vehicle is at an intersection, onboard device 208 can predict whether the object will likely travel in a straight line or turn. As another example, if sensor data and / or map data indicate that there are no traffic lights at the intersection, onboard device 208 can predict whether the object will stop before entering the intersection.

[0039] Onboard device 208 can generate motion plans for vehicle 110 based on sensor data, navigation data, and / or object data (e.g., current object data and / or future object data). For example, based on the current position of an object and / or its predicted future position, onboard device 208 can generate motion plans to move vehicle 110 along a surface and avoid collisions with other objects. In some aspects, the motion plan may include the speed, direction, and / or acceleration of vehicle 110 at one or more points in time. Additionally or alternatively, the motion plan may instruct one or more actions regarding detected objects, such as whether to catch up with the object, give way to the object, or overtake the object. Onboard device 208 can generate one or more commands or instructions based on the motion plan and can provide those commands to one or more controllers associated with control subsystem 206 for execution.

[0040] As indicated above,Figure 2 This is provided as an example. Other examples are available with reference to [the relevant information]. Figure 2 The examples described are different. Figure 2 The number and arrangement of components shown are provided as an example. In reality, with... Figure 2 Compared to the components shown, there may be additional components, fewer components, different components, or components arranged in a different manner. Furthermore, Figure 2 The two or more components shown can be implemented within a single component, or Figure 2 The single component shown can be implemented as multiple distributed components. Additionally or alternatively, Figure 2 The component collection shown (e.g., one or more components) can be executed as described by Figure 2 The other set of components shown performs one or more functions. For example, although Figure 3 Some components are primarily associated with land vehicles, but other types of vehicles are also within the scope of this disclosure.

[0041] Figure 1 This is a diagram illustrating example components of device 300 according to the present disclosure. Device 300 may correspond to... Figure 2 The depicted onboard system 120, remote device 130, or network node 150 Figure 3 The depicted onboard system 200 or onboard device 208 and / or any other device, system, subsystem, or component described herein. In some aspects, onboard system 120, remote device 130, network node 150, onboard system 200, onboard device 208, and / or other device, system, subsystem, or component described herein may include one or more devices 300 and / or one or more components of device 300. Figure 3 As shown, device 300 may include bus 305, processor 310, memory 315, input component 320, output component 325, communication component 330 and / or stop information component 335.

[0042] Bus 305 may include one or more components that enable wired and / or wireless communication between components of device 300. Bus 305 may connect components such as via operative coupling, communicative coupling, electronic coupling, and / or electrical coupling. Figure 3Two or more components are coupled together. For example, bus 305 may include electrical connections (e.g., wires, traces, and / or leads) and / or wireless buses. Processor 310 may include a central processing unit, graphics processing unit, microprocessor, controller, microcontroller, digital signal processor, field-programmable gate array, application-specific integrated circuit, and / or another type of processing component. Processor 310 may be implemented in hardware, firmware, or a combination of hardware and software. In some aspects, processor 310 may include one or more processors capable of being programmed to perform one or more operations or processes described elsewhere herein.

[0043] Memory 315 may include volatile memory and / or non-volatile memory. For example, memory 315 may include random access memory (RAM), read-only memory (ROM), hard disk drive, and / or another type of memory (e.g., flash memory, magnetic memory, and / or optical memory). Memory 315 may include internal memory (e.g., RAM, ROM, or hard disk drive) and / or removable memory (e.g., removable via a universal serial bus connection). Memory 315 may be a non-transitory computer-readable medium. Memory 315 may store information related to the operation of device 300, one or more instructions, and / or software (e.g., one or more software applications). In some aspects, memory 315 may include one or more memories, such as those coupled (e.g., communicatively coupled) to one or more processors (e.g., processor 310) via bus 305. The communicative coupling between processor 310 and memory 315 enables processor 310 to read and / or process information stored in memory 315 and / or store information in memory 315.

[0044] Input component 320 enables device 300 to receive input, such as user input and / or sensed input. For example, input component 320 may include a touchscreen, keyboard, keypad, mouse, button, microphone, switch, sensor, GPS sensor, GNSS sensor, accelerometer, gyroscope, and / or actuator. Output component 325 enables device 300 to provide output, such as via a display, speaker, and / or light-emitting diode. Communication component 330 enables device 300 to communicate with other devices via wired and / or wireless connections. For example, communication component 330 may include a receiver, transmitter, transceiver, modem, network interface card, and / or antenna.

[0045] The stop information component 335 may receive sensor data collected by a set of available sensors (e.g., one or more sensors included in the onboard system 120); select a stop detection mode to be used in connection with determining stop information associated with a movable object (e.g., vehicle 110); select one or more selected sensors from the set of available sensors to be used in connection with determining the stop information, at least in part based on the stop detection mode; and determine the stop information based on the stop detection mode and using the sensor data collected by the one or more selected sensors.

[0046] Device 300 may perform one or more operations or procedures described herein. For example, a non-transitory computer-readable medium (e.g., memory 315) may store a set of instructions (e.g., one or more instructions or code) for execution by processor 310. Processor 310 may execute the set of instructions to perform one or more operations or procedures described herein. In some aspects, execution of the set of instructions by one or more processors 310 causes one or more processors 310 and / or device 300 to perform one or more operations or procedures described herein. In some aspects, hardwired circuitry may be used in place of or in combination with instructions to perform one or more operations or procedures described herein. Additionally or alternatively, processor 310 may be configured to perform one or more operations or procedures described herein. Thus, the aspects described herein are not limited to any particular combination of hardware circuitry and software.

[0047] In some aspects, device 300 may include: components for acquiring scene information associated with determining stopping information associated with a movable object (e.g., vehicle 110); components for receiving sensor data collected by a set of available sensors; components for selecting a stopping detection mode to be used in connection with determining the stopping information, wherein the stopping detection mode is selected at least in part based on the scene information and the sensor data collected by the set of available sensors; components for selecting one or more selected sensors from the set of available sensors to be used in connection with determining the stopping information, at least in part based on the stopping detection mode; and / or components for determining the stopping information based on the stopping detection mode and using the sensor data collected by the one or more selected sensors. In some aspects, the components for device 300 to perform the processes and / or operations described herein may include combinations of... Figure 2 One or more components of the described device 300, such as bus 305, processor 310, memory 315, input component 320, output component 325, communication component 330, and / or stop information component 335. Additionally or alternatively, components for the device 300 to perform the processes and / or operations described herein may include combinations of... Figure 3One or more components of the described onboard system 200, such as sensor subsystem 204, control subsystem 206 and / or onboard device 208, etc.

