KR102223836B1 - Electric steering torque compensation - Google Patents

Abstract

Above all, it describes the technology for electric steering torque compensation. A technique is provided for a method implemented by a computer, for example a computer mounted on an autonomous vehicle. A planning circuit mounted on the vehicle and connected to the vehicle's EPS determines a compensation torque signal to correct the actual steering angle of the vehicle's steering wheel to match the expected steering angle of the steering wheel. The planning circuit transmits a compensation torque signal to a control circuit that controls the steering angle of the steering wheel. The EPS corrects the actual steering angle based on the compensation torque signal, resulting in a corrected steering angle. The control circuit operates the vehicle based on the modified steering angle.

Description

Electric steering torque compensation

Cross-reference of related applications

This application claims priority to U.S. Provisional Patent Application No. 62/798,419, filed Jan. 29, 2019, and Danish Patent Application No. PA201970163, filed Mar. 14, 2019, the full contents of which are by reference. Incorporated herein by.

Field of invention

This description relates to electric power steering torque compensation.

An electric power steering (EPS) system is a system that rotates a vehicle in one direction or another by applying torque to a steering column of a vehicle. The EPS (sometimes called electric power-assisted steering) system is mounted on a steering column or steering gear and applies torque to the steering column, which rotates the steering wheel. In an autonomous vehicle, the EPS system can apply torque to the steering column, thereby controlling the operation of the vehicle.

A technique is provided for a method implemented by a computer, for example a computer mounted on an autonomous vehicle. A planning circuit mounted on the vehicle and connected to the vehicle's EPS system is a compensatory torque signal to modify the actual steering angle of the vehicle's steering wheel to match the expected steering angle of the steering wheel. torque signal). The planning circuit transmits a compensation torque signal to a control circuit that controls the steering angle of the steering wheel. The EPS system corrects the actual steering angle based on the compensation torque signal, resulting in a corrected steering angle. The control circuit operates the vehicle based on the modified steering angle.

These and other aspects, features, and implementations may be represented as a method, apparatus, system, component, program product, means, or step for performing a function, and in other ways.

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

1 shows an example of an autonomous vehicle having an autonomous driving capability.
2 illustrates an example "cloud" computing environment.
3 illustrates a computer system.
4 shows an exemplary architecture for an autonomous vehicle.
5 shows an example of inputs and outputs that can be used by the cognitive module.
6 shows an example of a LiDAR system.
7 shows a LiDAR system in operation.
8 shows the operation of the LiDAR system in additional detail.
9 shows a block diagram of the relationship between the inputs and outputs of the planning module.
10 shows a direction graph used in route planning.
11 shows a block diagram of the inputs and outputs of the control module.
12 shows a block diagram of the inputs, outputs, and components of the controller.
13 shows a schematic diagram of a controller connected to the steering wheel.
14 is a flowchart of an exemplary process of applying compensation torque to a steering column.
15 is an example of a voltage signal that can be applied to the EPS system.
16 is a schematic diagram of a control loop with a feedforward function.
17 is a plot relating vehicle condition and steering angle to feedforward torque.

In the following description for purposes of explanation, a number of specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

In the drawings, for ease of description, a specific arrangement or sequence of schematic elements is shown, such as representing devices, modules, instruction blocks and data elements. However, it will be appreciated by those of ordinary skill in the art that a particular order or arrangement of schematic elements in the figures is not meant to imply that a particular order or sequence of processing, or separation of processes, is required. Furthermore, the inclusion of schematic elements in the drawings does not imply that such elements are required in all embodiments, or features expressed by such elements are not included in some embodiments or combined with other elements. It doesn't mean to imply that it might not.

Furthermore, in the drawing, where a connecting element such as a solid or dashed line or arrow is used to illustrate a connection, relationship or association between two or more different schematic elements, the absence of any such connecting element is a connection, relationship or association. It does not mean to imply that it cannot exist. In other words, some connections, relationships, or associations between elements are not shown in the drawings in order not to obscure the present disclosure. In addition, to facilitate illustration, a single connecting element is used to represent multiple connections, relationships or associations between the elements. For example, if the connecting element represents a communication of signals, data or instructions, if one of ordinary skill in the art would like to, such an element may be necessary to carry out the communication, one or more signal paths (e.g. For example, you will understand that express bus).

Embodiments will now be referred to in detail, in which examples are illustrated in the accompanying drawings. In the detailed description that follows, many specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to those skilled in the art that various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail in order not to unnecessarily obscure aspects of the embodiments.

Several features are described below that can each be used independently of one another or with any combination of other features. However, any individual feature may not be able to solve any of the problems discussed above or may only solve one of the problems discussed above. Some of the problems discussed above may not be completely solved by any of the features described herein. Although multiple headings are provided, information relating to a particular heading but not found in the section bearing that heading may be found elsewhere in the description. Examples are described herein according to the outline below.

1. General overview

2. System overview

3. Autonomous vehicle architecture

4. Autonomous vehicle input

5. Self-driving vehicle planning

6. Autonomous vehicle control

General overview

The present disclosure describes an autonomous vehicle having a planning circuit (described below) mounted on a vehicle that is connected to the vehicle's EPS system. The EPS system which rotates the steering wheel to a desired steering angle by applying a base torque to the steering column of the vehicle is controlled by a control circuit. In some embodiments, the control circuit determines a base torque that will cause a desired steering angle, determines a base torque signal (e.g., a voltage signal) of a magnitude corresponding to the magnitude of the base torque, and generates the base torque signal. Send to EPS. In response, the EPS applies the base torque represented by the base torque signal to the steering column. In this way, the base torque signal is expected to rotate the steering wheel by the desired steering angle. In some cases, for example at certain vehicle speeds, the actual steering angle at which the steering wheel turns in response to the base torque signal deviates from the desired (or expected) steering angle. In some embodiments, the actual steering angle is provided to the control circuit as a feedback signal, and the base torque is compensated by a compensation torque that further modifies the actual steering angle to substantially match the expected steering angle.

The present disclosure describes applying a compensation torque to the steering column as a feedforward signal rather than a feedback signal based on knowing in advance the expected deviation of the actual steering angle from the expected steering angle, that is, before the base torque signal is applied to the EPS. do. The feedforward signal represents the amount of compensation torque for which the base torque must be adjusted (eg, increased or decreased) so that the actual steering angle substantially matches the expected steering angle. In other words, based on the vehicle driving conditions, the planning circuit predicts a deviation between the actual steering angle and the expected steering angle. As a proactive response, the planning circuit determines a compensation torque signal to correct the actual steering angle of the steering wheel to match the expected steering angle, and transmits the compensation torque signal to the control circuit. The control circuit modifies the base torque signal according to the compensation torque signal and applies the modified torque signal to the EPS system. Embodiments of the present disclosure are described with reference to a torque applied to a steering column to rotate a steering wheel connected to the steering column. The techniques described herein are applicable to vehicles without steering wheels in which the steering column operates other features of the vehicle to rotate the vehicle.

