WO2021077313A1

WO2021077313A1 - Systems and methods for autonomous driving

Info

Publication number: WO2021077313A1
Application number: PCT/CN2019/112648
Authority: WO
Inventors: Muchenxuan Tong; Yun Jiang; Zhen Chen
Original assignee: Beijing Voyager Technology Co., Ltd.
Priority date: 2019-10-23
Filing date: 2019-10-23
Publication date: 2021-04-29
Also published as: CN117163049A; CN112041210A; CN112041210B

Abstract

Systems and methods for autonomous driving are provided. The system may specify a plurality of grids on a two-dimensional plane (510). For each of the plurality of grids, the system may obtain first point cloud data corresponding to the grid at a first time point (520). The system may also determine a first hash value of the first point cloud data (530). For each of the plurality of grids, the system may obtain second point cloud data corresponding to the grid at a second time point (540). The system may also determine a second hash value of the second point cloud data (550). Further, in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, the system may refrain from further processing the second point cloud data (560).

Description

SYSTEMS AND METHODS FOR AUTONOMOUS DRIVING

TECHNICAL FIELD

The present disclosure generally relates to systems and methods for autonomous driving, and in particular, to systems and methods for processing point cloud data associated with the autonomous driving.

BACKGROUND

With the development of micro-electronic and robotic technologies, the exploration of autonomous driving has developed rapidly nowadays. Commonly, an autonomous driving system can sense environmental information by capturing point cloud data by a sensor device (e.g., a Lidar) and processing the point cloud data. Since a capture time interval of point cloud data is relatively short, in some situations, point cloud data captured at a current time point may be partially the same as point cloud data captured at a previous time point. For a part of the point cloud data captured at the current time point, which is the same as a part of the point cloud data captured at the previous time point, if a calculation and/or a processing is still carried out, it may result in a waste of processing resources and a reduction in processing speed. Therefore, it is desirable to provide systems and methods for identifying the part of the point cloud data captured at the current time point which is the same as part of the point cloud data captured at the previous time point, refraining from further processing the part of the point cloud data, and retrieving a previous processing result, thereby improving the efficiency of the processing.

SUMMARY

An aspect of the present disclosure relates to a system for autonomous driving. The system may include a storage medium to store a set of instructions and a processor communicatively coupled to the storage medium. The system may specify a plurality of grids on a two-dimensional plane; for each of the plurality of grids, obtain first point cloud data corresponding to the grid at a first time point; determine a first hash value of the first point cloud data; obtain second point cloud data corresponding to the grid at a second time point; determine a second hash value of the second point cloud data; and in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refrain from further processing the second point cloud data.

In some embodiments, the first time point may be a previous time point for the second time point.

In some embodiments, the system may further retrieve a previous processing result associated with the second point cloud data from a cache.

In some embodiments, the previous processing result associated with the second point cloud data may include a processing result of first point cloud data with a first hash value matching the second hash value.

In some embodiments, the system may further, in response to determining that no first hash value matches with the second hash value, process the second point cloud data.

In some embodiments, the system may further store a processing result of the second point cloud data into the cache.

In some embodiments, the cache may be dynamically released according to a predetermined time interval.

In some embodiments, the first point cloud data and/or the second point cloud data may include point cloud data associated with environmental information within a predetermined range of a vehicle.

In some embodiments, the environmental information within the predetermined range of the vehicle may include road information, height information, and/or static object information.

In some embodiments, the first point cloud data and/or the second point cloud data may be acquired by a sensor device. The sensor device may include a light detection and ranging (Lidar) device.

In some embodiments, a shape of the plurality of grids may include a quadrangle, a hexagon, and/or an irregular polygon.

Another aspect of the present disclosure relates to a method implemented on a computing device including at least one processor, at least one storage medium, and a communication platform connected to a network. The method may include specifying a plurality of grids on a two-dimensional plane; for each of the plurality of grids, obtaining first point cloud data corresponding to the grid at a first time point; determining a first hash value of the first point cloud data; obtaining second point cloud data corresponding to the grid at a second time point; determining a second hash value of the second point cloud data; and in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refraining from further processing the second point cloud data.

In some embodiments, the method may further include retrieving a previous processing result associated with the second point cloud data from a cache.

In some embodiments, the method may further include in response to determining that no first hash value matches with the second hash value, processing the second point cloud data.

In some embodiments, the method may further include storing a processing result of the second point cloud data into the cache.

A further aspect of the present disclosure relates to a vehicle configured for autonomous driving. The vehicle may include a detecting component, a planning component, and a control component. The planning component may be configured to specify a plurality of grids on a two-dimensional plane; for each of the plurality of grids, obtain first point cloud data corresponding to the grid at a first time point; determine a first hash value of the first point cloud data; obtain second point cloud data corresponding to the grid at a second time point; determine a second hash value of the second point cloud data; and in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refrain from further processing the second point cloud data.

In some embodiments, the planning component may be further configured to retrieve a previous processing result associated with the second point cloud data from a cache.

In some embodiments, the planning component may be further configured to, in response to determining that no first hash value matches with the second hash value, process the second point cloud data.

In some embodiments, the planning component may be further configured to store a processing result of the second point cloud data into the cache.

In some embodiments, the first point cloud data and/or the second point cloud data may be acquired by the detecting component. The detecting component may include a light detection and ranging (Lidar) device.

A still further aspect of the present disclosure relates to a system for autonomous driving. The system may include a specification module, a first obtaining module, a first determination module, a second obtaining module, a second determination module, and a processing module. The specification module may be configured to specify a plurality of grids on a two-dimensional ground plane. The first obtaining module may be configured to obtain first point cloud data corresponding to each of the plurality of grids at a first time point. The first determination module may be configured to determine a first hash value of the first point cloud data. The second obtaining module may be configured to obtain second point cloud data corresponding to each of the plurality of grids at a second time point. The second determination module may be configured to determine a second hash value of the second point cloud data. The processing module may be configured to refrain from further processing the second point cloud data in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value.