[0048] Figure 3 The number and arrangement of components shown are provided as an example. Figures 4A to 4E Compared to the components shown, device 300 may include additional components, fewer components, different components, or components arranged in a different manner. Additionally or alternatively, the set of components of device 300 (e.g., one or more components) may perform one or more functions described as being performed by another set of components of device 300.

[0049] Figure 4A This is a diagram illustrating an example of a stop detection system according to this disclosure. For example... Figure 4A As shown, Example 400 includes a vehicle 450 (e.g., vehicle 110) equipped with a system 455 (e.g., onboard system 120, onboard system 200 and / or onboard device 208) that supports autonomous driving and / or ADAS technologies, and a collection of available sensors 460 (e.g., one or more motion sensors 226, one or more speed sensors 228, one or more positioning sensors 234, one or more cameras 236, lidar system 238, one or more ranging systems 240, etc.).

[0050] like Figure 4B As shown, at reference numeral 402, system 455 can obtain scene information associated with determining stopping information associated with a movable object (e.g., vehicle 450). Stopping information includes information related to performing stopping detection associated with vehicle 450. For example, stopping information may include information indicating one or more points in time (or time periods) during which vehicle 450 is determined to have stopped (e.g., vehicle 450 is not in motion). Similarly, stopping information may include information indicating one or more points in time (or time periods) during which vehicle 450 is determined not to have stopped (e.g., vehicle 450 is in motion).

[0051] Scene information includes information defining the scene or application in which the stopping information determined by system 455 will be utilized. In some aspects, the scene defined by the scene information can be an online scene. As used herein, an online scene refers to a scene in which the stopping information will be used in real-time or near real-time. In an online scene, it may be necessary for system 455 to process sensor data in real-time or near real-time in association with determining the stopping information. An example of an online scene is a real-time positioning scene (e.g., when system 455 will perform real-time positioning of vehicle 450). Another example of an online scene is a UAV flight scene (e.g., when system 455 will perform motion control on vehicle 450 in the form of a UAV). Generally, in an online scene, stopping information needs to be determined when vehicle 450 is in operation and / or using one or more available sensors 460 for real-time or near real-time positioning.

[0052] Additionally or alternatively, the scenario defined by the scenario information can be an offline scenario. As used herein, an offline scenario refers to a scenario where stop information will not be used in real-time or near real-time. In an offline scenario, real-time or near real-time processing of sensor data is not required, meaning that sensor data may be buffered or stored (e.g., by one or more sensors 460 or by system 455) for a period of time before system 455 determines the stop information. An example of an offline scenario is an HD map building scenario. In an HD map building scenario, stop information may not be needed in real-time or near real-time, meaning that sensor data may be buffered or stored for a period of time (e.g., five minutes) before processing associated with generating an HD map. Another example of an offline scenario is a SLAM data scenario where system 455 will generate SLAM data.

[0053] In some aspects, scenario information may indicate one or more scenarios. For example, in some aspects, scenario information may include information associated with one or more online scenarios and information associated with one or more offline scenarios. In some aspects, scenario information may indicate a single scenario. For example, in some aspects, scenario information may include only information associated with online scenarios. As another example, in some aspects, scenario information may include only information associated with offline scenarios. In some aspects, scenario information may indicate a user-defined or user-customized scenario (e.g., compared to a default scenario configured on system 455).

[0054] In some aspects, scene information may indicate one or more sensors and / or one or more types of sensor data associated with the scene. That is, scene information may indicate one or more sensors 460 and / or one or more types of sensor data that can be used in the scene defined by the scene information. As a specific example, scene information associated with an online scene may indicate one or more types of sensor data (e.g., to be provided by one or more sensors 460) that can be used in conjunction with real-time or near-real-time determination of stopping information, such as image data, GNSS data, OBD data, CAN sensor data, IMU data, etc. As another specific example, scene information associated with an offline scene may indicate one or more types of sensor data (e.g., to be provided by one or more sensors 460) that can be used in conjunction with non-real-time determination of stopping information, such as IMU data, OBD data, accelerometer data, etc.

[0055] In some respects, system 455 may obtain scene information via user input (e.g., input provided via a user device (not shown)). That is, in some respects, the user may provide input defining scene information, and system 455 may obtain scene information based on the user input. Additionally or alternatively, system 455 may obtain scene information based on configuration (e.g., the initial configuration of system 455).

[0056] As indicated by reference numeral 404, system 455 may receive sensor data collected by a set of available sensors 460. The sensor data may include, for example, data collected by one or more motion sensors (e.g., one or more motion sensors 226) (such as accelerometers, gyroscopes, or IMUs). As another example, the sensor data may include data collected by one or more speed sensors (e.g., one or more speed sensors 228) (such as one or more speed sensors communicating with system 455 via a CAN bus). As another example, the sensor data may include data collected by one or more positioning sensors (e.g., one or more positioning sensors 234) (such as GNSS sensors or GPS sensors). As another example, the sensor data may include data collected by one or more cameras (e.g., one or more cameras 236). As another example, the sensor data may include data collected by a lidar system (e.g., lidar system 238). As another example, the sensor data may include data collected by one or more ranging systems (e.g., one or more ranging systems 240) (such as radar systems or sonar systems).

[0057] In some respects, system 455 may receive one or more sensor data items in real time or near real time. Additionally or alternatively, system 455 may receive one or more sensor data items in non-real time. For example, system 455 may receive one or more sensor data items after sensor 460, which provides one or more sensor data items, has processed or buffered the one or more sensor data items (e.g., such that the one or more sensor data items are not provided to system 455 in real time or near real time).

[0058] As indicated by reference numeral 406 in the attached figure, system 455 can select the stop detection mode to be used in conjunction with determining stop information. The stop detection mode is the mode in which system 455 determines the stop information associated with vehicle 450. The stop detection mode can be, for example, an online stop detection mode or an offline stop detection mode. An online stop detection mode is an operating mode in which system 455 determines stop information in real-time or near real-time. Conversely, an offline stop detection mode is an operating mode in which system 455 determines stop information in non-real-time.