System overview

1 shows an example of an autonomous vehicle 100 having an autonomous driving capability.

As used herein, the term “autonomous driving capability” means that a vehicle will be partially or completely operated without real-time human intervention, including, but not limited to, fully autonomous vehicles, highly autonomous vehicles, and conditional autonomous vehicles. Refers to a function, feature, or facility that enables it.

As used herein, an autonomous vehicle (AV) is a vehicle with autonomous driving capability.

As used herein, “vehicle” includes the means of transportation of goods or people. For example, cars, buses, trains, airplanes, drones, trucks, boats, ships, submarines, airships, etc. Driverless cars are an example of a vehicle.

As used herein, “trajectory” refers to a path or route that travels the AV from a first spatiotemporal location to a second spatiotemporal location. In one embodiment, the first spatiotemporal location is referred to as an initial or starting location and the second spatiotemporal location is referred to as a destination, final location, target, target location, or target location. In some examples, a trajectory is composed of one or more segments (eg, sections of a road), and each segment is composed of one or more blocks (eg, portions of a lane or intersection). In one embodiment, the spatiotemporal location corresponds to a real world location. For example, a spatiotemporal location is a pick up or drop-off location where people are picked up or unloaded, or goods are loaded or unloaded.

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 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. , Data storage devices (eg, RAM and/or non-volatile storage), software or firmware components, and data processing components such as application-specific integrated circuits (ASICs), microprocessors and/or microcontrollers.

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

As used herein, a “road” is a physical area that can be traversed by a vehicle and may correspond to a named major road (eg, city street, interstate, etc.), or an unnamed major road ( For example, it may correspond to a private road in a house or office building, a parking lot section, a vacant lot section, an unpaved route in a rural area, etc.) Because some vehicles (e.g., four-wheel drive pickup trucks, sport utility vehicles, etc.) may traverse various physical areas that are not particularly suitable for vehicle progression, "roads" are designated by any municipality or other government or administrative agency. It may be a physical area that is not officially defined as a major road.

As used herein, a “lane” is a portion of a road that can be traversed by a vehicle. Lanes are sometimes identified based on lane marking. For example, a lane may correspond to most or all of the space between lane markings, or may correspond to only a portion (eg, less than 50%) of the space between lane markings. For example, a road with distantly spaced lane markings can accommodate more than one vehicle between the lane markings, so that one vehicle can overtake another vehicle without crossing the lane markings, and thus the lane markings. It can be interpreted as having a lane narrower than the space between or having two lanes between the lane markings. Lanes can be interpreted even in the absence of lane markings. For example, lanes may be defined based on physical characteristics of the environment, such as rocks and trees along major roads in rural areas, or natural obstacles to avoid, for example in undeveloped areas. Lanes can also be interpreted independently of lane marking or physical characteristics. For example, a lane may be interpreted based on an arbitrary path without obstacles in an area where there is no other characteristic to be interpreted as a lane boundary. In an exemplary scenario, the AV can interpret the lane through an unobstructed portion of a field or vacant lot. In another exemplary scenario, the AV may interpret lanes through wide (eg, wide enough for two or more lanes) roadways that do not have lane markings. In this scenario, an AV can convey information about lanes to other AVs, so that different AVs can use the same lane information to coordinate route planning between themselves.

The term “over-the-air (OTA) client” refers to any AV, or any electronic device (e.g., computer, controller, IoT device, electronic device that is embedded in, coupled to, or communicates with the AV). Control unit (electronic control unit (ECU)).

The term “over-the-air (OTA) update” includes cellular mobile communications (eg, 2G, 3G, 4G, 5G), radio wireless area networks (eg, WiFi) and/or satellite internet, but Any updates, changes, deletions, or additions to software, firmware, data, or configuration settings, or any combination thereof, delivered to OTA clients using proprietary and/or standardized wireless communications technology, without limitation. Means.

The term “edge node” means one or more edge devices coupled to a network capable of communicating with other edge nodes and cloud-based computing platforms to provide a portal to communicate with AV and to schedule OTA updates and deliver them to OTA clients. .

The term “edge device” refers to a device that implements an edge node and provides a physical wireless access point (AP) to an enterprise or service provider (eg, VERIZON, AT&T) core network. Examples of edge devices include, but are not limited to, computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area network (MAN) and wide area network (WAN) access devices.

“More than one” means that the function is performed by one element, the function is performed by more than one element, eg, in a distributed manner, that several functions are performed by one element, and several It includes the function being performed by several elements, or any combination thereof.

In addition, while the terms first, second, etc. have been used herein to describe various elements, in some instances, it will be understood that such elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly a second contact may be referred to as a first contact, without departing from the scope of the various embodiments described. Both the first contact and the second contact are contacts, but not the same contact.

The terms used in the description of the various embodiments described herein are for describing specific embodiments only, and are not intended to be limiting. As used in the description of the various embodiments described and the appended claims, the singular is intended to include the plural as well, unless the context clearly indicates otherwise. In addition, it will be understood that the term “and/or” as used herein refers to and includes any and all possible combinations of one or more of the listed related items. In addition, the terms “comprising” and/or “comprising”, when used in this description, specify the presence of the recited feature, integer, step, action, element, and/or component, but one or more other features, integers. It will also be understood that the presence or addition of, steps, actions, elements, components, and/or groups thereof are not excluded.

As used herein, the term “if” is optionally interpreted to mean “when…” or “when” or “in response to a decision” or “in response to detection” depending on the context. . Likewise, the phrase "if determined" or "if [the stated condition or event] is detected" optionally means "on a decision" or "in response to a decision" or "[the stated condition or event], depending on the context. ] On the detection of" or "in response to detection of [the mentioned condition or event]" is interpreted as meaning.

As used herein, an AV system refers to an AV with an array of hardware, software, stored data, and data generated in real time that support the operation of the AV. In one embodiment, the AV system is contained within the AV. In one embodiment, AV systems are spread across multiple locations. For example, some of the software of the AV system is implemented on a cloud computing environment similar to the cloud computing environment 300 described below with respect to FIG. 3.

In general, the present application relates to any vehicle having one or more autonomous driving capabilities, including fully autonomous vehicles, highly autonomous vehicles, and conditional autonomous vehicles such as so-called level 5 vehicles, level 4 vehicles and level 3 vehicles, respectively. (Details on the classification of vehicle autonomy levels are included in their entirety by reference, SAE International Standard J3016: Taxonomy and Definitions of Terms for On-Road Vehicle Automated Driving Systems. for Terms Related to On-128-172020-02-28 Road Motor Vehicle Automated Driving Systems). In addition, the technology disclosed herein is also applicable to partially autonomous vehicles and driver assistance vehicles, for example, so-called level 2 and level 1 vehicles (SAE International Standard J3016: Classification and definition of terms relating to on-road vehicle automatic driving systems. Reference). In one embodiment, one or more of the Level 1, Level 2, Level 3, Level 4, and Level 5 vehicle systems are based on the processing of sensor inputs, under specific vehicle operation (e.g., steering, braking, And use of maps). The techniques disclosed herein can benefit vehicles at any level, from fully autonomous vehicles to human-driving vehicles.