A still further aspect of the present disclosure relates to a non-transitory computer readable medium including executable instructions. When the executable instructions are executed by at least one processor, the executable instructions may direct the at least one processor to perform a method. The method may include specifying a plurality of grids on a two-dimensional plane; for each of the plurality of grids, obtaining first point cloud data corresponding to the grid at a first time point; determining a first hash value of the first point cloud data; obtaining second point cloud data corresponding to the grid at a second time point; determining a second hash value of the second point cloud data; and in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refraining from further processing the second point cloud data.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities, and combinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. The drawings are not to scale. These embodiments are non-limiting schematic embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary autonomous driving system according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for processing point cloud data according to some embodiments of the present disclosure;

FIG. 6-A and FIG. 6-B are schematic diagrams illustrating exemplary grids on a two-dimensional plane according to some embodiments of the present disclosure;

FIG. 7 is a schematic diagram illustrating an exemplary process for matching point cloud data captured at different time points according to some embodiments of the present disclosure; and

FIG. 8 is a schematic diagram illustrating an exemplary dynamic release of a cache according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled in the art to make and use the present disclosure and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a, ” “an, ” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise, ” “comprises, ” and/or “comprising, ” “include, ” “includes, ” and/or “including, ” when used in this disclosure, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

These and other features, and characteristics of the present disclosure, as well as the methods of operations and functions of the related elements of structure and the combination of parts and economies of manufacture, may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

The flowcharts used in the present disclosure illustrate operations that systems implement according to some embodiments of the present disclosure. It is to be expressly understood, the operations of the flowcharts may be implemented not in order. Conversely, the operations may be implemented in inverted order, or simultaneously. Moreover, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.

Moreover, while the systems and methods disclosed in the present disclosure are described primarily regarding a transportation system in land, it should be understood that this is only one exemplary embodiment. The systems and methods of the present disclosure may be applied to any other kind of transportation system. For example, the systems and methods of the present disclosure may be applied to transportation systems of different environments including land, ocean, aerospace, or the like, or any combination thereof. The autonomous vehicle of the transportation systems may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a vessel, an aircraft, a spaceship, a hot-air balloon, or the like, or any combination thereof.

An aspect of the present disclosure relates to systems and methods for processing point cloud data associated with autonomous driving. The system may specify a plurality of grids on a two-dimensional plane (e.g., a two-dimensional plane corresponding to a predetermined range of a vehicle) . For each of the plurality of grids, the system may obtain first point cloud data corresponding to the grid at a first time point and determine a first hash value of the first point cloud data. The system may also obtain second point cloud data corresponding to the grid at a second time point and determine a second hash value of the second point cloud data. The first time point may be a previous time point for the second time point. Further, in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, the system may refrain from further processing the second point cloud data. That is, a part of point cloud data captured (or acquired) at the second time point may be the same as a part of point cloud data captured at the first time point, for the same part captured at the second time point, the system may refrain from further processing and retrieve a previous processing result (e.g., a processing result of first point cloud data with a first hash value matching the second hash value) , thereby saving the processing resources and improving the efficiency of the processing of the point cloud data.

FIG. 1 is a schematic diagram illustrating an exemplary autonomous driving system according to some embodiments of the present disclosure. In some embodiments, the autonomous driving system 100 may include a vehicle 110 (e.g. 110-1, 110-2, …, 110-n) , a server 120, a terminal device 130, a storage device 140, a network 150, and a positioning and navigation system 160.

The vehicle 110 may be any type of autonomous vehicle, unmanned aerial vehicle, etc. As used herein, an autonomous vehicle or an unmanned aerial vehicle may refer to a vehicle that is capable of achieving a certain level of driving automation. Exemplary levels of driving automation may include a first level at which the vehicle is mainly supervised by a human and has a specific autonomous function (e.g., autonomous steering or accelerating) , a second level at which the vehicle has one or more advanced driver assistance systems (ADAS) (e.g., an adaptive cruise control system, a lane-keep system) that can control a braking, a steering, and/or an acceleration of the vehicle, a third level at which the vehicle is able to drive autonomously when one or more certain conditions are met, a fourth level at which the vehicle can operate without a human input or oversight but still is subject to some constraints (e.g., be confined to a certain area) , a fifth level at which the vehicle can operate autonomously under all circumstances, or the like, or any combination thereof.

In some embodiments, the vehicle 110 may have equivalent structures that enable the vehicle 110 to move around or fly. For example, the vehicle 110 may include structures of a conventional vehicle, for example, a chassis, a suspension, a steering device (e.g., a steering wheel) , a brake device (e.g., a brake pedal) , an accelerator, etc. As another example, the vehicle 110 may have a body and at least one wheel. The body may be a body of any style, such as a sports vehicle, a coupe, a sedan, a pick-up truck, a station wagon, a sports utility vehicle (SUV) , a minivan, or a conversion van. The at least one wheel may be configured as all-wheel drive (AWD) , front-wheel drive (FWR) , rear-wheel drive (RWD) , etc. In some embodiments, it is contemplated that the vehicle 110 may be an electric vehicle, a fuel cell vehicle, a hybrid vehicle, a conventional internal combustion engine vehicle, etc.

In some embodiments, the vehicle 110 may be capable of sensing its environment and navigating with one or more detecting units 112. The plurality of detection units 112 may include a sensor device (e.g., a radar (e.g., a light detection and ranging (Lidar) device) ) , a global position system (GPS) module, an inertial measurement unit (IMU) , a camera, or the like, or any combination thereof. The radar (e.g., the Lidar device) may be configured to scan the surrounding of the vehicle 110 and generate point cloud data. The point cloud data may be used to generate digital three dimensional (3D) representations of one or more objects surrounding the vehicle 110. The GPS module may refer to a device that is capable of receiving geolocation and time information from GPS satellites and then determining the device’s geographical position. The IMU may refer to an electronic device that measures and provides a vehicle’s specific force, angular rate, and sometimes a magnetic field surrounding the vehicle, using various inertial sensors. In some embodiments, the various inertial sensors may include an acceleration sensor (e.g., a piezoelectric sensor) , a velocity sensor (e.g., a Hall sensor) , a distance sensor (e.g., a radar, an infrared sensor) , a steering angle sensor (e.g., a tilt sensor) , a traction-related sensor (e.g., a force sensor) , etc. The camera may be configured to obtain one or more images relating to objects (e.g., a person, an animal, a tree, a roadblock, a building, or a vehicle) that are within the scope of the camera.

