Determination of an optimal spatiotemporal sensor configuration for navigation of a vehicle using simulation of virtual sensors

Techniques for determination of an optimal spatiotemporal sensor configuration for navigation of a vehicle include generating a model of a virtual vehicle operating in an environment. The model of the virtual vehicle includes a virtual sensor having a virtual viewing range. The virtual viewing range of the virtual sensor is segregated into frustums. The virtual viewing range of the virtual sensor corresponds to a viewing range of a sensor of a vehicle operating in the environment. A geometric viewport is generated including pixels. The geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor. The geometric viewport is segregated into sections. Each section corresponds to a frustum. A virtual point cloud of the virtual sensor is rendered. The virtual point cloud includes coordinate positions representing a portion of the environment located within the virtual viewing range of the virtual sensor.

FIELD OF THE INVENTION

This description relates generally to sensor configuration design for vehicles and specifically to determination of an optimal spatiotemporal sensor configuration for navigation of a vehicle using simulation of virtual sensors.

BACKGROUND

Traditional methods for vehicular design often use remote sensing devices such as LIDARs to measure a distance from an object in an environment to the devices. However, conventional methods for vehicular design and verification using sensors may not be practical in complex and dense environments because LIDAR sensors are expensive to manufacture and test extensively. Moreover, conventional methods for sensor design and placement often rely on polygon intersection methods that may not be optimal in realistic environments because of the large number of polygonal geometries.

SUMMARY

Techniques are provided for determination of an optimal spatiotemporal sensor configuration for navigation of a vehicle using simulation of virtual sensors. The techniques include generating, using one or more processors, a model of a virtual vehicle operating in an environment. The model of the virtual vehicle includes a virtual sensor having a virtual viewing range. Using the one or more processors, the virtual viewing range of the virtual sensor is segregated into a plurality of frustums. The virtual viewing range of the virtual sensor corresponds to a viewing range of a sensor of a vehicle operating in the environment. Using the one or more processors, a geometric viewport is generated including a plurality of pixels. The geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor. Using the one or more processors, the geometric viewport is segregated into a plurality of sections. Each section of the plurality of sections corresponds to a frustum of the plurality of frustums. Using the one or more processors, a virtual point cloud of the virtual sensor is generated. The virtual point cloud includes a plurality of coordinate positions representing a portion of the environment located within the virtual viewing range of the virtual sensor.

In one embodiment, based on the virtual point cloud of the virtual sensor, a spatiotemporal configuration of the sensor of the vehicle is determined.

In one embodiment, using the one or more processors, virtual point clouds of virtual sensors of the virtual vehicle are rendered. Using the one or more processors, the virtual point clouds are aggregated into an aggregate virtual point cloud. The aggregate virtual point cloud represents a portion of the environment located within a virtual viewing range of the sensors. Based on the aggregate virtual point cloud, a spatiotemporal configuration of sensors of the vehicle is determined, wherein each sensor corresponds to a virtual sensor.

In one embodiment, based on the virtual point cloud of the virtual sensor, a blind spot of the sensor of the vehicle is determined, wherein the blind spot includes a spatiotemporal location of the environment.

In one embodiment, an object is located at the blind spot and the coordinate positions are free of the object.

In one embodiment, the viewing range of the sensor of the vehicle is extended based on analyzing the virtual point cloud data of the virtual sensor.

In one embodiment, the sensor includes at least one of a LIDAR, a monocular video camera, a stereo video camera, an infrared camera, a RADAR, an ultrasonic sensor, or a time-of-flight (TOF) depth sensor.

In one embodiment, the sensor is one of several sensors located on the vehicle and arranged in a spatiotemporal configuration.

In one embodiment, the spatiotemporal configuration is one of several spatiotemporal configurations. The method further includes for each spatiotemporal configuration of the several spatiotemporal configurations, rendering a raster image representing the coordinate positions. An optimal spatiotemporal configuration of the several spatiotemporal configurations is determined based on the plurality of raster images.

In one embodiment, each raster image includes the pixels of the geometric viewport and represents coordinate positions of an object located within the environment.

In one embodiment, a raster image representing the coordinate positions of the object located within the environment is transmitted to a display device of the vehicle.

In one embodiment, the rendering of the raster image includes receiving, using the sensor, sensor data representing coordinate positions of the object. Using the one or more processors, pixels representing the object are generated. The sensor data is combined with the pixels to generate the raster image.

In one embodiment, the sensor data includes LIDAR point cloud data.

In one embodiment, the geometric viewport has a width that increases as a number of the frustums increases.

In one embodiment, the rendering of the raster image is based on a geometric position and directional orientation of the sensor relative to the coordinate positions of the object.

In one embodiment, the raster image includes a two-dimensional cylindrical representation of a surface of the object.

In one embodiment, the segregating of the geometric viewport into the sections includes mapping a near plane of each frustum onto a corresponding section.

In one embodiment, using the coordinate positions of the object in the raster image, a distance from the vehicle to the object is determined. Using a control module of the vehicle, the vehicle is navigated to avoid collisions with the object based on the distance.

In one embodiment, using the raster image, a reflectance of a surface of the object is determined. Using a control module of the vehicle, the vehicle is navigated to avoid a collision of the vehicle with the object based on the reflectance.

In one embodiment, a distinct raster image representing the object is rendered onto the geometric viewport. A representational quality of the distinct raster image associated with the reflectance of a surface of the object is determined.

In one embodiment, a vehicle includes one or more computer processors and one or more non-transitory storage media storing instructions which, when executed by the one or more computer processors, cause performance of any one of the methods disclosed herein.

In one embodiment, one or more non-transitory storage media store instructions which, when executed by one or more computing devices, cause performance of any one of the methods disclosed herein.

In one embodiment, a method includes performing a machine-executed operation involving instructions which, when executed by one or more computing devices, cause performance of any one of the methods disclosed herein, wherein the machine-executed operation is at least one of sending said instructions, receiving said instructions, storing said instructions, or executing said instructions.

In one embodiment, a vehicle includes one or more processors configured to generate a model of a virtual vehicle operating in an environment. A virtual viewing range of a virtual sensor of the virtual vehicle is segregated into frustums. The virtual viewing range of the virtual sensor corresponds to a viewing range of a sensor of the vehicle operating in the environment. A geometric viewport is generated including pixels, wherein the geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor. The geometric viewport is segregated into sections, wherein each section corresponds to a frustum. A virtual point cloud of the virtual sensor is generated, wherein the virtual point cloud includes coordinate positions representing a portion of the environment located within the virtual viewing range of the virtual sensor.

In one embodiment, one or more processors receive data describing an environment in which a vehicle is operating. For each sensor of a plurality of sensors of the vehicle, the one or more processors generate a model of a virtual vehicle operating in the environment. The model of the virtual vehicle includes a virtual sensor corresponding to the sensor. Using the received data describing the environment, a virtual point cloud of the virtual sensor is rendered. Using the virtual point cloud, a quality metric of the virtual sensor is determined. The quality metric includes a range of the virtual sensor or a visibility of the virtual sensor. Using the plurality of quality metrics, an optimal sensor of the plurality of sensors for operating the vehicle within the environment is selected.

These and other aspects, features, and implementations can be expressed as methods, apparatus, systems, components, program products, means or steps for performing a function, and in other ways.

These and other aspects, features, and implementations will become apparent from the following descriptions, including the claims.

DETAILED DESCRIPTION

In the drawings, specific arrangements or orderings of schematic elements, such as those representing devices, modules, instruction blocks and data elements, are shown for ease of description. However, it should be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments.

Several features are described hereafter that can each be used independently of one another or with any combination of other features. However, any individual feature may not address any of the problems discussed above or might only address one of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Although headings are provided, information related to a particular heading, but not found in the section having that heading, may also be found elsewhere in this description. Embodiments are described herein according to the following outline:

1. General Overview

2. System Overview

3. Autonomous Vehicle Architecture

4. Autonomous Vehicle Inputs

5. Autonomous Vehicle Planning

6. Autonomous Vehicle Control

7. Environment for Determining Optimal Sensors

8. Architecture for Determining Optimal Sensors

9. Examples of Determining Optimal Sensors

10. Processes for Determining Optimal Sensors

General Overview

An autonomous vehicle (AV) uses sensors to detect objects and determine distances from objects during navigation within an environment. The sensors include visual sensors such as cameras and LIDAR. A LIDAR is a remote sensing device that uses a grid of pulsed laser beams to measure a distance from an object to the device, a direction in which the object lies, as well as a reflectance of a surface of the object. In embodiments herein, different types of sensors and LIDAR devices are simulated using a model of a virtual AV in a controlled environment to improve the accuracy of LIDAR operation, increase the fidelity of the simulation scenarios, and derive more meaningful results from simulation miles driven by the AV.

The method generates a simulated point cloud of a spinning LIDAR using image rasterization. One or more processors define a size of a rectangular output viewport in pixels. The viewport has a height corresponding to a number of rays emitted by the LIDAR. The viewport has a width corresponding to a density of the LIDAR simulation. A viewing range of the LIDAR is subdivided into frustums. A number of the frustums is defined corresponding to an accuracy of the simulation. The viewport is also subdivided by the number of frustums. The method maps a near plane of each frustum onto a corresponding section on the viewport. World coordinate positions of the environmental geometry are interpolated and rendered onto the viewport using rasterization. The resulting render is a 360° cylindrical view wrapped onto a 2D surface. The resulting render contains the simulated LIDAR point cloud values, which are used for determination of an optimal spatiotemporal sensor configuration for navigation of the AV.

System Overview

FIG.1illustrates an example of an autonomous vehicle100having autonomous capability.

As used herein, the term “autonomous capability” refers to a function, feature, or facility that enables a vehicle to be partially or fully operated without real-time human intervention, including without limitation fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possesses autonomous capability.

As used herein, “vehicle” includes means of transportation of goods or people. For example, cars, buses, trains, airplanes, drones, trucks, boats, ships, submersibles, dirigibles, etc. A driverless car is an example of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AV from a first spatiotemporal location to second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as the initial or starting location and the second spatiotemporal location is referred to as the destination, final location, goal, goal position, or goal location. In some examples, a trajectory is made up of one or more segments (e.g., sections of road) and each segment is made up of one or more blocks (e.g., portions of a lane or intersection). In an embodiment, the spatiotemporal locations correspond to real world locations. For example, the spatiotemporal locations are pick up or drop-off locations to pick up or drop-off persons or goods.

As used herein, “sensor(s)” includes one or more hardware components that detect information about the environment surrounding the sensor. Some of the hardware components can include sensing components (e.g., image sensors, biometric sensors), transmitting and/or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components such as analog-to-digital converters, a data storage device (such as a RAM and/or a nonvolatile storage), software or firmware components and data processing components such as an ASIC (application-specific integrated circuit), a microprocessor and/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on the AV vehicle or provided by a source external to the AV.

As used herein, a “road” is a physical area that can be traversed by a vehicle, and may correspond to a named thoroughfare (e.g., city street, interstate freeway, etc.) or may correspond to an unnamed thoroughfare (e.g., a driveway in a house or office building, a section of a parking lot, a section of a vacant lot, a dirt path in a rural area, etc.). Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utility vehicles, etc.) are capable of traversing a variety of physical areas not specifically adapted for vehicle travel, a “road” may be a physical area not formally defined as a thoroughfare by any municipality or other governmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed by a vehicle and may correspond to most or all of the space between lane markings, or may correspond to only some (e.g., less than 50%) of the space between lane markings. For example, a road having lane markings spaced far apart might accommodate two or more vehicles between the markings, such that one vehicle can pass the other without traversing the lane markings, and thus could be interpreted as having a lane narrower than the space between the lane markings or having two lanes between the lane markings. A lane could also be interpreted in the absence of lane markings. For example, a lane may be defined based on physical features of an environment, e.g., rocks and trees along a thoroughfare in a rural area.

As used herein, an AV system refers to the AV along with the array of hardware, software, stored data, and data generated in real-time that supports the operation of the AV. In an embodiment, the AV system is incorporated within the AV. In an embodiment, the AV system is spread across several locations. For example, some of the software of the AV system is implemented on a cloud computing environment similar to cloud computing environment300described below with respect toFIG.3.

In general, this document describes technologies applicable to any vehicles that have one or more autonomous capabilities including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, such as so-called Level 5, Level 4 and Level 3 vehicles, respectively (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety, for more details on the classification of levels of autonomy in vehicles). The technologies described in this document are also applicable to partially autonomous vehicles and driver assisted vehicles, such as so-called Level 2 and Level 1 vehicles (see SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems). In an embodiment, one or more of the Level 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicle operations (e.g., steering, braking, and using maps) under certain operating conditions based on processing of sensor inputs. The technologies described in this document can benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.