Autonomous vehicles have advantages over vehicles that require a human driver. One advantage is safety. For example, in 2016, the U.S. experienced 6 million car accidents, 2.4 million injuries, 40,000 deaths, and 13 million vehicle crashes, estimated at an estimated social cost of $910 billion. The U.S. traffic fatalities per 100 million mile drive dropped from about six to one between 1965 and 2015, in part due to additional safety measures installed in the vehicle. For example, an additional 0.5-second warning that a collision will occur is believed to mitigate 60% of post-war collisions. However, passive safety features (e.g. seat belts, airbags) would have reached the limit in improving this number. Therefore, active safety measures, such as automatic control of the vehicle, are a promising next step in improving these statistics. Since human drivers are believed to be responsible for critical pre-collision events in 95% of collisions, automated driving systems can, for example, by recognizing and avoiding critical situations more reliably than humans; By making better decisions, complying with traffic laws, and predicting future events better than humans; And better safety results can be achieved by controlling the vehicle more reliably than humans.

Referring to FIG. 1, the AV system 120 avoids an object (for example, a natural obstacle 191, a vehicle 193, a pedestrian 192, a cyclist, and other obstacles) and avoids road regulations (for example, , Operating the AV 100 along the trajectory 198 through the environment 190 to the destination 199 (sometimes referred to as the final position), adhering to the operating rules or driving preferences.

In one embodiment, AV system 120 includes a device 101 that is equipped to receive operation commands from computer processor 146 and operate accordingly. In one embodiment, computing processor 146 is similar to processor 304 described below with reference to FIG. 3. Examples of device 101 include steering control 102, brake 103, gears, accelerator pedals or other acceleration control mechanisms, windshield wipers, side-door locks, window controls, and turn signals.

In one embodiment, the AV system 120 includes the AV 100 such as the position of the AV, the linear velocity and the angular velocity, the linear acceleration and the angular acceleration, and a heading (e.g., the orientation of the tip of the AV 100). It includes a sensor 121 for measuring or inferring the characteristics of the state or condition of. Examples of the sensor 121 include GPS, an inertial measurement unit (IMU) that measures both vehicle linear acceleration and angular rate, a wheel speed sensor to measure or estimate a wheel slip ratio, and a wheel brake. Pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular velocity sensors.

In one embodiment, the sensor 121 also includes a sensor for sensing or measuring the characteristics of the environment of the AV. For example, visible, infrared or thermal (or both) spectral monocular or stereo video camera 122, LiDAR 123, RADAR, ultrasonic sensor, time-of-flight (TOF) depth sensor, speed sensor, temperature Sensor, humidity sensor, and rainfall sensor.

In one embodiment, the AV system 120 includes a data storage unit 142 and a memory 144 for storing data collected by the sensor 121 or machine instructions associated with the computer processor 146. In one embodiment, the data storage unit 142 is similar to the ROM 308 or storage device 310 described below with respect to FIG. 3. In one embodiment, memory 144 is similar to main memory 306 described below. In one embodiment, data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about environment 190. In one embodiment, the stored information includes a map, driving performance, traffic congestion updates or weather conditions. In one embodiment, data relating to the environment 190 is transmitted from a remotely located database 134 to the AV 100 via a communication channel.

In one embodiment, the AV system 120 provides different vehicle conditions and conditions, e.g., position, linear velocity and angular velocity, linear acceleration and angular acceleration, and measured or inferred characteristics of linear and angular headings towards AV 100. And a communication device 140 for communicating. The device is capable of wireless communication over vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication devices and point-to-point or ad hoc networks, or both. Includes a device for. In one embodiment, communication device 140 communicates over the electromagnetic spectrum (including radio and optical communications) or other media (eg, air and acoustic media). The combination of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) communication (and one or more other types of communication in some embodiments) is sometimes referred to as Vehicle-to-Everything (V2X) communication. V2X communication typically conforms to one or more communication standards for communication with and between autonomous vehicles.

In one embodiment, communication device 140 includes a communication interface. For example, wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interfaces. The communication interface transmits data from a remotely located database 134 to the AV system 120. In one embodiment, a remotely located database 134 is embedded in a cloud computing environment 200 as described in FIG. 2. The communication interface 140 transmits data collected from the sensor 121 or other data related to the operation of the AV 100 to the remotely located database 134. In one embodiment, communication interface 140 transmits information related to teleoperation to AV 100. In some embodiments, AV 100 communicates with another remote (eg, “cloud”) server 136.

In one embodiment, the remotely located database 134 also stores and transmits digital data (eg, stores data such as road and street locations). Such data may be stored in memory 144 on AV 100 or transmitted to AV 100 via a communication channel from a database 134 located remotely.

In one embodiment, the remotely located database 134 retrieves historical information about the driving characteristics (e.g., speed and acceleration profiles) of the vehicle previously progressed along the trajectory 198 at a similar time of day. Save and send. In one implementation, such data may be stored in memory 144 on AV 100 or may be transmitted to AV 100 via a communication channel from a remotely located database 134.

Computing device 146 located on AV 100 algorithmically generates control actions based on both real-time sensor data and previous information, allowing AV system 120 to execute autonomous driving capabilities.

In one embodiment, AV system 120 is a computer peripheral coupled to computing device 146 to provide information and alerts to a user (e.g., occupant or remote user) of AV 100 and receive input therefrom. It includes (132). In one embodiment, peripherals 132 are similar to display 312, input device 314, and cursor controller 316 discussed below with reference to FIG. 3. The combination can be wireless or wired. Any two or more of the interface devices may be integrated into a single device.

2 illustrates an example "cloud" computing environment. Cloud computing is a service to enable easy on-demand network access to a shared pool of configurable computing resources (e.g., network, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services). It is a model of service delivery. In a typical cloud computing system, one or more large cloud data centers house machines used to deliver the services provided by the cloud. Referring now to FIG. 2, cloud computing environment 200 includes cloud data centers 204a, 204b, and 204c interconnected through cloud 202. Data centers 204a, 204b, and 204c provide cloud computing services to computer systems 206a, 206b, 206c, 206d, 206e, and 206f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud data centers. In general, a cloud data center, e.g., the cloud data center 204a shown in FIG. 2, is a cloud, e.g., the cloud 202 shown in FIG. 2, or a physical arrangement of servers that make up a specific part of the cloud. Refers to a sieve. For example, servers are physically arranged in rooms, groups, rows, and racks within a cloud data center. Cloud data centers have one or more zones containing one or more server rooms. Each room has one or more server rows, and each row contains one or more racks. Each rack contains one or more individual server nodes. In some implementations, servers in zones, rooms, racks, and/or rows are based on the physical infrastructure requirements of the data center facility, including power requirements, energy requirements, thermal requirements, heating requirements, and/or other requirements. Arranged in groups. In one embodiment, the server node is similar to the computer system described in FIG. 3. Data center 204a has multiple computing systems distributed across multiple racks.