In some embodiments, the server 120 may be a single server or a server group. The server group may be centralized or distributed (e.g., the server 120 may be a distributed system) . In some embodiments, the server 120 may be local or remote. For example, the server 120 may access information and/or data stored in the terminal device 130, the detecting units 112, the vehicle 110, the storage device 140, and/or the positioning and navigation system 160 via the network 150. As another example, the server 120 may be directly connected to the terminal device 130, the detecting units 112, the vehicle 110, and/or the storage device 140 to access stored information and/or data. In some embodiments, the server 120 may be implemented on a cloud platform or an onboard computer. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the server 120 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2 in the present disclosure.

In some embodiments, the server 120 may include a processing device 122. The processing device 122 may process information and/or data associated with driving information associated with the vehicle 110 to perform one or more functions described in the present disclosure. For example, the processing device 122 specifies a plurality of grids on a two-dimensional plane. For each of the plurality of grids, the processing device 122 may determine a first hash value of first point cloud data captured at a first time point (which may be a previous time point for the second time point) and determine a second hash value of second point cloud data captured at a second time point. Further, in response to determining one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, the processing device 122 may refrain from further processing the second point cloud data and retrieve a previous processing result associated with the second point cloud data; in response to determining that no first hash value matches the second hash value, the processing device 122 may process the second point cloud data. The processing device 122 may also determine a driving path for the vehicle 110 based on a processing result of second point cloud data corresponding to the plurality of grids. That is, the processing device 122 may be configured as a planning component of the vehicle 110.

In some embodiments, the processing device 122 may include one or more processing devices (e.g., single-core processing device (s) or multi-core processor (s) ) . Merely by way of example, the processing device 122 may include a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , an application-specific instruction-set processor (ASIP) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a digital signal processor (DSP) , a field-programmable gate array (FPGA) , a programmable logic device (PLD) , a controller, a microcontroller unit, a reduced instruction set computer (RISC) , a microprocessor, or the like, or any combination thereof. In some embodiments, the processing device 122 may be integrated into the vehicle 110 and/or the terminal device 130.

In some embodiments, the terminal device 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a built-in device in a vehicle 130-4, a wearable device130-5, or the like, or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. The smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. The smart mobile device may include a smartphone, a personal digital assistant (PDA) , a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination thereof. The virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google ^TM Glass, an Oculus Rift, a HoloLens, a Gear VR, etc. In some embodiments, the built-in device in the vehicle 130-4 may include an onboard computer, an onboard television, etc. In some embodiments, the wearable device 130-5 may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the terminal device 130 may be a device with positioning technology for locating the location of the terminal device 130. In some embodiments, the server 120 may be integrated into the vehicle 110 and/or the terminal device 130.

The storage device 140 may store data and/or instructions. In some embodiments, the storage device 140 may store data obtained from the vehicle 110, the detecting units 112, the processing device 122, the terminal device 130, the positioning and navigation system 160, and/or an external storage device. For example, the storage device 140 may store point cloud data captured by the detecting units 112. In some embodiments, the storage device 140 may store data and/or instructions that the server 120 may execute or use to perform exemplary methods described in the present disclosure. For example, the storage device 140 may store instructions that the processing device 122 may execute or use to obtain point cloud data captured at different time points and determine hash values of the point cloud data. In some embodiments, the storage device 140 may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drive, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM) . Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc. Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically-erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc. In some embodiments, the storage device 140 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 140 may be connected to the network 150 to communicate with one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, and/or the positioning and navigation system 160) of the autonomous driving system 100. One or more components of the autonomous driving system 100 may access the data or instructions stored in the storage device 140 via the network 150. In some embodiments, the storage device 140 may be directly connected to or communicate with one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, and/or the positioning and navigation system 160) of the autonomous driving system 100. In some embodiments, the storage device 140 may be part of the server 120. In some embodiments, the storage device 140 may be integrated into the vehicle 110.

The network 150 may facilitate exchange of information and/or data. In some embodiments, one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, the storage device 140, or the positioning and navigation system 160) of the autonomous driving system 100 may send information and/or data to other component (s) of the autonomous driving system 100 via the network 150. For example, the server 120 may obtain point cloud data from the storage device 140 via the network 150. In some embodiments, the network 150 may be any type of wired or wireless network, or combination thereof. Merely by way of example, the network 150 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an Internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a wide area network (WAN) , a public telephone switched network (PSTN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points (e.g., 150-1, 150-2) , through which one or more components of the autonomous driving system 100 may be connected to the network 150 to exchange data and/or information.

The positioning and navigation system 160 may determine information associated with an object, for example, the terminal device 130, the vehicle 110, etc. In some embodiments, the positioning and navigation system 160 may include a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a BeiDou navigation satellite system, a Galileo positioning system, a quasi-zenith satellite system (QZSS) , etc. The information may include a location, an elevation, a velocity, an acceleration of the object, a current time, etc. The positioning and navigation system 160 may include one or more satellites, for example, a satellite 160-1, a satellite 160-2, and a satellite 160-3. The satellites 160-1 through 160-3 may determine the information mentioned above independently or jointly. The positioning and navigation system 160 may send the information mentioned above to the server 120, the vehicle 110, and/or the terminal device 130 via wireless connections.

One of ordinary skill in the art would understand that when an element (or component) of the autonomous driving system 100 performs, the element may perform through electrical signals and/or electromagnetic signals. For example, when the terminal device 130 transmits out a request to the server 120, a processor of the terminal device 130 may generate an electrical signal encoding the request. The processor of the terminal device 130 may then transmit the electrical signal to an output port. If the terminal device 130 communicates with the server 120 via a wired network, the output port may be physically connected to a cable, which further may transmit the electrical signal to an input port of the server 120. If the terminal device 130 communicates with the server 120 via a wireless network, the output port of the terminal device 130 may be one or more antennas, which convert the electrical signal to an electromagnetic signal. Within an electronic device, such as the terminal device 130 and/or the server 120, when a processor thereof processes an instruction, transmits out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals. For example, when the processor retrieves or saves data from a storage medium (e.g., the storage device 140) , it may transmit out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Here, an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure. In some embodiments, the server 120 and/or the terminal device 130 may be implemented on the computing device 200. For example, the processing device 122 may be implemented on the computing device 200 and configured to perform functions of the processing device 122 disclosed in this disclosure.