[0059] In some respects, system 455 may select a stop detection mode based at least in part on scene information and sensor data. For example, system 455 may identify a set of available sensors 460 from which system 455 has received sensor data. System 455 may then compare the information identifying the set of available sensors 460 with scene information to identify a scene indicated by the scene information that requires sensor data from the set of sensors 460 identified by system 455. Thus, system 455 may identify an applicable scene and may determine, at least in part, whether the scene is an online scene or an offline scene based on scene information. System 455 may then select a stop detection mode (e.g., online stop detection mode, offline stop detection mode, user-configured stop detection mode).

[0060] Additionally or alternatively, system 455 may select a stop detection mode based at least in part on a determination of whether sensor data is received by system 455 in real-time or near real-time. For example, system 455 may determine (e.g., based at least in part on information included in the sensor data) that sensor data is being received by system 455 in real-time or near real-time. In this example, system 455 may select an online stop detection mode as the selected stop detection mode based at least in part on whether the sensor data is received in real-time or near real-time. That is, in some implementations, receiving sensor data in real-time or near real-time may instruct system 455 to select an online stop detection mode. As another example, system 455 may determine (e.g., based at least in part on information included in the sensor data) that sensor data is not received by system 455 in real-time or near real-time (e.g., when sensor data is received in multiple batches or sets of data). In this example, system 455 may select an offline stop detection mode as the selected stop detection mode based at least in part on whether the sensor data is not received in real-time or near real-time. In other words, in some specific implementations, receiving sensor data in a non-real-time manner can instruct the system 455 to select an offline stop detection mode.

[0061] As indicated by reference numeral 408 in the accompanying drawings, system 455 may select one or more selected sensors 460 from the set of available sensors 460 to be used in association with determining stop information, at least in part, based on a stop detection mode. That is, in some aspects, system 455 may select one or more available sensors 460 that provide sensor data, wherein the selection of the one or more selected sensors 460 is at least in part based on a selected stop detection mode. For example, an online stop detection mode may be configured such that sensor data collected by a first specific subset of the set of available sensors 460 will be used in association with determining stop information according to the online detection mode. As another example, an offline stop detection mode may be configured such that sensor data collected by a second specific subset of the set of available sensors 460 will be used in association with determining stop information according to the offline detection mode.

[0062] In one example, if the scene information indicates a UAV flight scenario and the set of available sensors 460 includes an IMU, then system 455 may select an online stop detection mode and select the IMU to provide sensor data to be used to determine stop information. In another example, if the scene information indicates a SLAM data scenario and the set of available sensors 460 includes an OBD sensor and an IMU, then system 455 may select an offline stop detection mode and select both the OBD sensor and the IMU to provide sensor data to be used to determine stop information.

[0063] In some aspects, system 455 may select one or more sensors 460 based at least in part on priority ordering information associated with the set of available sensors 460 (e.g., the set of sensors 460 from which system 455 receives sensor data). Priority information may include, for example, an order of priority associated with the set of sensors 460 (e.g., from highest to lowest). Here, system 455 may select one or more sensors 460 based at least in part on priority ordering information. In some aspects, priority ordering information may be configured, for example, to give preference to relatively more reliable or relatively more accurate sensors 460 (e.g., one or more sensors 460 communicating via a CAN bus, OBD sensor, etc.) in a given stop detection mode.

[0064] In some aspects, one or more selected sensors 460 may include one or more primary selected sensors 460 and one or more secondary selected sensors 460. One or more primary selected sensors 460 may be used to determine stop information, while one or more secondary selected sensors 460 may be used to correct or verify the stop information determined using one or more primary selected sensors 460. In one example, if scene information indicates a location scene and the set of available sensors 460 includes an OBD sensor and an IMU, then system 455 may select an online stop detection mode, selecting an OBD sensor as the primary selected sensor 460 for determining stop information, and selecting an IMU as the secondary sensor 460 to be used to correct the stop information (e.g., to increase the accuracy or reliability of the stop information).

[0065] As indicated by reference numeral 410, system 455 can determine stop information based on a stop detection mode and using sensor data collected by one or more selected sensors 460. In some aspects, the sensor data may include data received as described above with respect to reference numeral 404. Additionally or alternatively, the sensor data may include data received at a later point in time (e.g., after system 455 selects the stop detection mode and / or one or more selected sensors 460).

[0066] In some respects, the selected stop detection mode is an online stop detection mode, and system 455 can use the online stop detection mode to determine stop information.

[0067] In some aspects, when determining stop information based on an online stop detection mode, system 455 may determine the stop information at least in part based on sensor data indicating the speed of vehicle 450. For example, system 455 may receive sensor data including information indicating the speed of vehicle 450 at a given time. Here, system 455 may determine whether the information indicating the speed of vehicle 450 at a given time indicates that the speed of vehicle 450 meets a threshold (e.g., greater than zero feet per second). Here, if the speed of vehicle 450 meets the threshold, system 455 may determine stop information indicating that vehicle 450 is in motion (i.e., not stopped) at the given time. Conversely, if the speed of vehicle 450 does not meet the threshold, system 455 may determine stop information indicating that vehicle 450 has stopped at the given time. In some aspects, the sensor data used to achieve this determination may be provided by, for example, an OBD sensor, a speed sensor on a CAN bus, a GNSS sensor, etc. In some aspects, to improve the accuracy of this determination, system 455 may utilize sensor data provided by multiple (different) sensors 460.

[0068] Additionally or alternatively, when determining stopping information based on an online stopping detection mode, system 455 may determine the stopping information at least in part based on sensor data including one or more images (e.g., system 455 may utilize image-based stopping detection). For example, system 455 may receive sensor data including a set of images of the environment of vehicle 450, where each image is associated with a different point in time. Here, system 455 may process the set of images (e.g., using image processing techniques, deep learning techniques, etc.) to determine whether vehicle 450 has stopped at a given point in time.