Cloud 202 interconnects cloud data centers 204a, 204b, and 204c and helps to facilitate access of computing systems 206a to 206f to cloud computing services and network and networking resources (e.g., networking Equipment, nodes, routers, switches and networking cables) as well as cloud data centers 204a, 204b, and 204c. In one embodiment, a 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 transmitted using any number of network layer protocols such as Internet Protocol (IP), Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), and frame relay. Also, in embodiments where the network represents a combination of multiple sub-networks, a different network layer protocol is used in each of the underlying sub-networks. In some embodiments, a network represents one or more interconnected internetworks, such as the public Internet.

The computing systems 206a-206f or cloud computing service consumers are connected to the cloud 202 through network links and network adapters. In one embodiment, the computing systems 206a to 206f are various computing devices, for example, servers, desktops, laptops, tablets, smartphones, Internet of Things (IoT) devices, autonomous vehicles (cars, drones, shuttles, Including trains, buses, etc.) and consumer electronics. In one embodiment, computing systems 206a-206f are implemented in or as part of another system.

3 shows a computer system 300. In one implementation, computer system 300 is a special purpose computing device. A special-purpose computing device is a digital electronic device such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are hard-wired to perform a technology or are continuously programmed to perform a technology. Or one or more general-purpose hardware processors that are programmed to perform the technology according to program instructions in firmware, memory, other storage or combination. In addition, such special-purpose computing devices can combine custom fixed-wiring logic, ASICs, or FPGAs with custom programming to realize the technology. In various embodiments, the special-purpose computing device is a desktop computer system, portable computer system, portable device, network device, or any other device that includes fixed-wiring and/or program logic to implement the technology.

In one embodiment, computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with bus 302 for processing information. The hardware processor 304 is, for example, a general purpose microprocessor. Computer system 300 also includes main memory 306, such as a random access memory (RAM) or other dynamic storage device, for storing instructions and information to be executed by processor 304, coupled to bus 302. Includes. In one implementation, main memory 306 is used to store temporary variables or other intermediate information during execution of instructions to be executed by processor 304. Such instructions, when stored in a non-transitory storage medium accessible by processor 304, renders computer system 300 to a special-purpose machine customized to perform the operations specified in the instructions.

In one embodiment, computer system 300 further includes a read only memory (ROM) 308 or other static storage device coupled with bus 302 to store static information and instructions for processor 304 do. A storage device 310, such as a magnetic disk, optical disk, solid-state drive, or three-dimensional cross point memory, is provided and coupled to a bus 302 to store information and instructions.

In one embodiment, the computer system 300 via the bus 302, a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, and a light emitting diode (LED) display for displaying information to a computer user. , Or to a display 312 such as an OLED (organic light emitting diode) display. An input device 314 comprising alphanumeric and other keys is coupled to the bus 302 to communicate information and command selections to the processor 304. Another type of user input device is a cursor controller, such as a mouse, trackball, touch display, or cursor direction keys, for controlling cursor movement on the display 312 and communicating direction information and command selection to the processor 304. (316). This input device typically has two axes that allow the device to specify a position in a plane, i.e. in a first axis (e.g., x-axis) and a second axis (e.g., y-axis). It has 2 degrees of freedom.

According to one embodiment, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. Such instructions are read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequence of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In an alternative embodiment, fixed-wiring circuitry is used instead of or in combination with software instructions.

The term “storage medium” as used herein refers to any non-transitory medium that stores data and/or instructions that cause a machine to operate in a particular manner. Such storage media include non-volatile media and/or volatile media. Non-volatile media include, for example, an optical disk, a magnetic disk, a solid-state drive, or a three-dimensional cross point memory, such as a storage device 310. Volatile media include dynamic memory, such as main memory 306. Common types of storage media are, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tapes, or any other magnetic data storage medium, CD-ROM, any other optical data storage medium, hole Any physical medium having a pattern, RAM, PROM, and EPROM, FLASH-EPROM, NV-RAM, or any other memory chip, or cartridge.

The storage medium is separate from the transmission medium, but can be used with the transmission medium. Transmission media participates in transferring information between storage media. For example, transmission media include coaxial cables, copper wires, and optical fibers, including wires including bus 302. Further, the transmission medium may take the form of a light wave or an acoustic wave, such as that generated during radio wave and infrared data communication.

In one embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to processor 304 for execution. For example, the instructions are initially held on a magnetic disk or solid-state drive of a remote computer. The remote computer loads the command into dynamic memory and sends the command over the phone line using a modem. A modem local to the computer system 300 receives data over a telephone line and uses an infrared transmitter to convert the data into an infrared signal. The infrared detector receives the data carried in the infrared signal and the appropriate circuitry places the data on the bus 302. Bus 302 carries data to main memory 306, and processor 304 retrieves and executes instructions from main memory. Instructions received by main memory 306 may optionally be stored in storage device 310 before or after being executed by processor 304.

Computer system 300 also includes a communication interface 318 coupled with bus 302. Communication interface 318 provides a two-way data communication coupling for network link 320 connected to local network 322. For example, the communication interface 318 is an integrated service digital network (ISDN) card, a cable modem, a satellite modem, or a modem for providing a data communication connection to a corresponding type of telephone line. As another example, communication interface 318 is a LAN card for providing a data communication connection to a compatible local area network (LAN). In some implementations, a radio link is also implemented. In any such implementation, communication interface 318 transmits and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

Network link 320 typically provides data communication to other data devices over one or more networks. For example, the network link 320 may provide a connection to the host computer 324 through the local network 322 or to a cloud data center or equipment operated by an Internet Service Provider (ISP) 326. have. ISP 326 in turn provides data communication services through a world-wide packet data communication network, now collectively referred to as "Internet 328". Both the local network 322 and the Internet 328 use electrical, electromagnetic, or optical signals to carry digital data streams. Signals over various networks and signals on network link 320 through communication interface 318 that carry digital data to and from computer system 300 are exemplary forms of transmission media. In one embodiment, network 320 includes cloud 202 or a portion of cloud 202 described above.

Computer system 300 transmits and receives messages and data including program code via network(s), network link 320 and communication interface 318. In one embodiment, computer system 300 receives code for processing. The received code is executed by processor 304 when received and/or stored in storage device 310 or other non-volatile storage for later execution.

Autonomous vehicle architecture

FIG. 4 shows an exemplary architecture 400 for an autonomous vehicle (eg, AV 100 shown in FIG. 1 ). Architecture 400 includes a cognitive module 402 (sometimes referred to as a cognitive circuit), a planning module 404 (sometimes referred to as a planning circuit), and a control module 406 (sometimes referred to as a control circuit). , A localization module 408 (sometimes referred to as a localization circuit), and a database module 410 (sometimes referred to as a database circuit). Each module plays a predetermined role in the operation of the AV 100. Together, modules 402, 404, 406, 408 and 410 may be part of the AV system 120 shown in FIG. 1. In some embodiments, any of the modules 402, 404, 406, 408, and 410 are computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one These microprocessors, microcontrollers, application-specific integrated circuits (ASICs), hardware memory devices, different types of integrated circuits, different types of computer hardware, or a combination of any or all of these). Each module of modules 402, 404, 406, 408, and 410 is sometimes referred to as a processing circuit (eg, computer hardware, computer software, or a combination of the two). Any or a combination of all of the modules 402, 404, 406, 408, and 410 is also an example of a processing circuit.