The computing device 200 may be used to implement any component of the autonomous driving system 100 of the present disclosure. For example, the processing device 122 of the autonomous driving system 100 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown for convenience, the computer functions related to the autonomous driving system 100 as described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load.

The computing device 200 may include communication (COM) ports 250 connected to and from a network (e.g., the network 150) connected thereto to facilitate data communications. The computing device 200 may also include a processor (e.g., a processor 220) , in the form of one or more processors (e.g., logic circuits) , for executing program instructions. For example, the processor may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.

The computing device 200 may further include program storage and data storage of different forms, for example, a disk 270, a read-only memory (ROM) 230, or a random access memory (RAM) 240, for storing various data files to be processed and/or transmitted by the computing device 200. The computing device 200 may also include program instructions stored in the ROM 230, the RAM 240, and/or another type of non-transitory storage medium to be executed by the processor 220. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device 200 also includes an I/O component 260, supporting input/output between the computing device 200 and other components therein. The computing device 200 may also receive programming and data via network communications.

Merely for illustration, only one processor is described in the computing device 200. However, it should be noted that the computing device 200 in the present disclosure may also include multiple processors, and thus operations that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, the processor of the computing device 200 executes both operation A and operation B. As another example, operation A and operation B may also be performed by two different processors jointly or separately in the computing device 200 (e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B) .

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure. In some embodiments, the terminal device 130 may be implemented on the mobile device 300. As illustrated in FIG. 3, the mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, a mobile operating system (OS) 370, and storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300.

In some embodiments, the mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM) and one or more applications 380 may be loaded into the memory 360 from the storage 390 in order to be executed by the CPU 340. The applications 380 may include a browser or any other suitable mobile app for receiving and rendering information relating to positioning or other information from the processing device 122. User interactions with the information stream may be achieved via the I/O 350 and provided to the processing device 122 and/or other components of the autonomous driving system 100 via the network 150.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform (s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure. The processing device 122 may include a specification module 410, a first obtaining module 420, a first determination module 430, a second obtaining module 440, a second determination module 450, and a processing module 460.

The specification module 410 may be configured to specify a plurality of grids on a two-dimensional plane. In some embodiments, the plurality of grids on the two-dimensional plane can be deemed as a coordinate system or a reference system which moves with the subject (e.g., the vehicle 110) . In some embodiments, each of the plurality of grids may correspond to a serial number. For example, the plurality of grids may be 256 × 256 grids with a plurality of serial numbers 1, 2, 3, etc. In some embodiments, a shape of each of the plurality of grids may include a quadrangle, a hexagon, an irregular polygon, or the like, or any combination thereof. In some embodiments, a size of the grid may be a default setting (e.g., an empirical value (e.g., 20 cm × 20 cm) ) of the autonomous driving system 100 or may be adjustable under different situations. In some embodiments, the specification module 410 may specify the plurality of grids based on longitude and latitude information according to a geohash algorithm. According to the geohash algorithm, each of the plurality of grids corresponds to a character string (also referred to as a “geohash value” ) . More descriptions of the plurality of grids may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .

The first obtaining module 420 may be configured to obtain first point cloud data corresponding to each of the plurality of grids at a first time point. In some embodiments, the first point cloud data may include point cloud data associated with environmental information within the predetermined range of the subject (e.g., the vehicle 110) . The environmental information within the predetermined range of the subject may include road information (e.g., a road boundary, a lane line, a sidewalk) , height information (e.g., a road height) , static object information (e.g., information associated with a static building, information associated with a static obstacle) , or the like, or any combination thereof.

The first determination module 430 may be configured to determine a first hash value of the first point cloud data. The first determination module 430 may determine the first hash value of the first point cloud data based on a hash algorithm (also referred to as a “hash function” ) . In some embodiments, the first determination module 430 may determine the first hash value of the first point cloud data based on the at least one feature value of at least one feature of the physical points corresponding to first point cloud data according to the hash algorithm.

The second obtaining module 440 may be configured to obtain second point cloud data corresponding to each of the plurality of grids at a second time point. In some embodiments, the first time point may be a previous time point for the second time point. In some embodiments, similar to the first point cloud data, the second point cloud data may include point cloud data associated with the environmental information within the predetermined range of the subject (e.g., the vehicle 110) .

The second determination module 450 may be configured to determine a second hash value of the second point cloud data. As described above, the second determination module 450 may determine the second hash value of the second point cloud data based on the hash algorithm.

The processing module 460 may be configured to match the second hash value of the second point cloud data corresponding to each of the plurality of grids with a plurality of first hash values of first point cloud data corresponding to the plurality of grids at the first time point to determine whether there is first point cloud data captured at the first time point the same as or substantially the same as the second point cloud data corresponding to the grid. For example, the processing module 460 may determine a plurality of similarities between the second hash value and the plurality of first hash values and compare the plurality of similarities with a similarity threshold.

In response to determining that one of the plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, the processing module 460 may refrain from further processing the second point cloud data and retrieve a previous processing result associated with the second point cloud data (e.g., a processing result of first point cloud data with a first hash value matching the second hash value) from a cache, which can improve the efficiency of the processing of the point cloud data.

In response to determining that no first hash value matches with the second hash value, the processing module 460 may process the second point cloud data and store the processing result of the second point cloud data into the cache, which can be regarded as a “previous processing result” for a next time point.

The modules in the processing device 122 may be connected to or communicate with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or any combination thereof. Two or more of the modules may be combined as a single module, and any one of the modules may be divided into two or more units. For example, the first obtaining module 420 and the second obtaining module 440 may be combined as a single module which may both obtain the first point cloud data at a first time point and the second point cloud data at a second time point. As another example, the first determination module 430 and the second determination module 450 may be combined as a single module which may both determine the first hash value of the first point cloud data and the second hash value of the second point cloud data. As a further example, the processing device 122 may include a storage module (not shown) used to store information and/or data (e.g., the plurality of grids, the first point cloud data, the first hash values corresponding to the plurality of grids, the second point cloud data, the second hash values corresponding to the plurality of grids, the processing result of the second point cloud data) associated with the autonomous driving.

FIG. 5 is a flowchart illustrating an exemplary process for processing point cloud data according to some embodiments of the present disclosure. In some embodiments, the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing device 122 (e.g., the specification module 410) (e.g., the processing circuits of the processor 220) may specify a plurality of grids on a two-dimensional plane.