[0069] Additionally or alternatively, when determining stop information according to the online stop detection mode, system 455 may use sensor data provided by the IMU to determine the stop information. That is, in some aspects, the online stop detection mode may use IMU data in association with determining stop information. Therefore, in some aspects, system 455 may perform IMU-based stop detection in real time or near real time (e.g., using sensor data when system 455 receives sensor data).

[0070] In some aspects, in connection with using IMU data to determine stop information based on an online stop detection mode, system 455 may filter the IMU data to create filtered IMU data. For example, system 455 may perform variance filtering on the signal carrying the IMU data. In some aspects, to perform variance filtering on the IMU data, system 455 may calculate one or more variances of the IMU data over one or more corresponding time periods (e.g., variances calculated based on a set of 20 or 30 consecutive signal values). System 455 may then filter the IMU data at least partially based on the variance (e.g., by replacing the signal values ​​with the calculated variance). Figure 4B This is an exemplary example of variance filtering that can be performed by System 455. Figures 4C to 4D In this example, system 455 calculates a first variance V1 over a first set of 30 consecutive signal values ​​(e.g., signal value 1 to signal value 30) carrying IMU data. Here, system 455 replaces signal value 30 with the first variance V1. Similarly, system 455 calculates a second variance V2 over a second set of 30 consecutive signal values ​​(e.g., signal value 2 to signal value 31) carrying IMU data. Here, system 455 replaces signal value 31 with the second variance V2. System 455 can continue performing variance filtering for additional signal values ​​in this manner. In this example, system 455 uses IMU data starting with value 31 in association with performing stop detection according to an online stop detection mode.

[0071] It is noteworthy that the accumulated error of IMU data is typically caused by various interference factors, which is a common drawback of using IMU data to determine stop information. However, by performing variance filtering, system 455 smooths the IMU data signal and reduces noise interference, thereby improving the reliability of the IMU data and, consequently, the reliability of the stop information determined using the IMU data. In some aspects, the IMU data comprises nine axes of data, including three axes of angular velocity data, three axes of acceleration data, and three axes of magnetometer data, and system 455 performs variance filtering on the signals associated with one or more of these axes (e.g., the three axes of angular velocity data and the three axes of acceleration data).

[0072] Figure 4C Simulation results associated with variance filtering of acceleration data included in IMU data are illustrated. Figure 4D The example illustrates three axes of IMU acceleration data over a time period, while Figure 4C This example illustrates the three axes of IMU acceleration data after variance filtering by system 455. For example, by comparison... Figure 4D and Figure 4BIt is evident that variance filtering provides a relatively smoother signal and reduces noise, which implies an increase in the reliability of IMU acceleration data and therefore the reliability of stopping information determined at least in part based on IMU acceleration data.

[0073] In some aspects, system 455 may determine stopping information using filtered IMU data based on an online stop detection mode. For example, in online stop detection mode and using filtered IMU data, system 455 may, in some aspects, calculate a stopping score based at least in part on the filtered IMU data. The stopping score may be, for example, a value indicating whether vehicle 450 has stopped (e.g., is not in motion) at a given time point. In some aspects, system 455 may calculate the stopping score based at least in part on one or more other motion scores, which are calculated at least in part based on IMU data, such as an acceleration score associated with a given time point (e.g., a score calculated at least in part based on filtered IMU acceleration data) and an angular velocity score associated with a given time point (e.g., a score calculated at least in part based on filtered IMU angular velocity data).

[0074] In one example, an acceleration fraction algorithm can be used to configure system 455. This algorithm receives three values ​​of filtered acceleration data (e.g., x-axis, y-axis, and z-axis values ​​associated with a given time point) as input and provides an acceleration fraction as output. In some aspects, the acceleration fraction algorithm can be configured such that the acceleration fraction increases as the value in the filtered IMU acceleration data increases. Therefore, in some aspects, the acceleration fraction algorithm can be configured such that the acceleration fraction increases as the acceleration of vehicle 450 increases from zero (e.g., away from zero). Furthermore, an angular velocity fraction algorithm can be used to configure system 455. This algorithm receives three values ​​of filtered angular velocity data (e.g., x-axis, y-axis, and z-axis values ​​associated with a given time point) as input and provides an angular velocity fraction as output. In some aspects, the angular velocity fraction algorithm can be configured such that the angular velocity fraction increases as the value in the filtered IMU angular velocity data increases. Therefore, in some respects, the angular velocity fraction algorithm can be configured such that the angular velocity fraction increases as the angular velocity of the vehicle 450 increases from zero (e.g., away from zero).

[0075] In some implementations, system 455 may calculate the stopping score based at least in part on a set of motion scores. For example, system 455 may be configured using a stopping score algorithm that takes acceleration scores and angular velocity scores (e.g., associated with a given time point) as input and provides a stopping score as output. In one example, the stopping score algorithm may be configured to determine (1) whether the acceleration score is approximately equal to zero and (2) whether the angular velocity score meets an angular velocity threshold (e.g., less than or equal to 2). Here, if both (1) and (2) are true, system 455 may determine a stopping score (e.g., a value of 1) indicating that vehicle 450 has stopped. Thus, in this example, stopping information may include an indication that vehicle 450 has stopped at a given time point. Conversely, if (1) or (2) is not met, system 455 may determine a stopping score (e.g., a value of 0) indicating that vehicle 450 is in motion (e.g., not stopped). Thus, in this example, stopping information may include an indication that vehicle 450 has not stopped at a given time point. In this way, the online stop detection mode enables the system 455 to use IMU data to determine reliable and accurate stop information in real time or near real time.

[0076] In some aspects, the selected stop detection mode is an offline stop detection mode, and system 455 can use the offline stop detection mode to determine stop information. In some aspects, when determining stop information according to the offline stop detection mode, system 455 can use sensor data provided by the IMU to determine the stop information. That is, in some aspects, the offline stop detection mode can use IMU data in association with determining stop information. Therefore, in some aspects, system 455 can perform IMU-based stop detection non-real-time (e.g., using a batch of sensor data after system 455 buffers the sensor data).

[0077] In some respects, in connection with using IMU data to determine stop information based on an offline stop detection mode, system 455 may filter the IMU data to create filtered IMU data. For example, system 455 may perform variance filtering on the IMU data (e.g., in conjunction with the above). Figure 4E (Similar to the described method).