In use, the planning module 404 receives data indicative of the destination 412 and the trajectory 414 (sometimes with which the AV 100 can proceed to reach the destination 412 (e.g., to arrive)). (Referred to as a route). For planning module 404 to determine the data representing trajectory 414, planning module 404 receives data from recognition module 402, localization module 408, and database module 410.

The recognition module 402 identifies a nearby physical object using one or more sensors 121, for example, as also shown in FIG. 1. Objects are classified (eg, grouped into types such as pedestrians, bicycles, automobiles, traffic signs, etc.), and a scene description comprising the classified objects 416 is provided to the planning module 404.

In addition, the planning module 404 receives data representing the AV location 418 from the localization module 408. Localization module 408 uses data from sensor 121 and data from database module 410 (eg, geographic data) to calculate the location to determine the AV location. For example, the localization module 408 calculates the longitude and latitude of the AV using data and geographic data from Global Operation Satellite System (GNSS) sensors. In one embodiment, the data used by localization module 408 is a high-precision map of road geometry, a map describing road network connection properties, road physical properties (e.g., traffic speed, traffic volume, vehicle and cyclist traffic Maps describing the number of lanes, lane widths, lane traffic directions, or lane marker types and locations, or combinations thereof), and road features, such as crosswalks, traffic signs, or other types of travel. signal) contains a map describing its spatial location. In one embodiment, the high-precision map is constructed by adding data to the low-precision map through automatic or manual annotation.

The control module 406 receives data representing the trajectory 414 and data representing the AV location 418, and causes the AV 100 to advance the trajectory 414 toward the destination 412. Control functions 420a to 420c (eg, steering, throttling, braking, and ignition) are operated. For example, when the trajectory 414 includes a left turn, the control module 406, the steering angle of the steering function causes the AV 100 to rotate to the left, and throttling and braking cause the AV 100 to Before the rotation is made, the control functions 420a to 420c are operated in a manner that pauses and waits for a pedestrian or vehicle passing through.

Autonomous vehicle input

5 shows inputs 502a to 502d (e.g., sensors 121 shown in FIG. 1) and outputs 504a to 504d (e.g., sensors) used by the cognitive module 402 (FIG. 4). Data). One input 502a is a Light Detection and Ranging (LiDAR) system (eg, LiDAR 123 shown in FIG. 1 ). LiDAR is a technique that uses light (eg, a burst of light such as infrared light) to obtain data about a physical object in his gaze. The LiDAR system generates LiDAR data as output 504a. For example, LiDAR data is a collection of 3D or 2D points (also known as point clouds) used to construct a representation of the environment 190.

Another input 502b is the RADAR system. RADAR is a technology that uses radio waves to acquire data about nearby physical objects. RADAR can acquire data about an object that is not within the line of sight of the LiDAR system. RADAR system 502b generates RADAR data as output 504b. For example, RADAR data are one or more radio frequency electromagnetic signals used to construct a representation of the environment 190.

Another input 502c is a camera system. The camera system uses one or more cameras (eg, a digital camera using an optical sensor such as a charge-coupled device (CCD)) to obtain information about a nearby physical object. The camera system generates camera data as output 504c. Camera data often takes the form of image data (eg, data in an image data format such as RAW, JPEG, PNG, etc.). In some examples, the camera system has a number of independent cameras, eg, for stereopsis (stereo vision), which allow the camera system to perceive depth of field. The object perceived by the camera system is described herein as "nearby", but this is relative to the AV. In use, the camera system may be configured to “see” an object distant, eg, up to 1 kilometer or more in front of the AV. Thus, the camera system can have features such as sensors and lenses that are optimized to recognize objects that are far away.

Another input 502d is a traffic light detection (TLD) system. The TLD system uses one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual driving information. The TLD system generates TLD data as output 504d. TLD data often takes the form of image data (eg, data in an image data format such as RAW, JPEG, PNG, etc.). The TLD system uses a camera with a wide field of view (e.g., using a wide-angle lens or a fisheye lens) in order to obtain information on as many physical objects as possible to provide visual driving information. It differs from a system that includes a camera in that) accesses all relevant driving information provided by this object. For example, the viewing angle of a TLD system can be about 120 degrees or more.

In some embodiments, outputs 504a-504d are combined using a sensor fusion technique. Thus, either of the individual outputs 504a to 504d is provided to another system of the AV 100 (e.g., provided to the planning module 404 as shown in Fig. 4), or a combined output is The form of a single combined output or multiple combined outputs of the same type (using the same combination technique or combining the same outputs or both) or different types (e.g., using different respective combination techniques or different It may be provided to other systems in either the form of a single combined output or multiple combined outputs (either combining the respective outputs or both). In some embodiments, early fusion techniques are used. Early fusion techniques are characterized in that the outputs are combined before one or more data processing steps are applied to the combined outputs. In some embodiments, a late fusion technique is used. Late fusion techniques are characterized by combining the outputs after one or more data processing steps have been applied to the individual outputs.

6 shows an example of a LiDAR system 602 (eg, input 502a shown in FIG. 5). LiDAR system 602 emits light 604a-604c from light emitter 606 (eg, laser transmitter). The light emitted by the LiDAR system is typically not in the visible spectrum, for example infrared light is often used. Some of the emitted light 604b encounters a physical object 608 (eg, a vehicle) and is reflected back to the LiDAR system 602. (Light emitted from a LiDAR system typically does not penetrate a physical object, e.g., a physical object in solid form). In addition, the LiDAR system 602 has one or more photo detectors 610 that detect reflected light. In one embodiment, one or more data processing systems associated with the LiDAR system generate an image 612 representing the field of view 614 of the LiDAR system. The image 612 includes information representing the boundary 616 of the physical object 608. In this way, the image 612 is used to determine the boundary 616 of one or more physical objects near the AV.

7 shows the LiDAR system 602 in operation. In the scenario shown in this figure, AV 100 receives both a camera system output 504c in the form of an image 702 and a LiDAR system output 504a in the form of a LiDAR data point 704. In use, the data processing system of AV 100 compares image 702 to data points 704. In particular, the physical object 706 identified in the image 702 is also identified among the data points 704. In this way, the AV 100 recognizes the boundary of the physical object based on the contour and density of the data points 704.