The two-dimensional plane may correspond to a predetermined range (also referred to as a predetermined geographic region) from a subject (e.g., the vehicle 110) within which point cloud data may be captured. As used herein, the predetermined range may be a default setting of the autonomous driving system 100 or may be adjustable under different situations. For example, the predetermined range may depend on (or partially depend on) a scanning range of the sensor device (e.g., the Lidar device) .

In some embodiments, the two-dimensional plane may correspond to a map associated with the predetermined range. In some embodiments, the map may be a map presenting driving assistance information associated with a geographic region, such as a representation of a road network, for example, roads, intersections, traffic signals, lane rules, etc. As described above, a range of the geographic region may be the same as or larger than the predetermined range where the point cloud data is captured. A shape of the geographic region may be a regular triangle, a rectangle, a square, a regular hexagon, a circle, etc. For example, the shape of the geographic region may be a rectangle with a size of M meters × N meters, wherein M and N may be positive numbers (e.g., 5, 10, 20, 50, 100, 500) . In some embodiments, the map may be a three-dimensional (3D) map, a two-dimensional (2D) map, a four-dimensional (4D) map, etc.

In some embodiments, the map may be a high-definition map containing information of an accuracy of a centimeter level or a millimeter level. In some embodiments, the high-definition map may be generated online or offline. For example, the high-definition map may be generated offline based on data (e.g., point cloud data) captured by a plurality of detection units (e.g., the detection units described in FIG. 1) installed on a test vehicle which is used to execute a measurement trip. As the test vehicle moves along a road, the plurality of detection units may generate point cloud data associated with a surrounding environment of the test vehicle. Further, a processing device (e.g., the processing device 122) may generate a plurality of high-definition maps corresponding to different geographic regions based on the point cloud data and store the plurality of high-definition maps in a storage device (e.g., the storage device 140) of the autonomous driving system 100. Accordingly, the processing device 122 may access the storage device and retrieve a corresponding high-definition map based on an estimated location of the subject.

In some embodiments, the map may be a three-dimensional map which can be represented in a three-dimensional rectangular coordinate system including an X-axis, a Y-axis, and a Z-axis. In this situation, the two-dimensional plane may be the X-Y plane corresponding to the geographic region of the three-dimensional map.

In some embodiments, the plurality of grids on the two-dimensional plane can be deemed as a coordinate system or a reference system which moves with the subject (e.g., the vehicle 110) . In some embodiments, each of the plurality of grids may correspond to a serial number. For example, the plurality of grids may be 256 × 256 grids with a plurality of serial numbers 1, 2, 3, etc. In some embodiments, a shape of each of the plurality of grids may include a quadrangle, a hexagon, an irregular polygon, or the like, or any combination thereof. In some embodiments, a size of the grid may be a default setting (e.g., an empirical value (e.g., 20 cm × 20 cm) ) of the autonomous driving system 100 or may be adjustable under different situations. In some embodiments, the processing device 122 may specify the plurality of grids based on longitude and latitude information according to a geohash algorithm. According to the geohash algorithm, each of the plurality of grids corresponds to a character string (also referred to as a “geohash value” ) . More descriptions of the plurality of grids may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .

In 520, for each of the plurality of grids, the processing device 122 (e.g., the first obtaining module 420) (e.g., the interface circuits of the processor 220) may obtain first point cloud data corresponding to the grid at a first time point.

As used herein, the point cloud data may include a set of data points associated with one or more objects within the predetermined range of the subject (e.g., the vehicle 110) . The one or more objects may include a vehicle, a pedestrian, a building, an obstacle, or the like, or any combination thereof. The data point of the point cloud data may correspond to a physical point or region of an object in a space around an estimated location of the subject.

In some embodiments, as described in connection with FIG. 1, the sensor device (e.g., the Lidar device) may emit laser pulses to scan a surrounding environment of the subject. The laser pulses may be reflected by physical points in the surrounding environment and return to the sensor device. The senor device may generate the point cloud data representative of the surrounding environment based on one or more characteristics of the return laser pulses. During the acquisition of the point cloud data, the sensor device may rotate in a scanning angle range (e.g., 360 degrees, 180 degrees, 120 degrees) and scan the surrounding environment in a certain scanning frequency (e.g., 10Hz, 15Hz, 20 Hz) .

In some embodiments, the point cloud data may include at least one feature value of at least one feature of the physical points. Exemplary features of a physical point may include a location (e.g., a geographic position, a relative position with respect to the sensor device) of the physical point, an intensity (e.g., a return strength of the laser pulses emitted from the sensor device and reflected by the physical point) of the physical point, a classification (e.g., a type) of the physical point, a scan direction (e.g., a direction in which a scanning mirror of the sensor device was directed to when a corresponding data point was detected) associated with the physical point, or the like, or any combination thereof.

In some embodiments, the point cloud data may be captured according to a time period (also referred to as a “capture time interval” ) (e.g., 10 milliseconds, 100 milliseconds, 1 second, 2 seconds) when the subject (e.g., the vehicle 110) stops or travels along a road. As described above, the plurality of grids move with the subject (e.g., the vehicle 110) , accordingly, take a specific grid as an example, the first point cloud data corresponding to the specific grid may be point cloud data captured at the first time point and projected in the specific grid.

In some embodiments, the first point cloud data may include point cloud data associated with environmental information within the predetermined range of the subject (e.g., the vehicle 110) . The environmental information within the predetermined range of the subject may include road information (e.g., a road boundary, a lane line, a sidewalk) , height information (e.g., a road height) , static object information (e.g., information associated with a static building, information associated with a static obstacle) , or the like, or any combination thereof. For example, the first point cloud data may include point cloud data associated with a road where the subject is moving along at the first time point. As another example, the first point cloud data may include point cloud data associated with a static building or a static obstacle within the predetermined range of the subject at the first time point.

In 530, the processing device 122 (e.g., the first determination module 430) (e.g., the processing circuits of the processor 220) may determine a first hash value of the first point cloud data. The processing device 122 may determine the first hash value of the first point cloud data based on a hash algorithm (also referred to as a “hash function” ) .