Referring toFIG.1, an AV system120operates the AV100along a trajectory198through an environment190to a destination199(sometimes referred to as a final location) while avoiding objects (e.g., natural obstructions191, vehicles193, pedestrians192, cyclists, and other obstacles) and obeying rules of the road (e.g., rules of operation or driving preferences).

In an embodiment, the AV system120includes devices101that are instrumented to receive and act on operational commands from the computer processors146. In an embodiment, computing processors146are similar to the processor304described below in reference toFIG.3. Examples of devices101include a steering control102, brakes103, gears, accelerator pedal or other acceleration control mechanisms, windshield wipers, side-door locks, window controls, and turn-indicators.

In an embodiment, the AV system120includes sensors121for measuring or inferring properties of state or condition of the AV100, such as the AV's position, linear velocity and acceleration, angular velocity and acceleration, and heading (e.g., an orientation of the leading end of AV100). Example of sensors121are GNSS, inertial measurement units (IMU) that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

In an embodiment, the sensors121also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras122in the visible light, infrared or thermal (or both) spectra, LIDAR123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.

In an embodiment, the AV system120includes a data storage unit142and memory144for storing machine instructions associated with computer processors146or data collected by sensors121. In an embodiment, the data storage unit142is similar to the ROM308or storage device310described below in relation toFIG.3. In an embodiment, memory144is similar to the main memory306described below. In an embodiment, the data storage unit142and memory144store historical, real-time, and/or predictive information about the environment190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment190is transmitted to the AV100via a communications channel from a remotely located database134.

In an embodiment, the AV system120includes communications devices140for communicating measured or inferred properties of other vehicles' states and conditions, such as positions, linear and angular velocities, linear and angular accelerations, and linear and angular headings to the AV100. These devices include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication devices and devices for wireless communications over point-to-point or ad hoc networks or both. In an embodiment, the communications devices140communicate across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). A combination of Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication (and, in some embodiments, one or more other types of communication) is sometimes referred to as Vehicle-to-Everything (V2X) communication. V2X communication typically conforms to one or more communications standards for communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices140include communication interfaces. For example, wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interfaces. The communication interfaces transmit data from a remotely located database134to AV system120. In an embodiment, the remotely located database134is embedded in a cloud computing environment200as described inFIG.2. The communication interfaces140transmit data collected from sensors121or other data related to the operation of AV100to the remotely located database134. In an embodiment, communication interfaces140transmit information that relates to teleoperations to the AV100. In some embodiments, the AV100communicates with other remote (e.g., “cloud”) servers136.

In an embodiment, the remotely located database134also stores and transmits digital data (e.g., storing data such as road and street locations). Such data is stored on the memory144on the AV100, or transmitted to the AV100via a communications channel from the remotely located database134.

In an embodiment, the remotely located database134stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory198at similar times of day. In one implementation, such data may be stored on the memory144on the AV100, or transmitted to the AV100via a communications channel from the remotely located database134.

Computing devices146located on the AV100algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system120to execute its autonomous driving capabilities.

In an embodiment, the AV system120includes computer peripherals132coupled to computing devices146for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the AV100. In an embodiment, peripherals132are similar to the display312, input device314, and cursor controller316discussed below in reference toFIG.3. The coupling is wireless or wired. Any two or more of the interface devices may be integrated into a single device.

Example Cloud Computing Environment

FIG.2illustrates an example “cloud” computing environment. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services). In typical cloud computing systems, one or more large cloud data centers house the machines used to deliver the services provided by the cloud. Referring now toFIG.2, the cloud computing environment200includes cloud data centers204a,204b, and204cthat are interconnected through the cloud202. Data centers204a,204b, and204cprovide cloud computing services to computer systems206a,206b,206c,206d,206e, and206fconnected to cloud202.

The cloud computing environment200includes one or more cloud data centers. In general, a cloud data center, for example the cloud data center204ashown inFIG.2, refers to the physical arrangement of servers that make up a cloud, for example the cloud202shown inFIG.2, or a particular portion of a cloud. For example, servers are physically arranged in the cloud datacenter into rooms, groups, rows, and racks. A cloud datacenter has one or more zones, which include one or more rooms of servers. Each room has one or more rows of servers, and each row includes one or more racks. Each rack includes one or more individual server nodes. In some implementation, servers in zones, rooms, racks, and/or rows are arranged into groups based on physical infrastructure requirements of the datacenter facility, which include power, energy, thermal, heat, and/or other requirements. In an embodiment, the server nodes are similar to the computer system described inFIG.3. The data center204ahas many computing systems distributed through many racks.

The cloud202includes cloud data centers204a,204b, and204calong with the network and networking resources (for example, networking equipment, nodes, routers, switches, and networking cables) that interconnect the cloud data centers204a,204b, and204cand help facilitate the computing systems'206a-faccess to cloud computing services. In an embodiment, the network represents any combination of one or more local networks, wide area networks, or internetworks coupled using wired or wireless links deployed using terrestrial or satellite connections. Data exchanged over the network, is transferred using any number of network layer protocols, such as Internet Protocol (IP), Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, etc. Furthermore, in embodiments where the network represents a combination of multiple sub-networks, different network layer protocols are used at each of the underlying sub-networks. In some embodiments, the network represents one or more interconnected internetworks, such as the public Internet.

The computing systems206a-for cloud computing services consumers are connected to the cloud202through network links and network adapters. In an embodiment, the computing systems206a-fare implemented as various computing devices, for example servers, desktops, laptops, tablet, smartphones, Internet of Things (IoT) devices, autonomous vehicles (including, cars, drones, shuttles, trains, buses, etc.) and consumer electronics. In an embodiment, the computing systems206a-fare implemented in or as a part of other systems.

Computer System

FIG.3illustrates a computer system300. In an implementation, the computer system300is a special purpose computing device. The special-purpose computing device is hard-wired to perform the techniques or includes digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. In various embodiments, the special-purpose computing devices are desktop computer systems, portable computer systems, handheld devices, network devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system300includes a bus302or other communication mechanism for communicating information, and a hardware processor304coupled with a bus302for processing information. The hardware processor304is, for example, a general-purpose microprocessor. The computer system300also includes a main memory306, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus302for storing information and instructions to be executed by processor304. In one implementation, the main memory306is used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor304. Such instructions, when stored in non-transitory storage media accessible to the processor304, render the computer system300into a special-purpose machine that is customized to perform the operations specified in the instructions.

In an embodiment, the computer system300further includes a read only memory (ROM)308or other static storage device coupled to the bus302for storing static information and instructions for the processor304. A storage device310, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross point memory is provided and coupled to the bus302for storing information and instructions.

In an embodiment, the computer system300is coupled via the bus302to a display312, such as a cathode ray tube (CRT), a liquid crystal display (LCD), plasma display, light emitting diode (LED) display, or an organic light emitting diode (OLED) display for displaying information to a computer user. An input device314, including alphanumeric and other keys, is coupled to bus302for communicating information and command selections to the processor304. Another type of user input device is a cursor controller316, such as a mouse, a trackball, a touch-enabled display, or cursor direction keys for communicating direction information and command selections to the processor304and for controlling cursor movement on the display312. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x-axis) and a second axis (e.g., y-axis), that allows the device to specify positions in a plane.

According to one embodiment, the techniques herein are performed by the computer system300in response to the processor304executing one or more sequences of one or more instructions contained in the main memory306. Such instructions are read into the main memory306from another storage medium, such as the storage device310. Execution of the sequences of instructions contained in the main memory306causes the processor304to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions.

In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to the processor304for execution. For example, the instructions are initially carried on a magnetic disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system300receives the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal and appropriate circuitry places the data on the bus302. The bus302carries the data to the main memory306, from which processor304retrieves and executes the instructions. The instructions received by the main memory306may optionally be stored on the storage device310either before or after execution by processor304.

The computer system300also includes a communication interface318coupled to the bus302. The communication interface318provides a two-way data communication coupling to a network link320that is connected to a local network322. For example, the communication interface318is an integrated service digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface318is a local area network (LAN) card to provide a data communication connection to a compatible LAN. In some implementations, wireless links are also implemented. In any such implementation, the communication interface318sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

The network link320typically provides data communication through one or more networks to other data devices. For example, the network link320provides a connection through the local network322to a host computer324or to a cloud data center or equipment operated by an Internet Service Provider (ISP)326. The ISP326in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet”328. The local network322and Internet328both use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link320and through the communication interface318, which carry the digital data to and from the computer system300, are example forms of transmission media. In an embodiment, the network320contains the cloud202or a part of the cloud202described above.

The computer system300sends messages and receives data, including program code, through the network(s), the network link320, and the communication interface318. In an embodiment, the computer system300receives code for processing. The received code is executed by the processor304as it is received, and/or stored in storage device310, or other non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG.4illustrates an example architecture400for an autonomous vehicle (e.g., the AV100shown inFIG.1). The architecture400includes a perception module402(sometimes referred to as a perception circuit), a planning module404(sometimes referred to as a planning circuit), a control module406(sometimes referred to as a control circuit), a localization module408(sometimes referred to as a localization circuit), and a database module410(sometimes referred to as a database circuit). Each module plays a role in the operation of the AV100. Together, the modules402,404,406,408, and410may be part of the AV system120shown inFIG.1. In some embodiments, any of the modules402,404,406,408, and410is a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs]), hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these things).

In use, the planning module404receives data representing a destination412and determines data representing a trajectory414(sometimes referred to as a route) that can be traveled by the AV100to reach (e.g., arrive at) the destination412. In order for the planning module404to determine the data representing the trajectory414, the planning module404receives data from the perception module402, the localization module408, and the database module410.

The perception module402identifies nearby physical objects using one or more sensors121, e.g., as also shown inFIG.1. The objects are classified (e.g., grouped into types such as pedestrian, bicycle, automobile, traffic sign, etc.) and a scene description including the classified objects416is provided to the planning module404.

The planning module404also receives data representing the AV position418from the localization module408. The localization module408determines the AV position by using data from the sensors121and data from the database module410(e.g., a geographic data) to calculate a position. For example, the localization module408uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate a longitude and latitude of the AV. In an embodiment, data used by the localization module408includes high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations of them), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types.

The control module406receives the data representing the trajectory414and the data representing the AV position418and operates the control functions420a-c(e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the AV100to travel the trajectory414to the destination412. For example, if the trajectory414includes a left turn, the control module406will operate the control functions420a-cin a manner such that the steering angle of the steering function will cause the AV100to turn left and the throttling and braking will cause the AV100to pause and wait for passing pedestrians or vehicles before the turn is made.

Autonomous Vehicle Inputs

FIG.5illustrates an example of inputs502a-d(e.g., sensors121shown inFIG.1) and outputs504a-d(e.g., sensor data) that is used by the perception module402(FIG.4). One input502ais a LIDAR (Light Detection and Ranging) system (e.g., LIDAR123shown inFIG.1). LIDAR is a technology that uses light (e.g., bursts of light such as infrared light) to obtain data about physical objects in its line of sight. A LIDAR system produces LIDAR data as output504a. For example, LIDAR data is collections of 3D or 2D points (also known as a point clouds) that are used to construct a representation of the environment190.

Another input502bis a RADAR system. RADAR is a technology that uses radio waves to obtain data about nearby physical objects. RADARs can obtain data about objects not within the line of sight of a LIDAR system. A RADAR system502bproduces RADAR data as output504b. For example, RADAR data are one or more radio frequency electromagnetic signals that are used to construct a representation of the environment190.

Another input502cis a camera system. A camera system uses one or more cameras (e.g., digital cameras using a light sensor such as a charge-coupled device [CCD]) to obtain information about nearby physical objects. A camera system produces camera data as output504c. Camera data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has multiple independent cameras, e.g., for the purpose of stereopsis (stereo vision), which enables the camera system to perceive depth. Although the objects perceived by the camera system are described here as “nearby,” this is relative to the AV. In use, the camera system may be configured to “see” objects far, e.g., up to a kilometer or more ahead of the AV. Accordingly, the camera system may have features such as sensors and lenses that are optimized for perceiving objects that are far away.

Another input502dis a traffic light detection (TLD) system. A TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. A TLD system produces TLD data as output504d. TLD data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). A TLD system differs from a system incorporating a camera in that a TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fish-eye lens) in order to obtain information about as many physical objects providing visual navigation information as possible, so that the AV100has access to all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system may be about 120 degrees or more.