8 shows the operation of the LiDAR system 602 in additional detail. As described above, the AV 100 detects the boundary of the physical object based on the characteristics of the data points detected by the LiDAR system 602. As shown in FIG. 8, a flat object such as ground 802 will reflect light 804a-804d emitted from LiDAR system 602 in a consistent manner. In other words, because LiDAR system 602 emits light using consistent spacing, ground 802 will reflect light back to LiDAR system 602 at the same consistent spacing. As AV 100 advances above ground 802, LiDAR system 602 will continue to detect the light reflected by the next effective ground point 806 if there is nothing obstructing the roadway. However, if the object 808 obstructs the roadway, the light 804e-804f emitted by the LiDAR system 602 will be reflected from the points 810a-810b in a manner that is inconsistent with the expected consistent manner. From this information, the AV 100 may determine that the object 808 exists.

Route planning

9 shows a block diagram 900 of the relationship between the inputs and outputs of the planning module 404 (eg, as shown in FIG. 4). In general, the output of the planning module 404 is the route 902 from the start point 904 (e.g., source location or initial location) to the end point 906 (e.g., destination or final location). . Route 902 is typically defined by one or more segments. For example, a segment is a distance that runs over at least a portion of a street, road, highway, private road, or other physical area suitable for driving a vehicle. In some examples, for example, when the AV 100 is a vehicle capable of off-road driving such as a four-wheel drive (4WD) or all-wheel-drive (AWD) car, SUV, pickup truck, etc., Route 902 includes “off-road” segments such as unpaved paths or open fields.

In addition to route 902, the planning module also outputs lane-level route planning data 908. Lane-level route planning data 908 is used to traverse segments of route 902 based on the condition of the segment at a particular time. For example, if the route 902 includes a multi-lane highway, the lane-level route planning data 908 can be, for example, whether an exit is approaching, whether one or more of the lanes have different vehicles, or Based on other factors that vary over a period of minutes or less, the AV 100 includes trajectory planning data 910 that can be used to select one of the multiple lanes. Similarly, in some implementations, lane-level route planning data 908 includes speed constraints 912 that are specific to segments of route 902. For example, if the segment contains a pedestrian or an unexpected traffic situation, the speed constraint 912 makes the AV 100 a traveling speed slower than the expected speed, e.g., speed limit data for the segment. Can be limited to a speed based on.

In one embodiment, the input to the planning module 404 is the database data 914 (e.g., from the database module 410 shown in Figure 4), the current location data 916 (e.g., Figure 4 AV location 418 shown in), destination data 918 (for example, for the destination 412 shown in FIG. 4), and object data 920 (for example, the recognition shown in FIG. 4 ). It includes a classified object 416 recognized by module 402. In one embodiment, database data 914 includes rules used in planning. Rules are specified using formal language, for example using boolean logic. In any given situation of encountering AV 100, at least some of the rules will apply to that situation. If the rule has a condition that is satisfied based on information available to the AV 100, for example, information about the surrounding environment, the rule is applied to the given situation. Rules can have priority. For example, a rule that reads "If the road is a highway, go to the leftmost lane" would have a lower priority than if the exit is approaching within a mile, go to the rightmost lane. I can.

FIG. 10 shows a directional graph 1000 used for route planning by, for example, planning module 404 (FIG. 4). In general, a direction graph 1000 such as that shown in FIG. 10 is used to determine a path between any start point 1002 and end point 1004. In the real world, the distance separating the start point 1002 and the end point 1004 can be relatively large (e.g., within two different metropolitan areas) or can be relatively small (e.g., city blocks and Two adjacent intersections or two lanes of a multi-lane road).

In one embodiment, the direction graph 1000 has nodes 1006a-1006d representing different locations between a start point 1002 and an end point 1004 that may be occupied by AV 100. In some examples, for example, when start point 1002 and end point 1004 represent different metropolitan areas, nodes 1006a-1006d represent segments of a road. In some examples, for example, when start point 1002 and end point 1004 represent different locations on the same road, nodes 1006a-1006d represent different locations on that road. In this way, the direction graph 1000 contains information at various levels of granularity. Further, in one embodiment, the direction graph with a higher particle size is also a subgraph of another direction graph with a larger scale. For example, a directional graph in which the start point 1002 and end point 1004 are far apart (e.g., many miles away) is a graph of which most of its information is of low granularity and is based on stored data, but AV It also contains some high-granularity information for the portion of the graph representing the physical location within the clock of 100.

The nodes 1006a to 1006d are separate from the objects 1008a to 1008b that cannot overlap with the node. In one embodiment, when the particle size is low, the objects 1008a to 1008b represent areas that cannot be traversed by a vehicle, for example, a street or an area without roads. When the granularity is high, the objects 1008a to 1008b represent a physical object within the view of the AV 100, for example, another car, a pedestrian, or another entity in which the AV 100 cannot share a physical space. In one embodiment, some or all of the objects 1008a to 1008b are static objects (e.g., objects that do not change their position such as street lights or telephone poles) or dynamic objects (e.g., positions such as pedestrians or other vehicles). Is an object that can change.

Nodes 1006a-1006d are connected by edges 1010a-1010c. When the two nodes 1006a to 1006b are connected by the edge 1010a, the AV 100 does not need to proceed to the intermediate node before arriving at the other node 1006b, for example, one node 1006a. ) And another node 1006b. (When referring to the AV 100 running between nodes, it means that the AV 100 goes between two physical locations represented by respective nodes.) Edges 1010a to 1010c are defined as AV( 100) is often bidirectional in the sense that it proceeds from the first node to the second node, or from the second node to the first node. In one embodiment, the edges 1010a to 1010c are unidirectional in the sense that the AV 100 can proceed from the first node to the second node, but the AV 100 cannot proceed from the second node to the first node. to be. Edges 1010a-1010c are unidirectional when representing, for example, individual lanes of a one-way street, street, road, or highway, or other features that can only be traversed in one direction due to legal or physical constraints.

In one embodiment, the planning module 404 uses the direction graph 1000 to identify a path 1012 consisting of nodes and edges between the start point 1002 and the end point 1004.

Edges 1010a-1010c have an associated cost 1014a-1014b. Costs 1014a to 1014b are values representing resources to be consumed when the AV 100 selects a corresponding edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice the physical distance of the other edge 1010b, the associated cost 1014a of the first edge 1010a is the associated cost of the second edge 1010b ( 1014b) may be twice. Other factors influencing time include expected traffic conditions, number of intersections, and speed limits. Another typical resource is fuel economy. The two edges 1010a and 1010b may represent the same physical distance, but due to, for example, road conditions, expected weather, etc., one edge 1010a may require more fuel than the other edge 1010b. have.

When the planning module 404 identifies the path 1012 between the starting point 1002 and the ending point 1004, the planning module 404 is typically a cost-optimized path, e.g., individual edges of the edge. When the costs are added together, the path with the lowest total cost is chosen.

Autonomous vehicle control

11 shows a block diagram 1100 of the inputs and outputs of the control module 406 (eg, as shown in FIG. 4 ). The control module may be, for example, one or more processors similar to processor 304 (e.g., one or more computer processors such as microprocessors or microcontrollers or both), short and/or long term similar to main memory 306 A controller 1102 comprising data storage (e.g., memory random-access memory or flash memory or both), ROM 1308, and storage device 210, and the instructions stored in the memory, the The instructions perform the operation of the controller 1102 when the instructions are executed (eg, by one or more processors).