As used herein, the hash algorithm may refer to a mathematical algorithm that can be used to map data (e.g., the point cloud data) of arbitrary size onto data of a fixed size. A value returned by the hash algorithm is called a hash value (also referred to as “hash code, ” “digest, ” or “simply hash” ) , which may be a character string composed of numbers and letters. In some embodiments, the hash algorithm may include a message-digest (MD) algorithm (e.g., MD4, MD5) , a secure hash algorithm (SHA) (e.g., SHA-1, SHA-224, SHA-256, SHA-384, SHA-512) , etc.

In some embodiments, the processing device 122 may determine the first hash value of the first point cloud data based on the at least one feature value of at least one feature of the physical points corresponding to first point cloud data according to the hash algorithm.

In 540, for each of the plurality of grids, the processing device 122 (e.g., the second obtaining module 440) (e.g., the interface circuits of the processor 220) may obtain second point cloud data corresponding to the grid at a second time point.

As described in connection with operation 520, the point cloud data may be captured according to a time period when the subject (e.g., the vehicle 110) stops or travels along a road. Accordingly, the second point cloud data may be point cloud data captured at the second time point and projected in the grid.

In some embodiments, similar to the first point cloud data, the second point cloud data may include point cloud data associated with the environmental information within the predetermined range of the subject (e.g., the vehicle 110) . For example, the second point cloud data may include point cloud data associated with a road where the subject is moving along at the second time point. As another example, the second point cloud data may include point cloud data associated with a static building or a static obstacle within the predetermined range of the subject at the second time point.

In some embodiments, the first time point may be a previous time point for the second time point. For example, the first time point may be a previously adjacent time point for the second time point. As another example, the first time point may be a previous time point within a predetermined time range from the second time point.

In 550, the processing device 122 (e.g., the second determination module 450) (e.g., the processing circuits of the processor 220) may determine a second hash value of the second point cloud data. As described in connection with operation 530, the processing device 122 may determine the second hash value of the second point cloud data based on the hash algorithm.

In some situations, as described above, since the environmental information within the predetermined range of the subject is static and remains unchanged with time, point cloud data captured at the second time point may be partially the same as point cloud data captured at the first time point (which is a previous time point for the second time point) . Take a specific grid as an example, the second point cloud data captured at the second time point corresponding to the specific gird may be the same as or substantially the same as the first point cloud data captured at the first time point corresponding to the specific grid (if from the first time point to the second time point, the subject does not move) or the first point cloud data captured at the first time point corresponding to another grid (if from the first time point to the second time point, the subject moves forward) . Therefore, for a specific grid at the second time point, if there is first point cloud data captured at the first time point the same as or substantially the same as the second point cloud data captured at the second time point corresponding to the specific grid, it is unnecessary to process the second point cloud data and a previous processing result associated with the second point cloud data (e.g., a processing result of the first point cloud data) can be retrieved, thereby improving processing efficiency.

In some embodiments, in order to determine whether there is first point cloud data captured at the first time point the same as or substantially the same as the second point cloud data captured at the second time point corresponding to the specific grid, the processing device 122 may match the second hash value of the second point cloud data with a plurality of first hash values of first point cloud data corresponding to the plurality of grids at the first time point. For example, the processing device 122 may determine a plurality of similarities between the second hash value and the plurality of first hash values and compare the plurality of similarities with a similarity threshold. The similarity threshold may be a default setting of the autonomous driving system 100 or may be adjustable under different situations.

In 560, in response to determining that one of the plurality of first hash values of the first point cloud data corresponding to the plurality of grids matches the second hash value, the processing device 122 (e.g., the processing module 460) (e.g., the processing circuits of the processor 220) may refrain from further processing the second point cloud data.

For example, in response to determining that one of the plurality of similarities is higher than the similarity threshold, the processing device 122 may determine that a corresponding first hash value matches the second hash value. Further, the processing device 122 may refrain from further processing the second point cloud data and retrieve a previous processing result associated with the second point cloud data (e.g., a processing result of first point cloud data with a first hash value matching the second hash value) from a cache, which can improve the efficiency of the processing of the point cloud data. As used herein, the cache may be a component in the processing device 122 or the storage device 140. In some embodiments, the cache may include a hardware cache (e.g., a central processing unit (CPU) cache, a graphics processing unit (GPU) cache) , a software cache (e.g., a disk cache, a web cache) , a database cache, a distributed cache, etc.

In some embodiments, a plurality of previous processing results associated with a plurality of previous time points may be stored in the cache and each of the plurality of previous processing results corresponds to a specific grid at a specific time point. In some situations, over time, some of the processing results in the cache may be no longer needed. Therefore, the cache may be dynamically released according to a predetermined time interval (e.g., the capture time interval of point cloud data, a time interval defined by a user) . More descriptions of the dynamic release of the cache may be found elsewhere in the present disclosure (e.g., FIG. 8 and the descriptions thereof) . In some embodiments, the plurality of previous processing results may be stored in the storage device 140, accordingly, the processing device 122 may retrieve the previous processing results from the storage device 140.

In some embodiments, in response to determining that no first hash value matches with the second hash value (e.g., all the plurality of similarities are lower than or equal to the similarity threshold) , the processing device 122 (e.g., the processing module 460) (e.g., the processing circuits of the processor 220) may process the second point cloud data. For example, the processing device 122 may extract at least one feature (e.g., a density of data points) of the second point cloud data and determine an environmental parameter (e.g., a road height) based on the at least one feature. Further, the processing device 122 may store the processing result of the second point cloud data into the cache, which can be regarded as a “previous processing result” for a next time point.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure.

For example, one or more other optional operations (e.g., a storing operation) may be added elsewhere in the process 500. In the storing operation, the processing device 122 may store information and/or data (e.g., the plurality of grids, the first point cloud data, the first hash values corresponding to the plurality of grids, the second point cloud data, the second hash values corresponding to the plurality of grids, the processing result of the second point cloud data) associated with the autonomous driving in a storage device (e.g., the storage device 140) disclosed elsewhere in the present disclosure. As another example, operation 520 and operation 530 may be combined into a single operation in which the processing device 122 may both obtain the first point cloud data corresponding to the grid at the first time point and determine the first hash value of the first point cloud data. As a further example, operation 540 and operation 550 may be combined into a single operation in which the processing device 122 may both obtain the second point cloud data corresponding to the grid at the second time point and determine the second hash value of the second point cloud data. As a still further example, the processing device 122 may receive the first point cloud data and the second point cloud data from the storage device 140 or the terminal device 130 via the network 150. As a still further example, the processing device 122 may obtain the plurality of grids from the storage device 140 via the network 150.