[0078] In some respects, the system 455 may then determine stop information based at least in part on filtered IMU data, according to the offline stop detection mode. For example, in offline stop detection mode and using filtered IMU data, the system 455 may apply a Fast Fourier Transform (FFT) to the filtered IMU data to create transformed IMU data. In fact, when the vehicle 450 stops, the signal carrying the IMU data is smooth and has relatively low jitter and frequency. Therefore, the FFT can be used to transform the IMU data signal and remove the higher frequency components of the signal while retaining the lower frequency components. In one example, the system 455 may apply an FFT to the filtered IMU data to create transformed IMU data as indicated above. The system 455 may then identify values ​​in the transformed IMU data that satisfy (e.g., are greater than) a signal threshold, and may then set the identified values ​​to zero while keeping other values ​​in the transformed IMU data (i.e., values ​​less than or equal to the signal threshold) unchanged. In this manner, system 455 can filter the transformed IMU data to create filtered transformed IMU data. Then, system 455 can apply an inverse FFT (IFFT) to the filtered transformed IMU data to create modified IMU data. Here, the time points in the modified IMU data that are greater than zero indicate the time points in which vehicle 450 stops. Therefore, in this example, system 455 can determine stopping information at least in part based on the modified IMU data (e.g., one or more time points in the modified IMU data that are greater than zero are indicated as the time points in which vehicle 450 stops).

[0079] Figure 4E Simulation results associated with using IMU data to determine stop information based on offline stop detection mode are illustrated. Figure 4E The figure above illustrates one axis of IMU acceleration data after applying variance filtering over a period of time. Figure 4E The illustration in the middle shows how to use the diagram to illustrate the process. Figure 4E The above figure shows the filtered IMU data transformed by applying FFT to the IMU data. Figure 4E The figure below illustrates modified IMU data resulting from applying further filtering to the transformed IMU data and then applying IFFT to the filtered transformed IMU data. Figure 4E In the figure below, the time periods during which vehicle 450 stopped can be easily identified as the periods when the modified IMU data values ​​were non-zero. It is worth noting that FFT and IFFT operations are relatively low-complexity operations, and therefore, stop information can be determined with relatively low resource consumption (e.g., processing resource consumption, power consumption, etc.).

[0080] In some respects, system 455 can use a single axis of IMU data according to the offline stop detection mode (e.g., such as...).Figures 4A to 4E The system 455 determines stopping information using IMU data associated with a single axis of the illustrated acceleration data. Alternatively, in some aspects, the system 455 may determine stopping information using IMU data associated with multiple axes of the IMU data (e.g., one or more axes of the IMU acceleration data and / or one or more axes of the IMU angular velocity data) according to an offline stopping detection mode. In some aspects, using multiple axes of the data improves the reliability and robustness associated with determining stopping information. In some aspects, when using multiple axes of the IMU data, the system 455 may be configured to determine the final stopping information based at least in part on the stopping information associated with each of the multiple axes. For example, the system 455 may be configured to identify the time point at which the vehicle 450 stops as the time point at which the IMU data indicates the stopping of the vehicle 450 using a threshold number of axes (e.g., at least two, at least half, all, etc.).

[0081] In some aspects, system 455 may utilize a variety of techniques associated with determining stop information. For example, in some aspects, system 455 may use an online stop detection mode to determine stop information based at least in part on sensor data indicating the speed of vehicle 450, and (e.g., using an online stop detection mode and / or using an offline stop detection mode) at least in part on sensor data provided by an IMU. As another example, in some aspects, system 455 may use an online stop detection mode to determine stop information based at least in part on sensor data including one or more images, and (e.g., using an online stop detection mode and / or using an offline stop detection mode) at least in part on sensor data provided by an IMU. As another example, in some aspects, system 455 may use an online stop detection mode to determine stop information based at least in part on sensor data provided by an IMU and an offline stop detection mode to determine stop information based at least in part on sensor data provided by an IMU. In some aspects, a variety of techniques may be utilized to achieve verification of stop information, correction of stop information, redundancy associated with determining stop information, and / or diversity associated with determining stop information, thereby improving the accuracy or reliability of the stop information determined by system 455.

[0082] In some respects, system 455 can correct the stop information. For example, system 455 can determine, at least in part, that correction information will be applied to the stop information based on information associated with the set of available sensors 460. For example, system 455 can determine that the set of available sensors 460 includes one or more sensors that can be used to determine additional stop information (e.g., in addition to stop information determined using one or more other sensors 460 in the set of available sensors 460). Here, system 455 can determine correction information (e.g., additional stop information) and can correct the stop information at least in part based on the correction information. In this way, if available sensor data for performing correction is available, system 455 can enable a correction mode and can therefore correct (or verify) the stop information. Thus, the accuracy or reliability of the stop information as determined by system 455 can be improved.

[0083] In one example, system 455 may use sensor data indicating the speed of vehicle 450 at a given time point to determine stopping information (e.g., according to an online stop detection mode). System 455 may also determine correction information based at least in part on sensor data provided by an IMU (e.g., according to an online stop detection mode or an offline stop detection mode). System 455 may then correct (or verify) the speed-based stopping information based at least in part on the IMU-based correction information. In another example, system 455 may use sensor data provided by an IMU to determine stopping information (e.g., according to an online stop detection mode or an offline stop detection mode). System 455 may also determine correction information based at least in part on sensor data indicating the speed of vehicle 450 at a given time point and / or sensor data including one or more images (e.g., according to an online stop detection mode). System 455 may then correct (or verify) the IMU-based stopping information based at least in part on speed-based and / or image-based correction information.

[0084] As indicated above, Figures 4A to 4E This is provided as an example. Other examples are available with reference to [the relevant information]. Figure 5 The examples described are different.

[0085] Figure 5 This is a flowchart of an example process 500 associated with a stop detection system according to this disclosure. In some aspects, Figure 5 One or more process frames are executed by the system (e.g., onboard system 120). In some respects, Figure 5 One or more process frames are performed by another device or a group of devices separate from or including the system, such as remote devices (e.g., remote device 130). Additionally or alternatively, Figure 5One or more process frames may be executed by one or more components of device 300, such as processor 310, memory 315, input component 320, output component 325, communication component 330 and / or stop information component 335.