In one embodiment, the controller 1102 receives data indicative of the desired output 1104. The desired output 1104 typically includes speed, eg, speed and heading. The desired output 1104 may be based on data received from the planning module 404 (eg, as shown in FIG. 4 ), for example. Depending on the desired output 1104, the controller 1102 generates data that can be used as throttle input 1106 and steering input 1108. Throttle input 1106 represents the degree to which it is involved in the throttle (eg, acceleration control) of AV 100 by engaging the steering pedal or other throttle control to achieve the desired output 1104. In some examples, throttle input 1106 also includes data usable to engage the brake (eg, deceleration control) of AV 100. The steering input 1108 is a steering angle, e.g., the angle at which the steering control of the AV (e.g., a steering wheel, a steering angle actuator, or other functionality for controlling the steering angle) must be positioned to achieve the desired output 1104. Represents.

In one embodiment, the controller 1102 receives feedback used to adjust the throttle and input provided to steering. For example, if the AV 100 encounters an obstruction 1110 such as a hill, the measured speed 1112 of the AV 100 drops below the desired output speed. In one embodiment, any measured output 1114 is provided to the controller 1102 so that the necessary adjustments are made based on the difference 1113 between the measured speed and the desired output, for example. Measured output 1114 includes measured position 1116, measured velocity 1118 (including speed and heading), measured acceleration 1120, and other outputs measurable by the sensors of AV 100. do.

In one embodiment, information about the obstruction 1110 is pre-detected by a sensor, such as a camera or a LiDAR sensor, for example, and provided to the predictive feedback module 1122. The predictive feedback module 1122 then provides the information to the controller 1102, which the controller 1102 can use and adjust accordingly. For example, if a sensor in AV 100 has detected a hill ("bone"), this information can be used by controller 1102 to prepare to engage the throttle at an appropriate time to prevent significant deceleration. .

12 shows a block diagram 1200 of inputs, outputs, and components of controller 1102. The controller 1102 has a speed profiler 1202 that influences the operation of the throttle/brake controller 1204. For example, speed profiler 1202 may engage in acceleration using throttle/brake 1206, for example, depending on feedback received by controller 1102 and processed by speed profiler 1202 Command throttle/brake controller 1204 to engage in deceleration.

In addition, the controller 1102 has a lateral tracking controller 1208 that affects the operation of the steering controller 1210. For example, the lateral tracking controller 1208 steers to adjust the position of the steering angle actuator 1212 according to feedback received by the controller 1102 and processed by the lateral tracking controller 1208, for example. Command the controller 1204.

Controller 1102 receives several inputs used to determine how to control throttle/brake 1206 and steering angle actuator 1212. The planning module 404 is, for example, by the controller 1102 to select a heading when the AV 100 starts operating and to determine which road segment to traverse when the AV 100 reaches an intersection. Provides the information to be used. Localization module 408 allows controller 1102 to determine if AV 100 is in an expected position based on, for example, how throttle/brake 1206 and steering angle actuator 1212 are being controlled. Thus, information describing the current location of the AV 100 is provided to the controller 1102. In one embodiment, controller 1102 receives information from other inputs 1214, for example, information received from a database, computer network, or the like.

Steering angle control using feed forward signal

13 shows a schematic diagram of a controller 1102 connected to a steering wheel 1310 of a vehicle. The controller 1102 may be connected to or be a component of the vehicle's EPS system. The steering control signal applied by the controller 1102 may be transmitted to the steering wheel 1310 through the steering gear 1302, the intermediate shaft 1304, the steering column 1306 and the steering column assembly 1308. In response to this, the steering wheel 1308 may rotate by a steering angle corresponding to the strength of the steering control signal. In one embodiment, the vehicle does not include a steering wheel 1308.

14 is a flowchart of an exemplary process 1400 for applying compensation torque to a steering column. Certain aspects of process 1400 may be implemented by planning module 404, controller 1102, or a combination thereof. At 1402, a base torque signal is transmitted to the controller. For example, the planning module 404 sends a base torque signal to the controller 1102. The base torque signal represents the base torque that the controller 1102 needs to apply to the steering column of the vehicle to control the operation of the vehicle in a manner determined by the planning module 404. In some embodiments, based on the vehicle's trajectory, the planning module 404 determines the steering angle at which the steering wheel needs to be rotated to continue the trajectory. The planning module 404 determines the base torque to be applied to the steering column to rotate the steering wheel at the determined steering angle. The planning module 404 determines a base torque signal representing the base torque. The planning module 404 generates and transmits the base torque signal to the controller 1102 along with a command to transmit the base torque signal to the steering column. Alternatively, the planning module 404 sends a command to the controller 1102, and the controller 1102 then generates a base torque signal and sends it to the steering column.

In some implementations, the torque signal can be a voltage signal. 15 is an example of a voltage signal that can be applied to the EPS system. For example, the controller 1102 may include a digital to analog converter (DAC) 1502. The DAC 1502 may output a voltage signal in a voltage range, for example, 0.0 V to 1.0 V. _{The vehicle's EPS system may output a voltage V EPS in a} range different from (eg, higher) than the voltage range output by the DAC 1502. For example, the EPS system can accommodate a voltage in the range of 2.0 V to 3.0 V, which is the voltage output by the steering column torque sensor. The voltage from DAC 1502 is equal to a voltage of 2 V (V _int ) (that is, the difference between the DAC 1502 voltage and the voltage acceptable to the EPS system _{) to reach the voltage (V t) corresponding to the torque signal} Can be augmented. In some embodiments, the averaging mechanism is implemented to increase the voltage from DAC 1502, eg, a -2.5V voltage averaging mechanism.

Ideally, the base torque applied to the steering column will rotate the steering wheel by the expected steering angle determined by the planning module 404. However, under certain vehicle conditions (eg, vehicle speed, vehicle load, progress state, or similar vehicle conditions), the actual steering angle at which the steering wheel rotates deviates from the expected steering angle. Such deviations can be observed, for example, at speeds in the range of 7 m/s to 11 m/s. In some embodiments, the controller 1102 may implement a control loop, eg, a proportional-integral-derivative (PID) control loop, to modify the torque signal based on the actual steering angle.

In some embodiments, the planning module 404 determines that, based on vehicle conditions, for example, the base torque applied to the steering column will result in an actual steering angle deviating from the expected steering angle determined for that base torque. . Thus, the planning module 404 compensates for the deviation by applying a compensation torque as a feedforward signal to the control loop, as described below.

16 is a schematic diagram of a control loop with a feedforward function. The control loop may be implemented by the controller 1102 or the planning module 404. The control loop is connected to an EPS system 1602 that converts the input torque signal into an output torque to be applied to the steering column. Voltage module 1606 includes a DAC 1502 and a voltage boosting mechanism described with reference to FIG. 15. The feedforward function module 1608 applies a feedforward signal as described below. The regulation module 1604 represents the reactive term of the PID control loop which is modified based on the actual steering angle. The adjustment module 1604 adjusts the error, ie the deviation between the actual steering angle and the expected steering angle, to zero.