In some embodiments, outputs504a-dare combined using a sensor fusion technique. Thus, either the individual outputs504a-dare provided to other systems of the AV100(e.g., provided to a planning module404as shown inFIG.4), or the combined output can be provided to the other systems, either in the form of a single combined output or multiple combined outputs of the same type (e.g., using the same combination technique or combining the same outputs or both) or different types type (e.g., using different respective combination techniques or combining different respective outputs or both). In some embodiments, an early fusion technique is used. An early fusion technique is characterized by combining outputs before one or more data processing steps are applied to the combined output. In some embodiments, a late fusion technique is used. A late fusion technique is characterized by combining outputs after one or more data processing steps are applied to the individual outputs.

FIG.6illustrates an example of a LIDAR system602(e.g., the input502ashown inFIG.5). The LIDAR system602emits light604a-cfrom a light emitter606(e.g., a laser transmitter). Light emitted by a LIDAR system is typically not in the visible spectrum; for example, infrared light is often used. Some of the light604bemitted encounters a physical object608(e.g., a vehicle) and reflects back to the LIDAR system602. (Light emitted from a LIDAR system typically does not penetrate physical objects, e.g., physical objects in solid form.) The LIDAR system602also has one or more light detectors610, which detect the reflected light. In an embodiment, one or more data processing systems associated with the LIDAR system generates an image612representing the field of view614of the LIDAR system. The image612includes information that represents the boundaries616of a physical object608. In this way, the image612is used to determine the boundaries616of one or more physical objects near an AV.

FIG.7illustrates the LIDAR system602in operation. In the scenario shown in this figure, the AV100receives both camera system output504cin the form of an image702and LIDAR system output504ain the form of LIDAR data points704. In use, the data processing systems of the AV100compares the image702to the data points704. In particular, a physical object706identified in the image702is also identified among the data points704. In this way, the AV100perceives the boundaries of the physical object based on the contour and density of the data points704.

FIG.8illustrates the operation of the LIDAR system602in additional detail. As described above, the AV100detects the boundary of a physical object based on characteristics of the data points detected by the LIDAR system602. As shown inFIG.8, a flat object, such as the ground802, will reflect light804a-demitted from a LIDAR system602in a consistent manner. Put another way, because the LIDAR system602emits light using consistent spacing, the ground802will reflect light back to the LIDAR system602with the same consistent spacing. As the AV100travels over the ground802, the LIDAR system602will continue to detect light reflected by the next valid ground point806if nothing is obstructing the road. However, if an object808obstructs the road, light804e-femitted by the LIDAR system602will be reflected from points810a-bin a manner inconsistent with the expected consistent manner. From this information, the AV100can determine that the object808is present.

Path Planning

FIG.9illustrates a block diagram900of the relationships between inputs and outputs of a planning module404(e.g., as shown inFIG.4). In general, the output of a planning module404is a route902from a start point904(e.g., source location or initial location), and an end point906(e.g., destination or final location). The route902is typically defined by one or more segments. For example, a segment is a distance to be traveled over at least a portion of a street, road, highway, driveway, or other physical area appropriate for automobile travel. In some examples, e.g., if the AV100is an off-road capable vehicle such as a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV, pick-up truck, or the like, the route902includes “off-road” segments such as unpaved paths or open fields.

In addition to the route902, a planning module also outputs lane-level route planning data908. The lane-level route planning data908is used to traverse segments of the route902based on conditions of the segment at a particular time. For example, if the route902includes a multi-lane highway, the lane-level route planning data908includes trajectory planning data910that the AV100can use to choose a lane among the multiple lanes, e.g., based on whether an exit is approaching, whether one or more of the lanes have other vehicles, or other factors that vary over the course of a few minutes or less. Similarly, in some implementations, the lane-level route planning data908includes speed constraints912specific to a segment of the route902. For example, if the segment includes pedestrians or un-expected traffic, the speed constraints912may limit the AV100to a travel speed slower than an expected speed, e.g., a speed based on speed limit data for the segment.

In an embodiment, the inputs to the planning module404includes database data914(e.g., from the database module410shown inFIG.4), current location data916(e.g., the AV position418shown inFIG.4), destination data918(e.g., for the destination412shown inFIG.4), and object data920(e.g., the classified objects416as perceived by the perception module402as shown inFIG.4). In some embodiments, the database data914includes rules used in planning. Rules are specified using a formal language, e.g., using Boolean logic. In any given situation encountered by the AV100, at least some of the rules will apply to the situation. A rule applies to a given situation if the rule has conditions that are met based on information available to the AV100, e.g., information about the surrounding environment. Rules can have priority. For example, a rule that says, “if the road is a freeway, move to the leftmost lane” can have a lower priority than “if the exit is approaching within a mile, move to the rightmost lane.”

FIG.10illustrates a directed graph1000used in path planning, e.g., by the planning module404(FIG.4). In general, a directed graph1000like the one shown inFIG.10is used to determine a path between any start point1002and end point1004. In real-world terms, the distance separating the start point1002and end point1004may be relatively large (e.g., in two different metropolitan areas) or may be relatively small (e.g., two intersections abutting a city block or two lanes of a multi-lane road).

In an embodiment, the directed graph1000has nodes1006a-drepresenting different locations between the start point1002and the end point1004that could be occupied by an AV100. In some examples, e.g., when the start point1002and end point1004represent different metropolitan areas, the nodes1006a-drepresent segments of roads. In some examples, e.g., when the start point1002and the end point1004represent different locations on the same road, the nodes1006a-drepresent different positions on that road. In this way, the directed graph1000includes information at varying levels of granularity. In an embodiment, a directed graph having high granularity is also a subgraph of another directed graph having a larger scale. For example, a directed graph in which the start point1002and the end point1004are far away (e.g., many miles apart) has most of its information at a low granularity and is based on stored data, but also includes some high granularity information for the portion of the graph that represents physical locations in the field of view of the AV100.

The nodes1006a-dare distinct from objects1008a-bwhich cannot overlap with a node. In an embodiment, when granularity is low, the objects1008a-brepresent regions that cannot be traversed by automobile, e.g., areas that have no streets or roads. When granularity is high, the objects1008a-brepresent physical objects in the field of view of the AV100, e.g., other automobiles, pedestrians, or other entities with which the AV100cannot share physical space. In an embodiment, some or all of the objects1008a-bare static objects (e.g., an object that does not change position such as a street lamp or utility pole) or dynamic objects (e.g., an object that is capable of changing position such as a pedestrian or other car).

The nodes1006a-dare connected by edges1010a-c. If two nodes1006a-bare connected by an edge1010a, it is possible for an AV100to travel between one node1006aand the other node1006b, e.g., without having to travel to an intermediate node before arriving at the other node1006b. (When we refer to an AV100traveling between nodes, we mean that the AV100travels between the two physical positions represented by the respective nodes.) The edges1010a-care often bidirectional, in the sense that an AV100travels from a first node to a second node, or from the second node to the first node. In an embodiment, edges1010a-care unidirectional, in the sense that an AV100can travel from a first node to a second node, however the AV100cannot travel from the second node to the first node. Edges1010a-care unidirectional when they represent, for example, one-way streets, individual lanes of a street, road, or highway, or other features that can only be traversed in one direction due to legal or physical constraints.

In an embodiment, the planning module404uses the directed graph1000to identify a path1012made up of nodes and edges between the start point1002and end point1004.

An edge1010a-chas an associated cost1014a-b. The cost1014a-bis a value that represents the resources that will be expended if the AV100chooses that edge. A typical resource is time. For example, if one edge1010arepresents a physical distance that is twice that as another edge1010b, then the associated cost1014aof the first edge1010amay be twice the associated cost1014bof the second edge1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges1010a-bmay represent the same physical distance, but one edge1010amay require more fuel than another edge1010b, e.g., because of road conditions, expected weather, etc.

When the planning module404identifies a path1012between the start point1002and end point1004, the planning module404typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG.11illustrates a block diagram1100of the inputs and outputs of a control module406(e.g., as shown inFIG.4). A control module operates in accordance with a controller1102which includes, for example, one or more processors (e.g., one or more computer processors such as microprocessors or microcontrollers or both) similar to processor304, short-term and/or long-term data storage (e.g., memory random-access memory or flash memory or both) similar to main memory306, ROM1308, and storage device210, and instructions stored in memory that carry out operations of the controller1102when the instructions are executed (e.g., by the one or more processors).

In an embodiment, the controller1102receives data representing a desired output1104. The desired output1104typically includes a velocity, e.g., a speed and a heading. The desired output1104can be based on, for example, data received from a planning module404(e.g., as shown inFIG.4). In accordance with the desired output1104, the controller1102produces data usable as a throttle input1106and a steering input1108. The throttle input1106represents the magnitude in which to engage the throttle (e.g., acceleration control) of an AV100, e.g., by engaging the steering pedal, or engaging another throttle control, to achieve the desired output1104. In some examples, the throttle input1106also includes data usable to engage the brake (e.g., deceleration control) of the AV100. The steering input1108represents a steering angle, e.g., the angle at which the steering control (e.g., steering wheel, steering angle actuator, or other functionality for controlling steering angle) of the AV should be positioned to achieve the desired output1104.

In an embodiment, the controller1102receives feedback that is used in adjusting the inputs provided to the throttle and steering. For example, if the AV100encounters a disturbance1110, such as a hill, the measured speed1112of the AV100is lowered below the desired output speed. In an embodiment, any measured output1114is provided to the controller1102so that the necessary adjustments are performed, e.g., based on the differential1113between the measured speed and desired output. The measured output1114includes measured position1116, measured velocity1118, (including speed and heading), measured acceleration1120, and other outputs measurable by sensors of the AV100.

In an embodiment, information about the disturbance1110is detected in advance, e.g., by a sensor such as a camera or LIDAR sensor, and provided to a predictive feedback module1122. The predictive feedback module1122then provides information to the controller1102that the controller1102can use to adjust accordingly. For example, if the sensors of the AV100detect (“see”) a hill, this information can be used by the controller1102to prepare to engage the throttle at the appropriate time to avoid significant deceleration.

FIG.12illustrates a block diagram1200of the inputs, outputs, and components of the controller1102. The controller1102has a speed profiler1202which affects the operation of a throttle/brake controller1204. For example, the speed profiler1202instructs the throttle/brake controller1204to engage acceleration or engage deceleration using the throttle/brake1206depending on, e.g., feedback received by the controller1102and processed by the speed profiler1202.

The controller1102also has a lateral tracking controller1208which affects the operation of a steering controller1210. For example, the lateral tracking controller1208instructs the steering controller1204to adjust the position of the steering angle actuator1212depending on, e.g., feedback received by the controller1102and processed by the lateral tracking controller1208.

The controller1102receives several inputs used to determine how to control the throttle/brake1206and steering angle actuator1212. A planning module404provides information used by the controller1102, for example, to choose a heading when the AV100begins operation and to determine which road segment to traverse when the AV100reaches an intersection. A localization module408provides information to the controller1102describing the current location of the AV100, for example, so that the controller1102can determine if the AV100is at a location expected based on the manner in which the throttle/brake1206and steering angle actuator1212are being controlled. In an embodiment, the controller1102receives information from other inputs1214, e.g., information received from databases, computer networks, etc.

Architecture for Determination of an Optimal Spatiotemporal Sensor Configuration

FIG.13illustrates a block diagram of an architecture1300for determination of an optimal spatiotemporal sensor configuration for navigation of an AV1308using simulation of virtual sensors, in accordance with one or more embodiments. The architecture1300includes an environment1304within which the AV1308and objects1316,1320are located. The architecture1300also includes a remote server1324communicably coupled to the AV1308. In other embodiments, the architecture1300includes additional or fewer components than those described herein. Similarly, the functions can be distributed among the components and/or different entities in a different manner than is described here.

The server1324performs computations used by the AV1308and other vehicles located within the environment1304and also stores data accessed by the AV1308and the other vehicles. The server1324may be an example of the server136shown inFIG.1. In one embodiment, the server1308may be a “cloud” server as described in more detail above with respect to server136inFIGS.1and2. Portions of the server1308may be implemented in software or hardware. For example, the server1308or a portion of the server1308may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

In one embodiment, illustrated inFIG.13, the server1324contains an AV sensor configurator1328. In other embodiments, the AV sensor configurator1328is located within the AV1308or within a component of the AV1308, such as the perception module1336, the control module1340, or the planning module404illustrated and described above with reference toFIG.4. Portions of the AV sensor configurator1328may be implemented in software or hardware. For example, the AV sensor configurator1328or a portion of the AV sensor configurator1328may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The AV sensor configurator1328builds a model of a virtual AV for simulation in a controlled environment to determine an optimal spatiotemporal sensor configuration for navigation of the AV1308. The AV sensor configurator1328determines the optimal spatiotemporal sensor configuration by simulating virtual models of the visual sensors1344and odometry sensors1348of the AV1308. In one embodiment, the AV sensor configurator1328generates and segregates a virtual viewing range of a virtual sensor of the virtual AV into several frustums. An example virtual viewing range1530of a virtual sensor is illustrated and described below with reference toFIG.15B. The virtual viewing range of the virtual sensor corresponds to a viewing range of a real sensor, such as a visual sensor1344or an odometry sensor1348, of the AV1308.