[0086] like Figure 5 As shown, process 500 may include: obtaining scene information associated with determining stop information associated with a movable object (box 510). For example, the system may obtain scene information associated with determining stop information associated with a movable object, as described above.

[0087] like Figure 5 As further shown, process 500 may include receiving sensor data collected from a set of available sensors (block 520). For example, the system may receive sensor data collected from a set of available sensors, as described above.

[0088] like Figure 5 As further shown, process 500 may include: selecting a stop detection mode to be used by the system in association with determined stop information, the stop detection mode being selected based at least in part on scene information and sensor data collected from the set of available sensors (box 530). For example, the system may select a stop detection mode to be used by the system in association with determined stop information, the stop detection mode being selected based at least in part on scene information and sensor data collected from the set of available sensors, as described above.

[0089] like Figure 5 As further shown, process 500 may include: selecting one or more selected sensors from the set of available sensors to be used in association with determining stop information, at least in part based on a stop detection mode (block 540). For example, the system may select one or more selected sensors from the set of available sensors to be used in association with determining stop information, as described above, at least in part based on a stop detection mode.

[0090] like Figure 5 As further shown, process 500 may include: determining stop information based on a stop detection mode and using sensor data collected by one or more selected sensors (block 550). For example, the system may determine stop information based on a stop detection mode and using sensor data collected by one or more selected sensors, as described above.

[0091] Process 500 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0092] In the first aspect, the scene information is obtained through user input.

[0093] In the second aspect, either alone or in combination with the first aspect, the stop detection mode is selected based at least in part on the determination of whether sensor data collected by the set of available sensors is received by the system in real time or near real time.

[0094] In the third aspect, either alone or in combination with one or more of the first and second aspects, at least in part based on sensor data collected by the set of available sensors being received by the system in real time or near real time, the selected stop detection mode is an online stop detection mode.

[0095] In the fourth aspect, either alone or in combination with one or more of the first to third aspects, at least in part based on the fact that the sensor data collected by the set of available sensors is not received by the system in real time or near real time, the selected stop detection mode is an offline detection mode.

[0096] In the fifth aspect, individually or in combination with one or more of the first to fourth aspects, one or more of the selected sensors are selected based at least in part on priority ranking information associated with the set of available sensors.

[0097] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, process 500 includes: determining, at least in part, based on information associated with a set of available sensors, that correction information will be applied to stop information; determining the correction information; and correcting the stop information, at least in part, based on the correction information, to create corrected stop information.

[0098] although Figure 5 An example box for process 500 is shown, but in some respects, it differs from... ​ Compared to the boxes depicted, process 500 includes additional boxes, fewer boxes, different boxes, or boxes arranged in a different manner. Additionally or alternatively, two or more boxes in process 500 may be executed in parallel.

[0099] The following provides an overview of some aspects of this disclosure:

[0100] Aspect 1: A method comprising: obtaining scene information associated with determining stop information associated with a movable object by a system; receiving sensor data collected by the system from a set of available sensors; selecting a stop detection mode to be used by the system in connection with determining the stop information, the stop detection mode being selected at least in part based on the scene information and the sensor data collected by the set of available sensors; selecting one or more selected sensors from the set of available sensors to be used in connection with determining the stop information by the system and at least in part based on the stop detection mode; and determining the stop information by the system according to the stop detection mode and using the sensor data collected by the one or more selected sensors.

[0101] Aspect 2: According to the method of aspect 1, the scenario information is obtained via user input.

[0102] Aspect 3: The method according to any one of Aspects 1 to 2, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the system in real time or near real time.

[0103] Aspect 4: The method according to any one of Aspects 1 to 3, wherein the sensor data collected by the set of available sensors is received by the system in real time or near real time, and the selected stop detection mode is an online stop detection mode.

[0104] Aspect 5: The method according to any one of Aspects 1 to 4, wherein the sensor data collected by the set of available sensors is not received by the system in real time or near real time, and the selected stop detection mode is an offline detection mode.

[0105] Aspect 6: The method according to any one of Aspects 1 to 5, wherein the one or more selected sensors are selected at least in part based on priority ordering information associated with the set of available sensors.

[0106] Aspect 7: The method according to any one of Aspects 1 to 6, the method further comprising: determining, at least in part, based on information associated with the set of available sensors, that correction information will be applied to the stop information; determining the correction information; and correcting the stop information, at least in part, based on the correction information, to create corrected stop information.

[0107] Aspect 8: A system for wireless communication, the system comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors being configured to cause the system to: acquire scene information associated with determining stopping information associated with a movable object; receive sensor data collected by a set of available sensors; select a stopping detection mode to be used by the system in connection with determining the stopping information, the stopping detection mode being selected at least in part based on the scene information and the sensor data collected by the set of available sensors; select one or more selected sensors from the set of available sensors to be used in connection with determining the stopping information, at least in part based on the stopping detection mode; and determine the stopping information according to the stopping detection mode and using the sensor data collected by the one or more selected sensors.

[0108] Aspect 9: In the system according to aspect 8, the scene information is obtained via user input.

[0109] Aspect 10: The system according to any one of Aspects 8 to 9, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the system in real time or near real time.

[0110] Aspect 11: The system according to any one of Aspects 8 to 10, wherein the sensor data collected by the set of available sensors is received by the system in real time or near real time, and the selected stop detection mode is an online stop detection mode.

[0111] Aspect 12: The system according to any one of Aspects 8 to 11, wherein the selected stop detection mode is an offline detection mode, wherein the sensor data collected by the set of available sensors is not received by the system in real time or near real time, at least in part.

[0112] Aspect 13: The system according to any one of Aspects 8 to 12, wherein the one or more selected sensors are selected at least in part based on priority ordering information associated with the set of available sensors.

[0113] Aspect 14: The system according to any one of Aspects 8 to 13, wherein the one or more processors are further configured to cause the system to: determine, at least in part, based on information associated with the set of available sensors, that correction information will be applied to the stop information; determine the correction information; and correct the stop information at least in part based on the correction information to create corrected stop information.