FIG. 6-A and FIG. 6-B are schematic diagrams illustrating exemplary grids on a two-dimensional plane according to some embodiments of the present disclosure. As described in connection with operation 510, the processing device 122 may specify a plurality of grids on a two-dimensional plane (e.g., 600) corresponding to a predetermined range of a subject (e.g., the vehicle 110) .

As illustrated in FIG. 6-A, a shape of the plurality of grids is a square. As illustrated in FIG. 6-B, the shape of the plurality of grids is a hexagon. As described elsewhere in the present disclosure, the plurality of grids can be deemed as a coordinate system moving with the vehicle 110 with a central position of the vehicle 110 as an origin. It should be noted that the plurality of grids illustrated in FIG. 6-A or FIG. 6-B are provided for illustration purposes and not intended to be limiting. The shape of the plurality of grids can be any other shape, for example, a rectangle, a diamond, a star, a triangle, an irregular polygon, etc. The shapes of the plurality of grids may be the same as or different from each other. A size of the plurality of grids and/or a count of the plurality of grids may be default settings of the autonomous driving system 100 or may be adjustable under different situations.

FIG. 7 is a schematic diagram illustrating an exemplary process for matching point cloud data captured at different time points corresponding to grids according to some embodiments of the present disclosure. As described in connection with FIG. 5, the processing device 122 may specify a plurality of grids on a two-dimensional plane and the plurality of grids may move with the vehicle 110.

As illustrated in FIG. 7, the vehicle 110 travels to a position P ₀ at a time point T ₀, to a position P ₁ at a time point T ₁, and to a position P ₂ at a time point T ₂, wherein time point T ₀ is a previously adjacent time point for time point T ₁ and time point T ₁ is a previously adjacent time point for time point T ₂ (for brevity, the plurality of grids at the three time points are illustrated separately at separate page regions, in fact, the vehicle 110 travels along an almost straight line and the plurality of grids at the three time points partially overlap with each other) .

As described elsewhere in the present disclosure, when the vehicle 110 travels to a specific position (e.g., P ₂) at a specific time point (e.g., T ₂) , for each of the plurality of grids, the processing device 122 may obtain point cloud data (which can be called as “target point cloud data” (e.g., second point cloud data) ) corresponding to the grid and determine a hash value (which can be called as a “target hash value” (e.g., a second hash value) ) of the point cloud data. Further, the processing device 122 may match the target hash value with previous hash values (e.g., first hash values of first point cloud data captured at time point T ₁, hash values of point cloud data captured at time point T ₀) of previous point cloud data captured at previous time points. In response to determining that there is a previous hash value matching the target hash value, the processing device 122 may refrain from further processing the target point cloud data corresponding to the grid and retrieve a previous processing result from a cache. In response to determining that there is no previous hash value matching the target hash value, the processing device 122 may process the target point cloud data corresponding to the grid and store a corresponding processing result in the cache.

For example, for the grids in a box 710, the processing device 122 may determine that hash values of point cloud data captured at time point T ₁ corresponding to grids in a box 710’ and hash values of point cloud data captured at time point T ₀ corresponding to grids in a box 710” match target hash values of target point cloud data captured at time point T ₂, further, the processing device 122 may refrain from further processing the target point cloud data captured at time point T ₂ corresponding to the grids in the box 710 and retrieve a previous processing result (e.g., a processing result of point cloud data captured at time point T ₀ corresponding to the grids in the box 710” , that is, for a specific region within the predetermined range of the vehicle 110, corresponding point cloud data is processed only once and a processing result can be retrieved in subsequent process) from the cache. For the grids in a box 720, the processing device 122 may determine that hash values of point cloud data captured at time point T ₁ corresponding to grids in a box 720’ match target hash values of target point cloud data captured at time point T ₂, further, the processing device 122 may refrain from further processing the target point cloud data captured at time point T ₂ corresponding to the grids in the box 720 and retrieve a previous processing result (e.g., a processing result of point cloud data captured at time point T ₁ corresponding to the grids in the box 720’) from the cache. Further, for the grids in a box 730, the processing device 122 may determine that there is no previous hash value matching target hash values of target point cloud data captured at time point T ₂, therefore, the processing device 122 may process the target point cloud data corresponding to the grids in the box 730 and store corresponding processing results in the cache for further use.

As described in connection with FIG. 7, the vehicle 110 travels to a position P ₀ at a time point T ₀, to a position P ₁ at a time point T ₁, and to a position P ₂ at a time point T ₂, wherein time point T ₀ is a previously adjacent time point for time point T ₁ and time point T ₁ is a previously adjacent time point for time point T ₂. The processing results of the point cloud data captured at different time points may be stored in the cache. Over time, some of the processing results in the cache may be no longer needed in subsequent process. Therefore, in order to improve processing efficiency and save processing resources, the cache may be dynamically released according to a predetermined time interval.

As illustrated in FIG. 8, at time point T ₁, processing results of point cloud data captured at time point T ₀ corresponding to grids in a box 810 may be no longer needed in subsequent process and may be released. At time point T ₂, processing results of point cloud data captured at time point T ₀ corresponding to grids in a box 820, which correspond to point cloud data captured at time point T ₁ corresponding to grids in a box 820’, may be no longer needed in subsequent process and may be released. It can be seen that the cache may be dynamically released according to a capture time interval of the point cloud data. In some embodiments, the cache may be released according to a predetermined time interval (e.g., a time interval between a time point T _x and a time point T _y, wherein T _x is a previous time point for T _y) under which at a time point T _y, all processing results associated with T _x and processing results associated with time points before T _x are not needed.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure, and are within the spirit and scope of the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments of the present disclosure. For example, the terms “one embodiment, ” “an embodiment, ” and/or “some embodiments” mean that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, it is emphasized and should be appreciated that two or more references to “an embodiment” or “one embodiment” or “an alternative embodiment” in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined as suitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “unit, ” “module, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon.

A computer-readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. Although the above disclosure discusses through various examples what is currently considered to be a variety of useful embodiments of the disclosure, it is to be understood that such detail is solely for that purpose, and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the disclosed embodiments. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software-only solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the various embodiments. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, claimed subject matter may lie in less than all features of a single foregoing disclosed embodiment.