The AV sensor configurator1328generates a geometric viewport to simulate the virtual sensor. An example geometric viewport1500of a virtual sensor is illustrated and described below with reference toFIG.15A. A height of the geometric viewport, expressed in pixels, corresponds to a number of rays emitted from the virtual sensor. The AV sensor configurator1328segregates the geometric viewport into a number of sections, wherein each section corresponds to one of the frustums. The example geometric viewport1500is illustrated segregated into sections below with reference toFIG.15C. The AV sensor configurator1328renders a virtual point cloud of the virtual sensor. The rendering of the virtual point cloud is described in detail below with reference toFIG.14. The virtual point cloud includes coordinate positions representing a portion of the environment1304that is located within the virtual viewing range of the virtual sensor. The AV sensor configurator1328determines, based on the virtual point cloud of the virtual sensor, an optimal spatiotemporal configuration of the visual sensors1344and odometry sensors1348of the AV1308. The structure and operation of the AV sensor configurator1328is described in detail below with reference toFIG.14.

The sensor data store1332stores virtual sensor data generated by the AV sensor configurator1328as well as visual sensor data1352and odometry data1356generated by the visual sensors1344and the odometry sensors1348of the AV1308. The sensor data store1332is communicatively coupled to the AV sensor configurator1328. The data stored by the sensor data store1332is used by the AV sensor configurator1328for computation as well as by modules on the AV1308, such as the planning module404(inFIG.4) and the control module1340for navigation of the AV1308. The sensor data store1332may be organized as a database or table of images stored on one or more of removable or non-removable memory cards, tape cassettes, and computer hard drives.

In one embodiment, the sensor data store1332may include multiple data fields, each describing one or more attributes of sensor data. For example, the sensor data store1332stores virtual point cloud data of a virtual sensor generated by the AV sensor configurator1328, sensor data1352generated by the visual sensors1344representing coordinate positions of the objects1316,1320, pixels representing the objects1316,1320, raster images representing the coordinate positions of the objects1316,1320, pixels of a geometric viewport (illustrated below inFIG.15), two-dimensional cylindrical representations of a surface of the objects1316,1320, reflectance values of a surface of the objects1316,1320, LIDAR point cloud data generated by the LIDAR123or LIDAR system602(illustrated and described above inFIGS.1and6), or odometry data1356representing the AV1308's position, velocity, acceleration, and orientation generated by the odometry sensors1348.

The environment1304may be an example of the environment190illustrated and described above with reference toFIG.1. The environment1304represents a geographical area, such as a state, a town, a neighborhood, or a road network or segment. The environment1304includes the AV1308, and one or more objects1316,1320. The objects are physical entities external to the AV1308. In other embodiments, the environment1304includes additional or fewer components than those described herein. Similarly, the functions can be distributed among the components and/or different entities in a different manner than is described here.

The objects1316,1320are located within the environment1304external to the AV1308and are examples of the objects416shown inFIGS.4and5. In one embodiment, the object1316is a static portion or aspect of the environment1304, such as a road segment, a traffic signal a building, a parking space located on a road segment, a highway exit or entrance ramp, a plurality of lanes of a drivable area of the environment1304orientated in the same direction, an elevation of the drivable area, a curb located adjacent to the drivable area, or a median separating two lanes of the drivable area. Static objects have more permanent characteristics of the environment1304that do not change every day. In driving mode, once sensor data representing static characteristics is mapped, the AV1308can focus on navigating and mapping other sensor data representing more dynamic characteristics, such as another vehicle. In one embodiment, the object1320is a more-dynamic object, such as another vehicle, a pedestrian, or a cyclist. The sensor data representing the dynamic characteristics of the object1320instructs the AV1308to perform collision prediction and reduce driving aggressiveness if needed. The objects1316,1320are described above in more detail with reference to the physical object608, boundaries616of a physical object608, the physical object706, the ground802, and the object808inFIGS.6,7, and8.

In one embodiment, the objects1316,1320are other vehicles such as other AVs, semi-autonomous vehicles, or non-autonomous vehicles navigating or parked outside or within the environment1304. For example, a vehicle1316can enter and exit the environment1304during navigation as well as navigate within other environments. The vehicle1316may be part of the traffic experienced on roadways of the environment1304by the AV1308. In some embodiments, the vehicles1316,1320belong to one or more AV fleets.

The AV1308is a partly-autonomous or fully autonomous vehicle that uses its visual sensors1344, odometry sensors1348, and control module1340to navigate around objects1316,1320while following a trajectory, for example, the trajectory198shown inFIG.1, within the environment1304. The AV1308includes a communication device1360, the perception module1336, the control module1340, a display device1364, the visual sensors1344, and the odometry sensors1348. The AV1308is communicatively coupled to the AV sensor configurator1328. The AV1308may be an example of the AV100inFIG.1. In other embodiments, the AV1308includes additional or fewer components than those described herein. Similarly, the functions can be distributed among the components and/or different entities in a different manner than is described here.

The communication device1360communicates data such as sensor data1352generated by the visual sensors1344representing coordinate positions of the objects1316,1320, pixels representing the objects1316,1320, or raster images representing the coordinate positions of the objects1316,1320. In one embodiment, the communication device1360communicates data such as reflectance values of a surface of the objects1316,1320, LIDAR point cloud data generated by the LIDAR123or LIDAR system602(illustrated and described above inFIGS.1and6), or odometry data1356representing the AV1308's position, velocity, acceleration, and orientation generated by the odometry sensors1348. In one embodiment, the communication device1360communicates data such as measured or inferred properties of the AV1308's states and conditions with the server1324, a passenger within the AV1308, or other vehicles.

The communication device1360may be an example of the communication device140shown inFIG.1. The communication device1360is communicatively coupled to the AV sensor configurator1328across a network. In an embodiment, the communication device1360communicates across the Internet, electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). Portions of the communication device1360may be implemented in software or hardware. For example, the communication device1360or a portion of the communication device1360may be part of a PC, a tablet PC, an STB, a smartphone, an IoT appliance, or any machine capable of executing instructions that specify actions to be taken by that machine. The communication device1360is described in more detail above with respect to communication device140inFIG.1.

The perception module1336receives the visual sensor data1352from the visual sensors1344and the odometry data1356from the odometry sensors1348and performs object recognition and classification functions for the objects1316,1320. The perception module1336may be an example of the perception module402illustrated and described above with reference toFIG.4. The perception module1336is coupled to the AV sensor configurator1328to transmit the visual sensor data1352and the odometry data1356to the AV sensor configurator1328. Portions of the perception module1336may be implemented in software or hardware. For example, the perception module1336or a portion of the perception module1336may be part of a PC, a tablet PC, an STB, a smartphone, an IoT appliance, or any machine capable of executing instructions that specify actions to be taken by that machine. In one embodiment, the perception module1336determines a reflectance of a surface of the object1316using the visual sensor data1352. The reflectance of the surface is the effectiveness of the surface in reflecting light. The reflectance of surfaces of the objects1316,1320is used by the planning module404, perception module1336, or control module1340to navigate the AV1308around the objects1316,1320.

The control module1340uses inputs from the planning module404and the perception module1336to operate the brakes420c, steering420a, and throttle420b(illustrated and described above with reference toFIG.4) to navigate the AV1308within the environment1304. The control module1340may be an example of the control module406illustrated and described above with reference toFIG.4. The control module1340is coupled to the perception module1336. Portions of the control module1340may be implemented in software or hardware. For example, the control module1340or a portion of the control module1340may be part of a PC, a tablet PC, an STB, a smartphone, an IoT appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

To simulate a virtual sensor arranged in a given spatiotemporal configuration, the AV sensor configurator1328renders a virtual LIDAR point cloud of the virtual sensor. In one embodiment, the AV sensor configurator1328uses a real LIDAR sensor as a model to simulate the virtual sensor. The AV sensor configurator1328simulates the virtual sensor using parameters of the real LIDAR sensor, including a number of virtual lasers, a position and angle of each virtual laser, or a rotational speed of each virtual laser. The virtual LIDAR point cloud of the virtual sensor includes a plurality of coordinate positions representing a portion of the environment1304that is located within a virtual viewing range of the virtual sensor. The virtual LIDAR point cloud is a dataset of points that represent one or more 3D shapes or features of the portion of the environment1304located within the virtual viewing range of the virtual sensor. Each point in the virtual LIDAR point cloud has its own set of X, Y and Z coordinates and other attributes described below.

The AV sensor configurator1328renders, using the virtual LIDAR point cloud of the virtual sensor, a raster image representing coordinate positions of an object, for example, object1316. To render the raster image, the AV sensor configurator1328uses attributes of the dataset of points. The attributes represent time, flight line, intensity (the amount of light returning back from a coordinate position), or color of the object1316, etc. In one embodiment, the AV sensor configurator1328determines, using the coordinate positions of the object1316in the raster image, a distance from the AV1308to the object1316. In other embodiments, the perception module1336or the planning module404determines, using the coordinate positions of the object1316in the raster image, a distance from the AV1308to the object1316. For example, the distance from the AV1308to the object1316may be determined as follows. If the coordinate position of the AV1308in the environment1304is (p1, q1) and the coordinate position of the object1316is (p2, q2), the distance is determined by a square root of (p2−p1)2+(q2−q1)2.

In one embodiment, the perception module1336determines the distance from the AV1308to the object1316based on a distance of each cell in the raster image from a set of environmental features. The perception module1336may also determine a shortest path across a surface between the AV1308to the object1316. In another embodiment, the perception module1336measures the distance in terms of a cost, for example, energy expenditure of traveling to the object1316.

The control module1340of the AV1308navigates the AV1308to avoid collisions with the object1316based on the determined distance. In one embodiment, the control module1340navigates a discretized drivable area while performing collision checking or randomized planning, such as probabilistically exploring the drivable area around the object1316. In another embodiment, the control module1340follows a collision-free trajectory determined by the planning module404to avoid the object1316. In another embodiment, if the object1316is a moving object such as another vehicle, the control module1340infers the object1316's intention from its motion, such as giving way or acting aggressively. The control module1340triggers the steering control102, brakes103, gears, accelerator pedal or other acceleration control mechanisms if a predicted time to collision with the object1316falls below a threshold.

In one embodiment, the AV sensor configurator1328or the perception module1336determines, using the coordinate positions of the object1316in the raster image, a reflectance of a surface of the object1316. The reflectance of a surface of the object1316is the effectiveness of the surface in reflecting light. The reflectance is determined by the AV sensor configurator1328or the perception module1336as a fraction of incident light that is reflected by the surface. In one embodiment, the AV sensor configurator1328or the perception module1336determines a reflectance spectrum or spectral reflectance curve, which is a plot of the reflectance as a function of wavelength. In embodiments, the AV sensor configurator1328or the perception module1336may determine the hemispherical reflectance or directional reflectance of the surface of the object1316for use in navigation by the control module1340.

The control module1340navigates the AV1308to avoid a collision of the AV1308with the object1316based on the determined reflectance. In one embodiment, the environment1304is modeled as a probabilistic grid in which each grid cell is represented by a Gaussian distribution over reflectance values. The control module1340or planning module404uses Bayesian inference to preferentially weight grid cells most likely to be stationary within the environment1304to avoid collisions while driving the AV1308. In another embodiment, the control module1340or planning module404uses a reflectance-based inference grid based on the variations in reflectance introduced by laser source, angle of incidence, range, etc. The control module1340navigates the AV1308based on the appearance of the surface of the object1316from the reflectance-based inference grid. For example, a wet surface tends to reflect less infrared laser light than do dry surfaces.

The display device1364provides data to a passenger riding in the AV1308. The data may represent the trajectory198of the AV1308, passenger comfort settings, or operational metrics such as speed or acceleration, etc. The display device1364may be an example of the display312illustrated and described above with reference toFIG.3. The display device1364is coupled to the communication device1360and one or more other modules of the AV1308to receive the data to be displayed to the passenger. In one embodiment, the communication device1360transmits a raster image1368generated by the AV sensor configurator1328to the display device1364for display. The raster image represents coordinate positions of an object such as1316located within the environment1304, as described below with reference toFIG.14. The display device1364displays individual pixels as squares and constructs colors by adding the values for red, green and blue. In one embodiment, the raster image includes a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via the display device1364. The raster images are stored in image files in the sensor data store1332.