[0114] Aspect 15: A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising: one or more instructions, which, when executed by one or more processors of a system, cause the system to: obtain scene information associated with determining stop information associated with a movable object; receive sensor data collected by a set of available sensors; select a stop detection mode to be used by the system in connection with determining the stop information, the stop detection mode being selected at least in part based on the scene information and the sensor data collected by the set of available sensors; select one or more selected sensors from the set of available sensors to be used in connection with determining the stop information, at least in part based on the stop detection mode; and determine the stop information according to the stop detection mode and using the sensor data collected by the one or more selected sensors.

[0115] Aspect 16: The non-transitory computer-readable medium according to aspect 15, wherein the scene information is obtained via user input.

[0116] Aspect 17: A non-transitory computer-readable medium according to any one of Aspects 15 to 16, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the system in real time or near real time.

[0117] Aspect 18: A non-transitory computer-readable medium according to any one of Aspects 15 to 17, wherein the sensor data collected by the set of available sensors is received by the system in real time or near real time, and the selected stop detection mode is an online stop detection mode.

[0118] Aspect 19: A non-transitory computer-readable medium according to any one of Aspects 15 to 18, wherein the selected stop detection mode is an offline detection mode, wherein the sensor data collected by the set of available sensors is not received by the system in real time or near real time.

[0119] Aspect 20: A non-transitory computer-readable medium according to any one of aspects 15 to 19, wherein the one or more selected sensors are selected at least in part based on priority ordering information associated with the set of available sensors.

[0120] Aspect 21: A non-transitory computer-readable medium according to any one of aspects 15 to 20, wherein one or more instructions further cause the system to: determine, at least in part, based on information associated with the set of available sensors, that correction information will be applied to the stop information; determine the correction information; and correct the stop information at least in part based on the correction information to create corrected stop information.

[0121] Aspect 22: An apparatus for wireless communication, the apparatus comprising: means for obtaining scene information associated with determining stop information associated with a movable object; means for receiving sensor data collected by a set of available sensors; means for selecting a stop detection mode to be used by the apparatus in connection with determining the stop information, the stop detection mode being selected at least in part based on the scene information and the sensor data collected by the set of available sensors; means for selecting one or more selected sensors from the set of available sensors to be used in connection with determining the stop information, at least in part based on the stop detection mode; and means for determining the stop information according to the stop detection mode and using the sensor data collected by the one or more selected sensors.

[0122] Aspect 23: The apparatus according to aspect 22, wherein the scene information is obtained via user input.

[0123] Aspect 24: The apparatus according to any one of Aspects 22 to 23, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the apparatus in real time or near real time.

[0124] Aspect 25: The apparatus according to any one of Aspects 22 to 24, wherein the sensor data collected by the set of available sensors is received by the apparatus in real time or near real time, and the selected stop detection mode is an online stop detection mode.

[0125] Aspect 26: The apparatus according to any one of Aspects 22 to 25, wherein the selected stop detection mode is an offline detection mode, wherein the sensor data collected by the set of available sensors is not received by the apparatus in real time or near real time, at least in part.

[0126] Aspect 27: The apparatus according to any one of aspects 22 to 26, wherein the one or more selected sensors are selected at least in part based on priority ordering information associated with the set of available sensors.

[0127] Aspect 28: The apparatus according to any one of Aspects 22 to 27, the apparatus further comprising: means for determining, at least in part, based on information associated with the set of available sensors, that correction information will be applied to the stop information; means for determining the correction information; and means for correcting the stop information, at least in part, based on the correction information, to create corrected stop information.

[0128] Aspect 29: A system configured to perform one or more of the operations described in aspects 1 to 28.

[0129] Aspect 30: An apparatus comprising: components for performing one or more of the operations described in aspects 1 to 28.

[0130] Aspect 31: A non-transitory computer-readable medium storing an instruction set comprising one or more instructions that, when executed by a device, cause the device to perform one or more of the operations described in aspects 1 to 28.

[0131] Aspect 32: A computer program product comprising: instructions or code for performing one or more of the operations described in aspects 1 to 28.

[0132] While the foregoing disclosure provides examples and descriptions, it is not intended to be exhaustive or to limit aspects to the precise forms disclosed. Modifications and variations can be made based on the foregoing disclosure, or from various aspects of practice.

[0133] As used herein, the term "component" is intended to be interpreted broadly as hardware and / or a combination of hardware and software. Whether referred to as software, firmware, middleware, microcode, hardware description language, or other names, "software" should be interpreted broadly as meaning instructions, instruction sets, code, code segments, program code, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, and / or functions, etc. As used herein, a "processor" is implemented in hardware and / or a combination of hardware and software. It will be apparent to those skilled in the art that the systems and / or methods described herein can be implemented in various forms of hardware and / or combinations of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limiting in any way. Therefore, no specific software code is referenced in this document to describe the operation and behavior of the systems and / or methods, as those skilled in the art will understand that the software and hardware can be designed, at least in part, based on the descriptions herein, to implement the systems and / or methods.

[0134] As used in this article, depending on the context, "meeting the threshold" can mean a value greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, etc.

[0135] Although specific combinations of features are set forth in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features may be combined in ways not specifically set forth in the claims and / or not disclosed in the specification. The disclosure of various aspects includes each dependent claim in combination with each other claim in the set of claims. As used herein, the phrase referring to “at least one of” the list of items means any combination of these items, including a single member. As an example, “at least one of a, b, or c” is intended to cover: a, b, c, a+b, a+c, b+c, and a+b+c, as well as any combination having multiple identical elements (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b, a+c+c, b+b, b+b+b, b+b+c, c+c, and c+c+c, or any other ordering of a, b, and c).

[0136] No element, action, or instruction used herein should be construed as essential or necessary unless explicitly stated otherwise. Furthermore, as used herein, the articles “a” and “an” are intended to include one or more items and are interchangeable with “one or more.” Furthermore, as used herein, the article “described” is intended to include one or more items mentioned in connection with the article “described” and is interchangeable with “one or more.” Furthermore, as used herein, the terms “set” and “group” are intended to include one or more items and are interchangeable with “one or more.” If only one item is desired, the phrase “only one” or similar terminology will be used. Furthermore, as used herein, the terms “have,” “possess,” “have,” etc., are intended to be open-ended terms that do not limit the elements they modify (e.g., an element “having” A may also have B). Furthermore, the phrase “based on” is intended to mean “at least partially based on” unless otherwise explicitly stated. Furthermore, as used herein, the term “or” is intended to be inclusive when used in a series and is interchangeable with “and / or” unless otherwise explicitly stated (e.g., in the case of its use in conjunction with “any” or “only one”).