Claims

A system for autonomous driving, comprising:

a storage medium to store a set of instructions; and

a processor, communicatively coupled to the storage medium, to execute the set of instructions to:

specify a plurality of grids on a two-dimensional plane;

for each of the plurality of grids,

obtain first point cloud data corresponding to the grid at a first time point;

determine a first hash value of the first point cloud data;

obtain second point cloud data corresponding to the grid at a second time point;

determine a second hash value of the second point cloud data; and

in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refrain from further processing the second point cloud data.
The system of claim 1, wherein the first time point is a previous time point for the second time point.
The system of claim 1 or claim 2, wherein the processor is further to:

retrieve, from a cache, a previous processing result associated with the second point cloud data.
The system of claim 3, wherein the previous processing result associated with the second point cloud data comprises a processing result of first point cloud data with a first hash value matching the second hash value.
The system of claim 3 or claim 4, wherein the processor is further to:

in response to determining that no first hash value matches with the second hash value, process the second point cloud data.
The system of claim 5, wherein the processor is further to:

store a processing result of the second point cloud data into the cache.
The system of any of claims 3-6, wherein the cache is dynamically released according to a predetermined time interval.
The system of any of claims 1-7, wherein the first point cloud data or the second point cloud data comprises point cloud data associated with environmental information within a predetermined range of a vehicle.
The system of claim 8, wherein the environmental information within the predetermined range of the vehicle comprise road information, height information, or static object information.
The system of any of claims 1-9, wherein the first point cloud data or the second point cloud data is acquired by a sensor device, the sensor device comprising a light detection and ranging (Lidar) device.
The system of any of claims 1-10, wherein a shape of the plurality of grids comprises at least one of a quadrangle, a hexagon, or an irregular polygon.
A method implemented on a computing device including at least one processor, at least one storage medium, and a communication platform connected to a network, the method comprising:

specifying a plurality of grids on a two-dimensional plane;

for each of the plurality of grids,

obtaining first point cloud data corresponding to the grid at a first time point;

determining a first hash value of the first point cloud data;

obtaining second point cloud data corresponding to the grid at a second time point;

determining a second hash value of the second point cloud data; and

in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refraining from further processing the second point cloud data.
The method of claim 12, wherein the first time point is a previous time point for the second time point.
The method of claim 12 or claim 13, wherein the method further comprises:

retrieving, from a cache, a previous processing result associated with the second point cloud data.
The method of claim 14, wherein the previous processing result associated with the second point cloud data comprises a processing result of first point cloud data with a first hash value matching the second hash value.
The method of claim 14 or claim 15, wherein the method further comprises:

in response to determining that no first hash value matches with the second hash value, processing the second point cloud data.
The method of claim 16, wherein the method further comprises:

storing a processing result of the second point cloud data into the cache.
The method of any of claims 14-17, wherein the cache is dynamically released according to a predetermined time interval.
The method of any of claims 12-18, wherein the first point cloud data or the second point cloud data comprises point cloud data associated with environmental information within a predetermined range of a vehicle.
The method of claim 19, wherein the environmental information within the predetermined range of the vehicle comprise road information, height information, or static object information.
The method of any of claims 12-20, wherein the first point cloud data or the second point cloud data is acquired by a sensor device, the sensor device comprising a light detection and ranging (Lidar) device.
The method of any of claims 12-21, wherein a shape of the plurality of grids comprises at least one of a quadrangle, a hexagon, or an irregular polygon.
A vehicle configured for autonomous driving, comprising:

a detecting component, a planning component, and a control component, wherein the planning component is configured to:

specify a plurality of grids on a two-dimensional plane;

for each of the plurality of grids,

obtain first point cloud data corresponding to the grid at a first time point;

determine a first hash value of the first point cloud data;

obtain second point cloud data corresponding to the grid at a second time point;

determine a second hash value of the second point cloud data; and

in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refrain from further processing the second point cloud data.
The vehicle of claim 23, wherein the first time point is a previous time point for the second time point.
The vehicle of claim 23 or claim 24, wherein the planning component is further configured to:

retrieve, from a cache, a previous processing result associated with the second point cloud data.
The vehicle of claim 25, wherein the previous processing result associated with the second point cloud data comprises a processing result of first point cloud data with a first hash value matching the second hash value.
The vehicle of claim 25 or claim 26, wherein the planning component is further configured to:

in response to determining that no first hash value matches with the second hash value, process the second point cloud data.
The vehicle of claim 27, wherein the planning component is further configured to:

store a processing result of the second point cloud data into the cache.
The vehicle of any of claims 25-28, wherein the cache is dynamically released according to a predetermined time interval.
The vehicle of any of claims 23-29, wherein the first point cloud data or the second point cloud data comprises point cloud data associated with environmental information within a predetermined range of a vehicle.
The vehicle of claim 30, wherein the environmental information within the predetermined range of the vehicle comprise road information, height information, or static object information.
The vehicle of any of claims 23-31, wherein the first point cloud data or the second point cloud data is acquired by the detecting component, the detecting component comprising a light detection and ranging (Lidar) device.
The vehicle of any of claims 23-32, wherein a shape of the plurality of grids comprises at least one of a quadrangle, a hexagon, or an irregular polygon.
A system for autonomous driving, comprising:

a specification module configured to specify a plurality of grids on a two-dimensional ground plane;

a first obtaining module configured to obtain first point cloud data corresponding to each of the plurality of grids at a first time point;

a first determination module configured to determine a first hash value of the first point cloud data;

a second obtaining module configured to obtain second point cloud data corresponding to each of the plurality of grids at a second time point;

a second determination module configured to determine a second hash value of the second point cloud data; and

a processing module configured to refrain from further processing the second point cloud data in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value.
A non-transitory computer readable medium, comprising executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method, the method comprising:

specifying a plurality of grids on a two-dimensional ground plane;

for each of the plurality of grids,

obtaining first point cloud data corresponding to the grid at a first time point;

determining a first hash value of the first point cloud data;

obtaining second point cloud data corresponding to the grid at a second time point;

determining a second hash value of the second point cloud data; and

in response to determining that one of a plurality of first hash values of first point cloud data corresponding to the plurality of grids matches the second hash value, refraining from further processing the second point cloud data.