The one or more visual sensors1344sense a state of the environment1304, such as the presence and structure of the objects1316,1320, and transmit the sensor data1352and semantic data representing the state to the perception module1336. The visual sensors1344may be an example of the sensors122-123illustrated and described above with reference toFIG.1. The visual sensors1344are communicatively coupled to the perception module1336to transmit the sensor data1352and semantic data. The visual sensors1344include one or more monocular or stereo video cameras in the visible light, infrared or thermal (or both) spectra, LIDAR, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, and may include temperature sensors, humidity sensors, or precipitation sensors.

The visual sensors1344are arranged in a spatiotemporal configuration on the AV1308. In one embodiment, the visual sensors1344include LIDARs. The AV1308may be equipped with a single 360 degree LIDAR installed on the roof of the AV1308. In another embodiment, the AV1308includes a number of LIDARs. One or more LIDARs may be arranged on each side of the roof. The spatiotemporal configuration includes the pitch, roll, and heading of the LIDARs. The pitch of a LIDAR refers to the angular motion or angular orientation of the LIDAR about a transverse axis. The roll of a LIDAR refers to the rotational displacement or orientation of the LIDAR about a longitudinal axis. The heading of a LIDAR refers to the directional orientation of the LIDAR. An optimal spatiotemporal configuration is based on optimizing the information generated about the environment1304by the one or more LIDARs and the cost incurred in generating the information. In one embodiment, the visual sensors1344include 3D cameras. A 3D camera of the AV1308is used to acquire a larger field of view of the environment1304through a camera configuration. In this configuration, the environment1304is segmented into cubes and an optimal 3D camera configuration is determined by a number of cubes in the observation range of the cameras.

In one embodiment, the sensor data1352includes LIDAR point cloud data. For example, the LIDAR sensors1344of the AV1308are used to illuminate a target object1316with pulsed laser light and measure the reflected pulses. Differences in laser return times and wavelengths can then be used to generate the sensor data1352and create a digital 3-D representation (feature) of the target object1316. In one embodiment, the LIDAR point cloud data is stored as a multidimensional occupancy grid. The LIDAR point cloud data is pre-processed at the signal level and then processed at a higher level to extract features of the objects1316,1320. In some embodiments, a combination two- and three-dimensional grid structure is used and the space in these structures is tessellated into several discrete cells. The structure of the LIDAR point cloud data allows a large amount of raw measurement data to be handled by the perception module1336.

The sensor data1352represents coordinate positions of the object1316. In one embodiment, the measurement points in the sensor data1352are stored as a three-dimensional grid. Each grid cell of the three-dimensional grid has an associated probability. The probability refers to a likelihood that the grid cell is occupied by a portion of an object, for example, object1316. The grid cells that are occupied by a portion of the object1316have a probability greater than 0.5. The cells that are not occupied possess a probability less than 0.5 (white space). The grid coordinate system uses the spatiotemporal configuration of the visual sensors1344and the vehicle position (for example, determined using egomotion estimation) to represent the coordinate positions of the object1316. The perception module1336determines the spatial characteristics of the object1316using the sensor data1352.

In one embodiment, the visual sensors1344include spatially distributed smart camera or LIDAR devices capable of processing and fusing the sensor data1352of the environment1304from a variety of viewpoints into a more useful form of data than individual images. For example, the sensor data1352includes LIDAR point cloud data reflected from a target object1316. In another example, the sensor data1352includes an image of the environment1304. The sensor data1352is transmitted to the perception module1336for image processing, communication, and storage functions. The visual sensors1344are described above in more detail with reference to inputs502a-d, LIDAR system602, light604a-c, light emitter606, light detectors610, field of view614, and light804a-dinFIGS.6,7, and8. The sensor data1352is described above in more detail with reference to outputs504a-d, image612, and LIDAR data points704inFIGS.6,7, and8.

The one or more odometry sensors1348sense a state of the AV1308with respect to the environment1304and transmit odometry data1356representing the state of the AV1308to the perception module1336. The odometry sensors1348may be an example of the sensors121illustrated and described above with reference toFIG.1. The odometry sensors1348are communicatively coupled to the perception module1336to transmit the odometry data1356. The odometry sensors1348include one or more GNSS sensors, IMUs that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, or steering angle and angular rate sensors. An IMU is an electronic device that measures and reports the AV's specific force, angular rate, or the magnetic field surrounding the AV. The IMU uses a combination of accelerometers, gyroscopes, or magnetometers. The IMU is used to maneuver the AV. The IMU allows a GNSS receiver on the AV to work when GNSS-signals are unavailable, such as in tunnels, or when electronic interference is present. The odometry measurements include a speed, an acceleration, or a steering angle. The AV uses the odometry data to provide a uniquely identifying signature for distinguishing between different spatiotemporal locations within the environment.

In one embodiment, the odometry sensors1348measure and report the AV1308's spatiotemporal location, specific force, angular rate, or a magnetic field surrounding the AV1308, using a combination of accelerometers, gyroscopes, or magnetometers. In another embodiment, the odometry sensors1348generate odometry data1356including a speed, a steering angle, a longitudinal acceleration, or a lateral acceleration. The odometry sensors1348utilize the raw IMU measurements to determine attitude, angular rates, linear velocity, and position relative to a global reference frame. In one embodiment, the odometry data1356reported by the IMU is used to determine attitude, velocity, and position by integrating angular rate from a gyroscope to calculate angular position. The perception module1336or the AV sensor configurator1328integrates and correlates the odometry data1356with the sensor data1352to derive the coordinates of the AV1308and the objects1316,1320. The AV sensor configurator1328uses the odometry data1356to determine an optimal spatiotemporal configuration for the visual sensors1344based on the variation in odometry measurements with variation in spatiotemporal configuration for the visual sensors1344. The control module1340uses the odometry data1356to navigate the AV1308to avoid collisions with the objects1316,1320.

Among the benefits and advantages of the embodiments disclosed herein are that many different and complex self-driving scenarios can be simulated in a safe and cost-effective manner. The disclosed embodiments obviate driving millions of miles in a physical AV to analyze and verify different sensor configurations. In embodiments, sensors such as LIDARs, RADARs, and cameras can be simulated in dangerous and costly scenarios such as collisions of vehicles, which would be expensive using traditional methods on physical roads. Moreover, certain traditional sensors that rely on lasers, such as LIDARs are sometimes suboptimal when encountering reflective surfaces such as puddles of water or glass-fronted buildings. The disclosed embodiments can analyze and verify such scenarios and determine sensor accuracy. Furthermore, the disclosed embodiments can also be used to test the range and effectiveness of different sensors. AVs operating in different environmental conditions may require certain sensors. For example, dense urban environments may require spinning LIDARs whereas a highway or freeway environment may require solid state LIDARs. Other embodiments disclose performing blind spot analysis for various sensors to determine optimal sensor configuration involving the number, type, and spatial arrangement of the sensors.

Further benefits and advantages are that the virtual sensor simulations increase the usefulness of physically driving the AV1308through the environment by comparing the virtual point cloud of the simulation scenarios with the sensor data1352. The disclosed embodiments enable realistic sensor simulation at reduced cost and increased accuracy. Rendering the lines in the raster images does not affect the simulation performance and is useful in verifying the virtual point cloud data. For example, the raster images can be used to determine whether the virtual lasers and virtual point cloud are matched visually.

Solid-state LIDARs can have blind spots because the LIDARs emit rays in only a single direction. Moreover, the LIDARs are typically unable to sense heat. Therefore, traditional LIDARs can sometimes miss children or pets on a roadway. The embodiments disclosed herein provide an improved spatiotemporal configuration of LIDAR sensors that reduces blind spots and improves the detection of children and pets. Navigation of an AV using the optimal spatiotemporal configuration for the visual sensors1344obtained from the simulation of virtual sensors disclosed herein is more accurate and computationally less expensive than traditional methods. The AV is also able to efficiently determine localization for real-time navigation. Navigating the AV using the optimal spatiotemporal configuration for the visual sensors1344results in increased passenger and pedestrian safety, lower wear and tear on the AV, reduced travel time, a reduced travel distance, etc. Increased safety for other vehicles on the road network is also achieved.

Block Diagram of an AV Sensor Configurator

FIG.14illustrates a block diagram of an AV sensor configurator1328for determination of an optimal spatiotemporal sensor configuration for navigation of the AV1308using simulation of virtual sensors, in accordance with one or more embodiments. The AV sensor configurator1328builds a model of a virtual AV for simulation in a controlled environment to determine the optimal spatiotemporal sensor configuration by simulating virtual models of the visual sensors1344and odometry sensors1348of the AV1308. The AV sensor configurator1328includes a virtual AV model generator1400, a virtual viewing range segregator1404, a geometric viewport generator1408, a geometric viewport segregator1412, a virtual point cloud generator1416, a raster image generator1420, a sensor configuration generation module1424, and a communication interface1428. In other embodiments, the AV sensor configurator1328includes additional or fewer components than those described herein. Similarly, the functions can be distributed among the components and/or different entities in a different manner than is described here. The AV sensor configurator1328may be located on the server1324, as illustrated above inFIG.13, or on the AV1308. In an embodiment, the AV sensor configurator1328is part of the planning module404.

The virtual AV model generator1400generates a model of a virtual vehicle based on the AV1308to perform the sensor simulation. The virtual AV model generator1400is communicatively coupled to the virtual viewing range segregator1404, the virtual point cloud generator1416, and the sensor configurator generation module1424to generate the optimal spatiotemporal configuration of the visual sensors1344. Portions of the virtual AV model generator1400may be implemented in software or hardware. For example, the virtual AV model generator1400or a portion of the virtual AV model generator1400may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The virtual AV model generator1400generates a model of a virtual AV operating in the environment1304. In one embodiment, the model of the virtual AV includes a predictive model as well as a functional model of the AV1308's components and sensors. The model is designed to be flexible to define different driving scenarios for the AV1308. In one embodiment, the virtual AV model generator1400uses photorealistic simulation to model the AV1308's visual sensors1344, including cameras, LIDAR, and RADAR. The model of the virtual AV is used to simulate driving conditions, such as rainstorms, snowstorms, and glare on road surfaces.

The model of the virtual AV includes a virtual sensor, for example a virtual radar, virtual LIDAR, or virtual camera having a virtual viewing range. In one embodiment, the virtual sensor includes a number of virtual lasers separated into a number of groups. Each group of virtual lasers is angled and spaced from each other group as well as from the virtual viewing range. The individual virtual lasers are angled based on the virtual viewing range and the number of virtual lasers. Thus, different sensors and different sets of virtual lasers and angles are modeled. To simulate the virtual sensor, the virtual lasers are rotated in horizontal angular steps within a specific time frame and a hit position of each virtual laser is recorded. The virtual sensor within the model of the virtual AV is configured using parameters, such as a number of the virtual lasers, a virtual laser range, a rotation speed of the virtual sensor, a rotation angle between scans, a vertical offset between groups of virtual lasers, or a vertical viewing range.

The virtual viewing range segregator1404segregates a virtual viewing range of a virtual sensor of the model of the virtual AV. The virtual viewing range segregator1404is communicatively coupled to the virtual AV model generator1400to receive the model. Portions of the virtual viewing range segregator1404may be implemented in software or hardware. For example, the virtual viewing range segregator1404or a portion of the virtual viewing range segregator1404may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The virtual viewing range of the virtual sensor corresponds to a viewing range of a visual sensor1344of the AV1308operating in the environment1304. The virtual sensor sweeps or scans in a direction of the beam or rays, thus generating a collection of distance measurements within the virtual viewing range. The virtual viewing range is a range of horizontal and vertical angles through which the virtual sensor captures virtual sensor data. For example, a two-axis scanning virtual LIDAR captures shape information in the horizontal and vertical directions from a stationary location. In one embodiment, the virtual viewing range of the virtual sensor ranges from a single window to full spherical coverage of 360 by 180 degrees. In another embodiment, the virtual viewing range of the virtual sensor is 360 degrees in the horizontal and 30 to 120 degrees in the vertical.