Claims

1. A system for wireless communication, the system comprising: One or more memory units; and One or more processors coupled to the one or more memories, the one or more processors being configured to cause the system to: Obtain scene information associated with determining the stopping information associated with the movable object; Receive sensor data collected from a set of available sensors; The system selects a stop detection mode to be used in conjunction with determining the stop information, the stop detection mode being selected based at least in part on the scene information and the sensor data collected by the set of available sensors; The selection of one or more selected sensors to be used in association with determining the stop information is based at least in part on the stop detection mode from the set of available sensors. as well as The stop information is determined based on the stop detection mode and using sensor data collected by the one or more selected sensors.

2. The system according to claim 1, wherein the scene information is obtained via user input.

3. The system of claim 1, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the system in real time or near real time.

4. The system of claim 1, wherein the sensor data collected by the set of available sensors is received by the system in real time or near real time, and the selected stop detection mode is an online stop detection mode.

5. The system of claim 1, wherein the selected stop detection mode is an offline detection mode, based at least in part on the sensor data collected by the set of available sensors which is not received by the system in real time or near real time.

6. The system of claim 1, wherein the one or more selected sensors are selected at least in part based on priority ranking information associated with the set of available sensors.

7. The system of claim 1, wherein the one or more processors are further configured to cause the system to: The correction information to be applied to the stop information is determined at least in part based on information associated with the set of available sensors; Determine the correction information; and The stop information is corrected at least in part based on the correction information to create corrected stop information.

8. A method, the method comprising: The system obtains scene information associated with determining the stopping information related to the movable object; The system receives sensor data collected from a set of available sensors; The system selects a stop detection mode to be used in conjunction with determining the stop information, the stop detection mode being selected based at least in part on the scene information and the sensor data collected by the set of available sensors; The system selects one or more selected sensors from the set of available sensors, based at least in part on the stop detection mode, to be used in association with determining the stop information; as well as The system determines the stop information based on the stop detection mode and using sensor data collected by the one or more selected sensors.

9. The method of claim 8, wherein the scene information is obtained via user input.

10. The method of claim 8, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the system in real time or near real time.

11. The method of claim 8, wherein the sensor data collected by the set of available sensors is received by the system in real time or near real time, and the selected stop detection mode is an online stop detection mode.

12. The method of claim 8, wherein the selected stop detection mode is an offline detection mode, based at least in part on the sensor data collected by the set of available sensors not being received by the system in real time or near real time.

13. The method of claim 8, wherein the one or more selected sensors are selected at least in part based on priority ranking information associated with the set of available sensors.

14. The method according to claim 8, further comprising: The correction information to be applied to the stop information is determined at least in part based on information associated with the set of available sensors; Determine the correction information; as well as The stop information is corrected at least in part based on the correction information to create corrected stop information.

15. A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising: One or more instructions, which, when executed by one or more processors of the system, cause the system to: Obtain scene information associated with determining the stopping information associated with the movable object; Receive sensor data collected from a set of available sensors; The system selects a stop detection mode to be used in conjunction with determining the stop information, the stop detection mode being selected based at least in part on the scene information and the sensor data collected by the set of available sensors; The selection of one or more selected sensors to be used in association with determining the stop information is based at least in part on the stop detection mode from the set of available sensors. as well as The stop information is determined based on the stop detection mode and using sensor data collected by the one or more selected sensors.

16. The non-transitory computer-readable medium of claim 15, wherein the scene information is obtained via user input.

17. The non-transitory computer-readable medium of claim 15, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the system in real time or near real time.

18. The non-transitory computer-readable medium of claim 15, wherein the sensor data collected by the set of available sensors is received by the system in real time or near real time, and the selected stop detection mode is an online stop detection mode.

19. The non-transitory computer-readable medium of claim 15, wherein the selected stop detection mode is an offline detection mode, based at least in part on the sensor data collected by the set of available sensors which is not received by the system in real time or near real time.

20. The non-transitory computer-readable medium of claim 15, wherein the one or more selected sensors are selected at least in part based on priority ordering information associated with the set of available sensors.

21. The non-transitory computer-readable medium of claim 15, wherein one or more instructions further cause the system to: The correction information to be applied to the stop information is determined at least in part based on information associated with the set of available sensors; Determine the correction information; and The stop information is corrected at least in part based on the correction information to create corrected stop information.

22. An apparatus for wireless communication, the apparatus comprising: A component used to obtain scene information associated with determining stopping information associated with a movable object; A component for receiving sensor data collected from a set of available sensors; A component for selecting a stop detection mode to be used by the device in association with determining the stop information, the stop detection mode being selected based at least in part on the scene information and the sensor data collected by the set of available sensors; Components for selecting one or more selected sensors from the set of available sensors to be used in association with determining the stop information, based at least in part on the stop detection mode; and A component for determining the stop information based on the stop detection mode and using sensor data collected by the one or more selected sensors.

23. The apparatus of claim 22, wherein the scene information is obtained via user input.

24. The apparatus of claim 22, wherein the stop detection mode is selected at least in part based on a determination of whether the sensor data collected by the set of available sensors is received by the apparatus in real time or near real time.

25. The apparatus of claim 22, wherein the sensor data collected by the set of available sensors is received by the apparatus in real time or near real time, and the selected stop detection mode is an online stop detection mode.

26. The apparatus of claim 22, wherein the selected stop detection mode is an offline detection mode, based at least in part on the sensor data collected by the set of available sensors which is not received by the apparatus in real time or near real time.

27. The apparatus of claim 22, wherein the one or more selected sensors are selected at least in part based on priority ranking information associated with the set of available sensors.

28. The apparatus of claim 22, further comprising: Components for determining, at least in part, that correction information will be applied to the stop information based on information associated with the set of available sensors; Components used to determine the correction information; and A component for correcting the stop information based at least in part on the correction information to create corrected stop information.

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