The virtual viewing range segregator1404segregates the virtual viewing range of the virtual sensor into a plurality of frustums. Each frustum is a portion of a solid shape, such as a cone or a pyramid that lies between one or two parallel planes cutting the solid shape. For example, a right frustum is a parallel truncation of a right pyramid or a right cone. When all the edges of the frustum are identical, the frustum becomes a uniform prism. An example virtual viewing range1530of a virtual sensor segregated into frustums, for example the frustums1534,1538, is illustrated below with reference toFIG.15B. Each plane section of a frustum is a base of the frustum. The axis of a frustum is the same as the axis of the original cone or pyramid. A frustum may be circular if it has circular bases. The height of a frustum is the perpendicular distance between the planes of the two bases.

The geometric viewport generator1408generates a geometric viewport to simulate the virtual sensor. An example of a geometric viewport1500is illustrated below inFIG.15A. The geometric viewport generator1408is communicatively coupled to the virtual AV model generator1400and geometric viewport segregator1412to generate and transmit a geometric viewport. Portions of the geometric viewport generator1408may be implemented in software or hardware. For example, the geometric viewport generator1408or a portion of the geometric viewport generator1408may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The geometric viewport generator1408generates a geometric viewport including a plurality of pixels. The geometric viewport is a viewing region having a polygonal shape used for rendering a representation of the objects1316,1320as an image. In one embodiment, the geometric viewport includes an area of interest for the virtual sensor expressed in coordinates such as meters or GNSS coordinates. In one embodiment, the geometric viewport includes an area that is expressed in rendering-device-specific coordinates. For example, a plurality of pixels is used to express the screen coordinates in which the objects1316,1320are rendered. In one embodiment, the geometric viewport includes a 2D rectangle that is used to render a 3D environment as viewed by a spatiotemporal configuration of the virtual sensor.

In one embodiment, the geometric viewport has a rectangular shape, as illustrated with reference to the rectangular viewport1500below inFIG.15A. The geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor. As illustrated and described above with reference toFIG.6, a visual sensor such as the LIDAR system602emits light lays604a-cfrom a light emitter606, for example, a laser transmitter). The height of the geometric viewport thus corresponds to the number of rays emitted by the virtual sensor that is modeling a visual sensor1344of the AV1308. An example of a height1504of the rectangular viewport1500is illustrated below inFIG.15A. In one embodiment, the geometric viewport has a width that increases as a number of the frustums increases. The width corresponds to the density of the virtual sensor simulation or the density of the LIDAR returns. An example of a width1508of the rectangular viewport1500is illustrated below inFIG.15A. The number of rays emitted by the virtual sensor and the density of sensor returns correspond to the resolution of the LIDAR and allow the LIDAR to provide a three-dimensional view of the environment1304by scanning laser rays back and forth across the virtual viewing range.

The geometric viewport segregator1412divides the generated geometric viewport into sections to simulate the virtual sensor. An example of a segregated geometric viewport1500is illustrated below inFIG.15C. The geometric viewport segregator1412is communicatively coupled to the geometric viewport generator1408to receive the geometric viewport. Portions of the geometric viewport segregator1412may be implemented in software or hardware. For example, the geometric viewport segregator1412or a portion of the geometric viewport segregator1412may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The geometric viewport segregator1412segregates the geometric viewport into a plurality of sections. Each section of the geometric viewport is a virtual area used by the raster image generator1420to scale and size a raster image when rendering the raster image to the geometric viewport. An example of sections1560and1564of the geometric viewport1500is illustrated below inFIG.15C. Each section of the plurality of sections that the geometric viewport is segregated into corresponds to a frustum of the virtual viewing range. In one embodiment, each section of the geometric viewport corresponds to a region of the environment1304that is rendered on the geometric viewport. The geometric viewport segregator1412obtains a section by truncating, using parallel planes, a pyramid of vision of the virtual sensor. The section is thus an adaptation of a cone of vision that a visual sensor1344of the AV1308has to the geometric viewport.

In one embodiment, the segregating of the geometric viewport into the plurality of sections includes mapping a near plane of each frustum onto a corresponding section of the plurality of sections. The planes that intersect a frustum perpendicular to the viewing direction of the virtual sensor are called the near plane and the far plane. For example, a section may correspond to a frustum of a rectangular pyramid. An example of a near plane1542of a frustum1534is illustrated below with reference toFIG.15B.

The virtual point cloud generator1416renders a virtual point cloud of the virtual sensor. The virtual point cloud generator1416is communicatively coupled to the virtual AV model generator1400and the raster image generator1420to generate images representing the virtual point cloud data. Portions of the virtual point cloud generator1416may be implemented in software or hardware. For example, the virtual point cloud generator1416or a portion of the virtual point cloud generator1416may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The virtual point cloud generator1416renders a virtual point cloud of the virtual sensor. In an embodiment, the virtual point cloud is generated or modeled by projecting simple geometric shapes such as triangles that intersect the virtual laser beams generated by the virtual sensor. The virtual point cloud is then subsequently generated based on virtual sensor location and the relative position of the geometric shapes with respect to the virtual sensor. The virtual point cloud generator1416relies on the physical properties of the sensor to be simulated. For example, for simulating spinning LIDARs, the virtual point cloud generator1416accounts for the rotational movement of the spinning LIDAR motor and the movement of the entire LIDAR package, including the sensor housing, mounted on a moving vehicle). In an embodiment, the virtual point cloud generator1416simulates a virtual sensor by iteratively progressing the dynamic state of the simulated environment1304according to a fixed timestamp and then capturing the resulting viewing range of each laser at the iterated timestamp.

In one embodiment, the virtual point cloud generator1416uses parameters of the virtual sensor to tune the virtual sensor model and render the virtual point cloud. For example, the virtual point cloud generator1416may vary the scan angle, pulse rate frequency, sidelap, or mean point density of the virtual sensor to render the virtual point cloud. In one embodiment, the virtual point cloud generator1416uses a data-driven model of the virtual sensor, which is tuned based on real LIDAR data obtained from the visual sensors1344. The virtual point cloud generator1416uses a dataset, which is a set of pose-observation pairs of a LIDAR. Each virtual LIDAR pose-observation pair is converted by the virtual point cloud generator1416into the virtual point cloud. The pose of the LIDAR refers to the degrees of rotation and translation of the LIDAR's orientation. The pose-observation pair data is therefore used to reconstruct a 3D scene sensed by the LIDAR.

The virtual point cloud includes a plurality of coordinate positions representing a portion of the environment1304that is located within the virtual viewing range of the virtual sensor. The data points within the virtual point cloud include measurement coordinates of external surfaces of the objects1316,1320located within the virtual viewing range of the virtual sensor. In one embodiment, the virtual point cloud is converted to the plurality of coordinate positions using surface reconstruction. In other embodiments, the plurality of coordinate positions of the virtual point cloud is used to render a digital elevation model or a volumetric model of the portion of the environment1304that is located within the virtual viewing range of the virtual sensor.

In one embodiment, the virtual point cloud generator1416renders a plurality of virtual point clouds of a plurality of virtual sensors of the virtual vehicle. Feature curves of the objects1316,1320may be extracted from the plurality of virtual point clouds. The virtual point cloud generator1416extracts feature curves from the intersections of the plurality of virtual point clouds that represent regions of the environment1304. For example, the virtual point cloud generator1416uses linear approximation of the plurality of virtual point clouds through a variational-shape approximation approach. Variational-shape approximation is a process of repeatedly partitioning the plurality of virtual point clouds into a set of geometric shapes, for example, ellipses, that provide a concise representation of a surface of an object of the environment1304.

In one embodiment, the virtual point cloud generator1416aggregates the plurality of virtual point clouds into an aggregate virtual point cloud. The aggregate virtual point cloud represents a portion of the environment1304located within a virtual viewing range of the plurality of virtual sensors. For example, the virtual point cloud generator1416computes an axis-aligned bounding box for an overlapped region between the plurality of virtual point clouds. The virtual point cloud generator1416divides the bounding box into grid boxes and merges points within each grid box by averaging their locations and colors.

The raster image generator1420generates a raster image based on the virtual point cloud data. The raster image generator1420is communicatively coupled to the virtual point cloud generator1416to receive the virtual point cloud data. Portions of the raster image generator1420may be implemented in software or hardware. For example, the raster image generator1420or a portion of the raster image generator1420may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

In one embodiment, the raster image generator1420generates a plurality of raster images from the virtual point cloud data. The raster images provide a visual representation of how the virtual sensor is operating by rendering lines along the virtual lasers without having to display the virtual point cloud. Among the benefits and advantages of the disclosed approach are that rendering the lines does not affect the simulation performance and is useful in verifying the virtual point cloud data. For example, the raster images can be used to determine whether the virtual lasers and the virtual point cloud are matched visually.

Each raster image includes the plurality of pixels of the geometric viewport and represents coordinate positions of an object located within the environment. In one embodiment, a raster image includes a dot matrix data structure that represents a rectangular grid of pixels viewable via the geometric viewport. Each raster image rendered on the geometric viewport corresponds to a bitmap. The bitmap may be stored in the same format used for storage in the sensor data store1332or as a device-independent bitmap. Each raster image may be characterized by a width and a height of the raster image in pixels and by a number of bits per pixel or color depth. In one embodiment, a plurality of virtual sensors of the model of the virtual AV is arranged in a spatiotemporal configuration of a plurality of potential spatiotemporal configurations. For each such spatiotemporal configuration of the plurality of spatiotemporal configurations, the raster image generator1420renders a raster image representing the plurality of coordinate positions of the environment1304within the virtual viewing range of the plurality of virtual sensors.

In one embodiment, the rendering of the raster image includes receiving, using the visual sensors1344of the AV1308, the sensor data1352. The sensor data1352represents coordinate positions of an object located within the environment1304, for example, object1316. The process of receiving the sensor data1352representing coordinate positions of the object is illustrated and described in detail above with reference to the LIDAR system602inFIG.6. The perception module1336or the AV sensor configurator1328may transmit the sensor data1352to the sensor data store1332via the communication device1360and the communication interface1428. The raster image generator1420generates pixels representing the object1316. The generated pixels are combined with the sensor data1352to generate the raster image. In this manner spectral information may be combined with the pixels to increase object classification accuracy.

In one embodiment, the rendering of the raster image is based on a geometric position and a directional orientation of a visual sensor1344relative to the coordinate positions of an object, for example object1316. The rendering of the raster image is based on a position and a six degrees of freedom directional orientation of each visual sensor1344. For example, the six degrees of freedom directional orientation of a visual sensor may be determined by utilizing known reference geometries. In one embodiment, the rendering of the raster image is performed by varying parameters of the visual sensor1344, such as a horizontal angle, a horizontal resolution, a vertical angle, a vertical resolution, a range, a shape of a beam spot (circular, rectangular, or elliptical), a divergence, or a signal cutoff.

In one embodiment, the raster image includes a two-dimensional representation of a virtual three-dimensional cylindrical surface of the object, for example object1316. The virtual three-dimensional scene is projected on to a virtual three-dimensional cylindrical surface, which is then unwrapped to form a two-dimensional rectangle that contains a representation of the virtual three-dimensional cylindrical surface.

The raster image generator1420renders, onto the geometric viewport, a distinct raster image representing an object, for example1316. The AV sensor configurator1328determines a representational quality of the distinct raster image associated with the reflectance of a surface of the object1316. In one embodiment, the representational quality is used to classify the surface of the object1316as one that provides specular reflection or one that provides diffuse reflection. For specular surfaces, such as glass or polished metal, the reflectance is low at all angles except at the appropriate reflected angle. On the other hand, for diffuse surfaces, such as white paint, reflectance is more uniform. Thus, the reflectance of the surface of the object1316may be used to aid in object recognition and navigation within the environment1304. In another embodiment, the representational quality of the distinct raster image associated with the reflectance of the surface of the object1316is used to identify water bodies or water puddles on a roadway, such as to prevent hydroplaning by the AV1308. A water puddle on a roadway may have high reflectance only at certain wavelengths, while ice and snow generally have high reflectance across all visible wavelengths.

The sensor configuration generation module1424uses the simulation of the virtual sensors to generate an optimal spatiotemporal configuration for the visual sensors1344. The sensor configuration generation module1424is communicatively coupled to the virtual point cloud generator1416and the communication interface1428to generate the optimal spatiotemporal configuration. Portions of the sensor configuration generation module1424may be implemented in software or hardware. For example, the sensor configuration generation module1424or a portion of the sensor configuration generation module1424may be part of a PC, a tablet PC, an STB, a smartphone, an internet of things (IoT) appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

The sensor configuration generation module1424determines, based on the virtual point cloud of the virtual sensor, an optimal spatiotemporal configuration of the visual sensors1344of the AV1308. In one embodiment, the sensor configuration generation module1424uses the virtual point cloud to determine parameters of a visual sensor1344, such as a LIDAR. For example, features extracted from the virtual point cloud are matched to features extracted from the sensor data1352that represents the object1316. The spatiotemporal configuration of the visual sensors1344of the AV1308may thus be fine-tuned via regression analysis and simulation. In one embodiment, the resulting optimal spatiotemporal configuration of the visual sensors1344of the AV1308specifies a wide-angle-emitting visual sensor1344and wide-angle optics to focus the backscattered light and obtain the time-of-flight data for modeling the environment1304.

In one embodiment, the sensor configuration generation module1424determines, based on the aggregate virtual point cloud, an optimal spatiotemporal configuration of a plurality of visual sensors1344of the AV1308. Each visual sensor1344corresponds to a virtual sensor of the plurality of virtual sensors modeled by the virtual AV model generator1400. For example, the spatiotemporal configuration of the plurality of visual sensors1344may specify whether each visual sensor1344is laser-diode-based or uses an uncooled fiber laser. The spatiotemporal configuration may specify whether each visual sensor1344has the ability to split and route its high-power beams to multiple locations, etc. The spatiotemporal configuration may specify an optimal layout of a plurality of visual sensors1344.

In one embodiment, the sensor configuration generation module1424determines, based on the virtual point cloud of the virtual sensor, a blind spot of a visual sensor1344of the AV1308. The blind spot is a spatiotemporal location of the environment1304around the AV1308that cannot be directly observed by the visual sensors1344while the AV1308is navigating. For example, the sensor configuration generation module1424may use the data points in the virtual point cloud to identify areas of low visibility such as where lighting conditions blur the contrast between an object and its surroundings or areas blocked by other objects such as cargo. To identify the blind spot, the sensor configuration generation module1424determines when an object, such as object1316, is located at the blind spot. The plurality of coordinate positions that represent the portion of the environment1304that is located within the virtual viewing range of the virtual sensor is free of the object1316. Hence, the virtual point cloud does not contain the coordinate positions corresponding to the object1316.

In one embodiment, the sensor configuration generation module1424extends the viewing range of a visual sensor1344of the AV1308based on analyzing the virtual point cloud data of the virtual sensor. By extending the viewing range of the visual sensor1344, the sensor configuration generation module1424enables the visual sensors1344to generate a more-complete point cloud of the environment by reducing the time needed to sample the environment1344. For example, the spatiotemporal configuration of a visual sensor1344may angle each of the emitters and receivers above or below the horizontal to blanket more of the environment1344in the field of view of virtual lasers within the virtual sensor.

In an embodiment, the optimality or effectiveness of a spatiotemporal configuration of sensors can be determined by using numerical (or eyeballing) methods. For example, such methods are based on the visibility and range of simulated LIDAR point cloud. In one embodiment, the sensor configuration generation module1424determines an optimal spatiotemporal configuration of the plurality of spatiotemporal configurations based on the plurality of raster images. For example, based on the plurality of raster images, the sensor configuration generation module1424may determine an optimal number of visual sensors1344for the AV1308. LIDAR sensors are expensive, difficult to manufacture at scale, and may lack the robustness necessary to account for potholes and extreme temperatures. The disclosed embodiments therefore determine an optimal number of LIDAR sensors to navigate the environment1304and identify objects such as1316, while reducing the cost of deployment.

The communication interface1428communicates data such as a spatiotemporal configuration of the visual sensors1344, determined blind spot locations for the AV1308, coordinate positions of the objects1316,1320, raster images representing the coordinate positions of the objects1316,1320, or a geometric position and directional orientation of a visual sensor1344relative to the coordinate positions of an object1316. In one embodiment, the communication interface1428communicates instructions including an optimal spatiotemporal configuration of the visual sensors1344to the AV1308. The AV1308uses the instructions to configure and position its visual sensors1344according to the optimal spatiotemporal configuration to increase driving efficiency and safety.

The communication interface1428may be an example of the communication device140shown inFIG.1. The communication interface1428is communicatively coupled to the AV1308across a network. In an embodiment, the communication interface1428communicates across the Internet, electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). Portions of the communication interface1428may be implemented in software or hardware. For example, the communication interface1428or a portion of the communication interface1428may be part of a PC, a tablet PC, an STB, a smartphone, an IoT appliance, or any machine capable of executing instructions that specify actions to be taken by that machine.

Segregation of a Virtual Viewing Range and a Geometric Viewport

FIG.15Aillustrates a geometric viewport1500for determination of an optimal spatiotemporal sensor configuration for navigation of the AV1308using simulation of virtual sensors, in accordance with one or more embodiments. The geometric viewport1500has a rectangular shape. The rectangular viewport1500is used for rendering a representation of the objects1316,1320as an image. In one embodiment, the rectangular viewport1500includes an area of interest for the virtual sensor expressed in coordinates, such as in meters or GNSS coordinates. The rectangular viewport1500has a height1504corresponding to a number of rays emitted from the virtual sensor. The rectangular viewport1500has a width1508that increases as a number of the frustums increases. The number of rays emitted by the virtual sensor and the density of sensor returns correspond to the resolution of the LIDAR and allow the LIDAR to provide a three-dimensional view of the environment1304.

FIG.15Billustrates a virtual viewing range1530for determination of an optimal spatiotemporal sensor configuration for navigation of the AV1308using simulation of virtual sensors, in accordance with one or more embodiments. The virtual viewing range1530of the virtual sensor is segregated into a plurality of frustums, for example,1534,1538, and1542. The virtual viewing range1530of the virtual sensor corresponds to a viewing range of a visual sensor1344of the AV1308operating in the environment1304. Segregating the geometric viewport1500into a plurality of sections includes mapping a near plane of each frustum, such as1546,1550, and1554of the plurality of frustums onto a corresponding section of the plurality of sections.

FIG.15Cillustrates segregation of the rectangular viewport1500for determination of an optimal spatiotemporal sensor configuration for navigation of the AV1308using simulation of virtual sensors, in accordance with one or more embodiments. The rectangular viewport1500is segregated into a plurality of sections, for example, sections1560,1564, and1568. Each section, for example1560, corresponds to a frustum, for example1534.

Example Environment for Determination of an Optimal Spatiotemporal Sensor Configuration

FIG.16illustrates an example environment1600for determination of an optimal spatiotemporal sensor configuration for navigation of an AV using simulation of virtual sensors, in accordance with one or more embodiments. The virtual AV model generator1400generates a model of a virtual vehicle1604operating in the environment1600. The model of the virtual vehicle includes virtual sensors1608,1612.

In one embodiment, the virtual sensor1608includes a topographic LIDAR, a bathymetric LIDAR, or a terrestrial LIDAR. In a topographic LIDAR, a pulsed laser is optically coupled to a beam director, which scans the laser pulses over a swath of terrain, usually centered on, and co-linear with, a trajectory of the vehicle on which the LIDAR is mounted. Unlike a topographic LIDAR, which uses an infrared wavelength of light, a bathymetric LIDAR typically uses a green wavelength of light to scan water bodies. A terrestrial LIDAR is a land-based laser scanner which, combined with a differential GNSS, enables the production of three-dimensional computer models. Each of the virtual sensors1608,1612has a virtual viewing range. The virtual viewing range segregator segregates the virtual viewing range of the virtual sensors1608,1612into a plurality of frustums. The virtual viewing range of the virtual sensor1608corresponds to a viewing range of a sensor1344of the AV operating in the environment1600.

The geometric viewport generator1408generates a geometric viewport, for example the viewport1500illustrated and described above with reference toFIG.15A. The geometric viewport has a height, for example height1504, corresponding to a number of rays1616emitted from the virtual sensor. The raster image generator1420generates a raster image rendered onto the geometric viewport1500. The raster image includes a plurality of pixels of the geometric viewport1500and represents coordinate positions of objects located within the environment1600, for example, pedestrians1620,1624, and1628.

In one embodiment, the model of the virtual vehicle1604, including the virtual sensors1608,1612, is simulated to determine the quality of the sensor data1352obtained and to determine which sensors are best suited to different operating conditions for the AV1604under different environmental conditions. For example, one type of sensor may perform better than another type of sensor in urban environments that contain many buildings, construction zones, or pedestrians. Similarly, one type of sensor may perform better than another type of sensor in rainy or snowy weather when there are puddles of water on the ground surface.

The AV sensor configurator1328receives data describing the environment1600in which the AV1604is operating. The data describing the environment1600may include a pattern of weather, such as the temperature, whether it is a rainy or snowy day, and the visibility. The data describing the environment1600may also include parameters describing a density of the environment1600, such as a number of buildings per square mile, the amount of the environment1600that is covered by road surface, a number of pedestrians, for example pedestrian1620,1624,1628, an amount of vegetation per square mile, etc.

For each sensor of the visual sensors1344, the virtual AV model generator1400generates a model of the virtual AV1604operating in the environment1600. The model of the virtual AV1604includes at least one virtual sensor1608. Using the received data describing the environment1600, a virtual point cloud of the virtual sensor1608is rendered. In one embodiment, the model of the virtual AV1604includes a position of the virtual sensor1608. For example, the position of the virtual sensor1608may be denoted by rectangular coordinates within the environment1600, a spatiotemporal configuration relative to the AV1604, or by a distance from the pedestrian1620. The AV sensor configurator1328renders the virtual point cloud of the virtual sensor1608by projecting a geometric shape that intersects a virtual laser generated by the virtual sensor1608. For example, the AV sensor configurator1328may project one or more triangles that intersect a virtual laser beam generated by the virtual sensor1608. The AV sensor configurator1328determines a position of the geometric shape, for example, the one or more triangles, relative to the position of the virtual sensor1608. The position of the geometric shape is then used to form the virtual point cloud data. Further details on the virtual point cloud generation are described above with reference toFIG.14.

Referring back toFIG.16, in one embodiment, the virtual sensor1608is a virtual spinning LIDAR. A spinning LIDAR has a 360° field of view because a single spinning LIDAR can be mounted on the roof of the AV1604to obtain a complete view of the surroundings of the AV1604. The AV sensor configurator1328renders the virtual point cloud of the virtual sensor1608(spinning LIDAR) by simulating rotational movement of a motor of the virtual spinning LIDAR1608. For example, the rotational movement of the virtual spinning LIDAR1608may include a +15° to −25° vertical field of view, a range of 300 m, an angular resolution of 0.10°, and a mapping rate of 8 million points per second.

In one embodiment, the AV sensor configurator1328renders the virtual point cloud of the virtual sensor1608by segregating a virtual viewing range of the virtual sensor1608into a plurality of frustums (e.g., frustums1534,1538), as described and illustrated above with reference toFIG.15B. The plurality of frustums are used in the generation of the virtual point cloud of the virtual sensor1608. In one embodiment, the AV sensor configurator1328renders the virtual point cloud of the virtual sensor1608by generating a geometric viewport, such as the viewport1500illustrated and described above with reference toFIG.15A. The geometric viewport includes a plurality of pixels. The geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor1608. The geometric viewport is used to generate the virtual point cloud of the virtual sensor1608. The raster image generator1420renders a raster image representing a plurality of coordinate positions of the environment1600, as described above with reference toFIG.14. The raster image includes the plurality of pixels of the geometric viewport and represents coordinate positions of an object, for example pedestrian1620located within the environment1600.

In one embodiment, the AV sensor configurator1328renders the virtual point cloud of the virtual sensor1608by segregating a geometric viewport into a plurality of sections, as illustrated and described above with reference toFIGS.14and15C. Each section of the plurality of sections corresponds to a frustum of a plurality of frustums of a virtual viewing range of the virtual sensor1608. The AV sensor configurator1328generates, using the plurality of sections of the geometric viewport, the virtual point cloud of the virtual sensor1608. In one embodiment, the geometric viewport has a width that increases as a number of the plurality of frustums increases. In one embodiment, the segregating of the geometric viewport into the plurality of sections includes mapping a near plane of each frustum of the plurality of frustums onto a corresponding section of the plurality of sections, as described and illustrated above with reference toFIGS.14and15B.

Referring now toFIG.16, a quality metric of the virtual sensor1608is determined using the virtual point cloud. The quality metric reflects the range and visibility of the virtual sensor1608and is used to compare different types of visual sensors under different operating conditions of the AV1604. The quality metric may be expressed as a vector of different components, for example, viewing range, or a weighted aggregate of the components. Certain components that are more important on rainy days, for example determining a reflectance of a surface of an object may be weighted higher than other components of the quality metric.

In one embodiment, the quality metric includes a range of the virtual sensor1608or a visibility of the virtual sensor1608. The range and visibility of the virtual sensor1608may depend on a speed at which an object, for example pedestrian1620is scanned. Certain virtual sensors may include oscillating plane mirrors, a polygonal mirror, or a dual-axis scanner that increase the range in certain weather conditions. In one embodiment, the quality metric includes a point density of the virtual point cloud. The point density refers to an average number of points per unit area, which may be expressed as points per square meter. The point density may also be determined as an average distance between points (nominal point spacing).

In one embodiment, the quality metric includes a vertical accuracy of the virtual sensor1608. The vertical accuracy is expressed as the root mean square error (RMSE) and is a measure of the absolute deviation of the point cloud data from a known vertical datum, such as a surveyed location. In one embodiment, the quality metric includes a precision of the virtual sensor1608, which refers to the repeatability of a sensor measurement. The quality metric may be affected by the types of light that the virtual sensor1608uses to image an object, for example pedestrian1620. For example, a typical LIDAR uses ultraviolet, visible, or near infrared light to image objects. The quality metric may include the range of materials, including metals, non-metallic objects, or rocks that the virtual sensor can detect.

In one embodiment, the AV sensor configurator1328determines the quality metric of the virtual sensor1608by determining, using the virtual point cloud of the virtual sensor1608, a size of a blind spot of the sensor of the AV1604. Determination of a blind spot of a virtual sensor is described in detail above with reference toFIG.14. Referring now toFIG.16, the pedestrian1628may be located in a blind spot of the virtual sensor1608. The blind spot includes a plurality of coordinate positions of the environment1600. If the pedestrian1628is located in a blind spot, the plurality of coordinate positions sensed by the virtual sensor1608will be free of the pedestrian1628. The size of a blind spot of a sensor affects sensor quality and the viewing range. For example, a blind spot located where a pedestrian or another vehicle suddenly crosses a street may lead to a collision with the AV. Therefore, the quality metric is based on the size of the blind spot of the virtual sensor1608.

In one embodiment, the AV sensor configurator1328determines the quality metric of the virtual sensor1608by rendering, using a timestamp, a state of the environment1600. For example, two-dimensional LIDAR data may be rendered in a floating point binary format and the timestamp of the two-dimensional LiDAR data1352is stored. A virtual viewing range of the virtual sensor1608at the timestamp is determined. The different virtual sensor types may be compared using the virtual viewing ranges to determine which sensor type is best suited to the environment1600. In one embodiment, the AV sensor configurator1328determines the quality metric of the virtual sensor1608by rendering, using the virtual point cloud of the virtual sensor1608, a raster image describing the environment1600. Using the raster image, a reflectance of a surface of an object, for example pedestrian1620in the environment1600is determined. The performance of the virtual sensor1608in the presence of objects having a higher reflectance can affect the range and visibility on rainy days. Generation of the raster image and determination of reflectance is described in detail above with reference toFIG.14.

Using the plurality of quality metrics across the different virtual sensors, a range of different sensor types are evaluated. An optimal sensor for operating the vehicle within the environment1600is selected. For example, a spinning LIDAR may be better suited to certain environmental conditions than a solid-state LIDAR or a smart camera.

Process for Determination of an Optimal Spatiotemporal Sensor Configuration for an AV

FIG.17illustrates a process1700for determination of an optimal spatiotemporal sensor configuration for navigation of the AV1308using simulation of virtual sensors, in accordance with one or more embodiments. In one embodiment, the process ofFIG.1700is performed by the AV sensor configurator1328. Other entities, for example, one or more components of the AV1308perform some or all of the steps of the process1700in other embodiments. Likewise, embodiments may include different and/or additional steps, or perform the steps in different orders.

The AV sensor configurator1328generates1704a model of a virtual vehicle operating in the environment1304. The model of the virtual vehicle includes a virtual sensor having a virtual viewing range. In one embodiment, the virtual sensor includes a number of virtual lasers separated into a number of groups. Each group of virtual lasers is angled and spaced from each other group as well as from the virtual viewing range. The individual virtual lasers are angled based on the virtual viewing range and the number of virtual lasers. Thus, different sensors and different sets of lasers and angles are modeled.

The AV sensor configurator1328segregates1708the virtual viewing range, for example1530, of the virtual sensor into a plurality of frustums. The virtual viewing range1530of the virtual sensor corresponds to a viewing range of a visual sensor1344of the AV1308operating in the environment1304. The virtual sensor sweeps or scans in a direction of the beam or rays, thus generating a collection of distance measurements within the virtual viewing range1530. The virtual viewing range1530is a range of horizontal and vertical angles through which the virtual sensor captures virtual sensor data. Each frustum is a portion of a solid shape, such as a cone or a pyramid that lies between one or two parallel planes cutting the solid shape.

The AV sensor configurator1328generates1712a geometric viewport, for example1500, including a plurality of pixels. The geometric viewport1500has a height, for example1504, corresponding to a number of rays emitted from the virtual sensor. The geometric viewport1500is a viewing region having a polygonal shape used for rendering a representation of the objects1316,1320as an image. For example, a plurality of pixels is used to express the screen coordinates in which the objects1316,1320are rendered.

The AV sensor configurator1328segregates1716the geometric viewport1500into a plurality of sections, for example, sections1560,1564. Each section of the plurality of sections corresponds to a frustum of the plurality of frustums. Each section of the geometric viewport1500is a virtual area used by the raster image generator1420to scale and size a raster image when rendering the raster image to the geometric viewport1500. In one embodiment, each section of the geometric viewport corresponds to a region of the environment1304that is rendered on the geometric viewport.

The AV sensor configurator1328renders1720a virtual point cloud of the virtual sensor, wherein the virtual point cloud includes a plurality of coordinate positions representing a portion of the environment1304located within the virtual viewing range of the virtual sensor. The AV sensor configurator1328uses parameters of the virtual sensor to tune the virtual sensor model and render the virtual point cloud. For example, the AV sensor configurator1328may vary the scan angle, pulse rate frequency, sidelap, or mean point density of the virtual sensor to render the virtual point cloud.

The AV sensor configurator1328determines1724, based on the virtual point cloud of the virtual sensor, an optimal spatiotemporal configuration of the visual sensor1344of the AV1308. In one embodiment, the AV sensor configurator1328uses the virtual point cloud to determine parameters of the visual sensor1344, such as a LIDAR. For example, features extracted from the virtual point cloud may be matched to features extracted from the sensor data1352that represent the objects1316. The spatiotemporal configuration of the visual sensors1344of the AV1308are thus fine-tuned via regression analysis and simulation.

Process for Determining an Optimal Sensor

FIG.18illustrates a process1800for determining an optimal sensor for navigation of an AV1308using simulation of virtual sensors, in accordance with one or more embodiments. In one embodiment, the process ofFIG.1800is performed by the AV sensor configurator1328. Other entities, for example, one or more components of the AV1308perform some or all of the steps of the process1800in other embodiments. Likewise, embodiments may include different and/or additional steps, or perform the steps in different orders.

The AV sensor configurator1328receives1804, using one or more processors, data describing an environment1304in which the AV1308is operating. The data describing the environment1304may include a pattern of weather, such as the temperature, whether it is a rainy or snowy day, and the visibility. The data describing the environment1304may also include parameters describing a density of the environment1304, such as a number of buildings per square mile, the amount of the environment1304that is covered by road surface, a number of pedestrians, an amount of vegetation per square mile, etc.

For each sensor of a plurality of sensors1344of the AV1308, the AV sensor configurator1328generates1808, using the one or more processors, a model of a virtual AV operating in the environment1304. The model of the virtual AV includes a virtual sensor corresponding to the sensor.

The AV sensor configurator1328renders1812, using the received data describing the environment1304, a virtual point cloud of the virtual sensor. In one embodiment, the model of the virtual AV includes a position of the virtual sensor. For example, the position of the virtual sensor may be denoted by rectangular coordinates within the environment1304, a spatiotemporal configuration relative to the AV1308, or by a distance from an object1316. The AV sensor configurator1328renders the virtual point cloud of the virtual sensor by projecting a geometric shape that intersects a virtual laser generated by the virtual sensor. The AV sensor configurator1328determines a position of the geometric shape relative to the position of the virtual sensor. The position of the geometric shape is then used to form the virtual point cloud data.

The AV sensor configurator1328determines1816, using the virtual point cloud, a quality metric of the virtual sensor. The quality metric reflects the range and visibility of the virtual sensor and is used to compare different types of visual sensors under different operating conditions of the AV1308. The quality metric may be expressed as a vector of different components, for example, viewing range, or a weighted aggregate of the components. Certain components that are more important on rainy days, for example determining a reflectance of a surface of an object may be weighted higher than other components of the quality metric.

The AV sensor configurator1328selects1820, using the plurality of quality metrics, an optimal sensor of the plurality of sensors1344for operating the AV1308within the environment1304.

Additional Embodiments

In some embodiments, one or more processors of a vehicle are used receive data describing an environment in which the vehicle is operating. For each of multiple sensors of the vehicle, the one or more processors generate a model of a virtual vehicle operating in the environment. The model of the virtual vehicle includes a virtual sensor corresponding to the sensor. The received data describing the environment is used to render a virtual point cloud of the virtual sensor. The virtual point cloud is used to generate a quality metric of the virtual sensor. The quality metric includes a range of the virtual sensor or a visibility of the virtual sensor. The quality metrics are used to select an optimal sensor of the multiple sensors for operating the vehicle within the environment.

In some embodiments, the quality metric further includes a point density of the virtual point cloud.

In some embodiments, the quality metric further includes a vertical accuracy of the virtual sensor.

In some embodiments, the quality metric further includes a precision of the virtual sensor.

In some embodiments, the quality metric further includes a virtual viewing range of the virtual sensor.

In some embodiments, the determining of the quality metric of the virtual sensor includes rendering, using a timestamp, a state of the environment. The one or more processors determine the virtual viewing range of the virtual sensor at the timestamp.

In some embodiments, the determining of the quality metric of the virtual sensor includes determining, using the virtual point cloud of the virtual sensor, a size of a blind spot of the sensor of the vehicle.

In some embodiments, the blind spot includes multiple coordinate positions of the environment. An object is located at the blind spot. The multiple coordinate positions are free of the object.

In some embodiments, the virtual sensor includes a topographic LIDAR, a bathymetric LIDAR, or a terrestrial LIDAR.

In some embodiments, the model of the virtual vehicle further includes a position of the virtual sensor. The rendering of the virtual point cloud of the virtual sensor includes projecting, using the one or more processors, a geometric shape that intersects a virtual laser generated by the virtual sensor. The one or more processors determine a position of the geometric shape relative to the position of the virtual sensor.

In some embodiments, the virtual sensor is a virtual spinning LIDAR. The rendering of the virtual point cloud of the virtual sensor includes simulating, using the one or more processors, rotational movement of a motor of the virtual spinning LIDAR.

In some embodiments, the determining of the quality metric of the virtual sensor includes rendering, using the virtual point cloud of the virtual sensor, a raster image describing the environment. The raster image is used to determine a reflectance of a surface of an object in the environment.

In some embodiments, the rendering of the virtual point cloud of the virtual sensor includes segregating, using the one or more processors, a virtual viewing range of the virtual sensor into multiple frustums. The virtual viewing range of the virtual sensor corresponds to a viewing range of the sensor of the vehicle. The frustums are used to generate the virtual point cloud of the virtual sensor.

In some embodiments, the rendering of the virtual point cloud of the virtual sensor includes generating, using the one or more processors, a geometric viewport including multiple pixels. The geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor. The geometric viewport is used to generate the virtual point cloud of the virtual sensor.

In some embodiments, a raster image is rendered representing multiple coordinate positions of the environment. The raster image includes the multiple pixels of the geometric viewport and represents coordinate positions of an object located within the environment.

In some embodiments, the rendering of the virtual point cloud of the virtual sensor includes segregating, using the one or more processors, a geometric viewport into multiple sections. Each section corresponds to a frustum of multiple frustums of a virtual viewing range of the virtual sensor. The multiple sections of the geometric viewport are used to generate the virtual point cloud of the virtual sensor.

In some embodiments, the geometric viewport has a width that increases as a number of the frustums increases.

In some embodiments, the segregating of the geometric viewport into the multiple sections includes mapping a near plane of each frustum of the multiple frustums onto a corresponding section of the multiple sections.

In some embodiments, the raster image is used to determine a reflectance of a surface of the object. A control module of the vehicle is used to operate the vehicle to avoid a collision of the vehicle with the object based on the reflectance.

In some embodiments, a distinct raster image representing the object is rendered onto the geometric viewport. A representational quality of the distinct raster image associated with the reflectance of a surface of the object is determined.

In the foregoing description, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further including,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub-entity of a previously-recited step or entity.