The "Reference Trajectory" represents the signal from the planning module 404, and the controller 1102 controls the steering wheel accordingly. Based on the "reference trajectory", the voltage module 1606 generates a voltage representing a torque signal and transmits the torque signal to the EPS system 1602. EPS system 1602 applies torque. The actual steering angle of the steering wheel is provided to the controller 1102 or planning module 404 as feedback by the EPS system 1602. Based on the feedback, the planning module 404 or the controller 1102 operates the voltage module 1606 to adjust the voltage (e.g., to increase or decrease the voltage) so that the actual steering angle matches the expected steering angle. I can make it.

In some embodiments, planning module 404 or controller 1102 may operate feedforward function module 1608 to apply a feedforward signal to voltage module 1606 without feedback from EPS system 1602. . Returning to Fig. 14, at 1404, a compensation torque to be applied to the steering column is determined. At 1406, a compensation torque signal (eg, a voltage signal) indicative of the compensation torque is applied as a feedforward signal. The experimental technique implemented by the planning module 404 to determine the compensation torque signal is described later. At 1408, the base torque signal is modified according to the compensation torque signal. For example, the base torque signal output by the voltage module 1606 is amplified by the compensation torque signal. That is, the magnitude of the base torque signal is increased or decreased by the magnitude of the compensation torque signal. At 1410 a modified torque signal is applied to the steering column. For example, the voltage module 1606 transmits the modified torque signal to the EPS system 402, and the EPS system 402 applies the increased torque signal to the steering column, causing the steering wheel to rotate at the expected steering angle. do. At 1412, the vehicle is operated based on the modified steering angle. Planning module 404 or controller 1102 (or both) may implement the aforementioned steps of process 1400 for the duration of the vehicle's operation.

In order to apply the compensation torque to the base torque as a feedforward signal, the planning module 404 or the controller 1102 pre-generates and stores a plurality of compensation torque signals, each representing the compensation torque to be applied based on the vehicle condition. The vehicle conditions may include, for example, different speeds of the vehicle, a weather condition in which the vehicle is operating, a condition of a road in which the vehicle is operating, a load on the vehicle, or a combination thereof. The cargo on the vehicle may include a passenger profile, ie a description of the passengers known to board the vehicle.

At 1414, a number of compensating torque signals are generated. To do so, in some embodiments, the following process steps may be implemented. In some embodiments, the planning module 404 may assign a quantitative value to each vehicle condition and operate the vehicle under the corresponding vehicle condition. The planning module 404 may determine a compensation torque signal for each vehicle condition. At 1416, an expected steering angle for the respective torque signal is determined. For example, the planning module 404 can determine an expected steering angle for the base torque signal. In some embodiments, the planning module 404 may provide the expected steering angle by curve fitting a secondary polynomial surface that relates the vehicle condition (e.g., speed) and steering angle to the feedforward torque. To decide. 17 is a plot 1700 relating vehicle condition and steering angle to feedforward torque. In some embodiments, the planning module 404 determines the expected steering angle by measuring the steering angle of the steering wheel at a constant speed.

At 1418, a torque signal may be applied to the steering column. For example, the planning module 404 may apply a base torque signal to the controller 1102 as described above. At 1420, the vehicle may be operated under a predetermined vehicle condition. For example, the planning module 404 may operate the vehicle at each vehicle condition, as described above. At 1422, the actual steering angle is measured. For example, the EPS system 1602 may measure an actual steering angle at which the steering wheel rotates in response to a base torque signal received from the planning module 404. At 1424, the compensation torque signal required to correct the actual steering angle is determined. For example, the planning module 404 may determine a compensation torque signal to be applied to the base torque signal to eliminate a deviation between an expected steering angle and an actual steering angle. At 1426, a compensation torque signal, an actual steering angle and a predetermined vehicle condition may be stored. For example, the planning module 404 may generate a data set including a compensation torque signal, a base torque signal, and a quantitative value assigned to the vehicle condition.

The planning module 404 may implement process steps 1416-1426 several times in different vehicle conditions and generate multiple data sets. The data set may include one vehicle condition (eg, vehicle speed) or multiple vehicle conditions (eg, vehicle speed on a rainy day, vehicle speed on unpaved roads, etc.). In this way, the planning module 404 can implement an experimental technique for generating and storing a compensation torque signal to be applied as a feedforward signal. During vehicle operation, the planning module 404 may detect the vehicle operating condition and, among the stored data sets, identify a data set that substantially matches the vehicle operating condition. From that data set, the planning module 404 can identify the compensation torque signal and transmit the compensation torque signal as a feedforward signal. In some embodiments, the planning module 404 may apply the compensation torque signal in real time. Real-time means that the time between the planning module 404 deciding to apply the feedforward signal and identifying and transmitting the feedforward signal is negligible (e.g., on the order of a few nanoseconds, a few microseconds, or a few milliseconds). ).

For example, the planning module 404 may apply a compensation torque signal based on a weather condition in which the vehicle is operated. Weather conditions may include, for example, rain, snow, strong winds, other weather conditions, or combinations thereof. The planning module 404 is based on the information detected by the sensor mounted on the AV 100, based on the geographical location and time at which the AV 100 is driving, from a remote location, or a combination thereof Can be determined. As the speed of the AV 100 increases, when the AV 100 is operating under such extreme weather conditions, the deviation between the estimated steering angle and the actual steering angle is greater than when the AV 100 is operating under normal weather conditions. It can be big. In some embodiments, the planning module 404 determines a change in weather conditions and in response (e.g., in real time) generates a feedforward compensation torque signal to reduce or eliminate the deviation between the estimated steering angle and the actual steering angle. Can be approved.

In another example, the planning module 404 may apply a compensation torque signal based on a road condition in which the vehicle is being driven. Road conditions may include, for example, wet roads, slippery roads, unpaved roads, damaged roads or other road conditions. The planning module 404 determines the road condition from a remote location, or a combination thereof, based on the information detected by the sensor installed in the AV 100, based on the geographic location in which the AV 100 is driving. I can. As the speed of the AV 100 increases, when the AV 100 is driving under such extreme road conditions, the deviation between the estimated steering angle and the actual steering angle is greater than when the AV 100 is driving under normal road conditions. It can be big. In some embodiments, the planning module 404 determines a change in road conditions and in response (e.g., in real time) generates a feedforward compensation torque signal to reduce or eliminate the deviation between the estimated steering angle and the actual steering angle. Can be approved.

In another example, the planning module 404 may apply a compensation torque signal based on the load on the vehicle. The load may be greater than the normal load, for example due to increased occupancy or due to heavy goods being transported by the vehicle. The planning module 404 may determine the load from a remote location, or a combination thereof, based on information detected by a sensor mounted on the AV 100. As the speed of the AV 100 increases, when the AV 100 is operating under a high load, a deviation between the estimated steering angle and the actual steering angle may be greater than when the AV 100 is operating under a normal load. In some embodiments, the planning module 404 determines a change in load and in response (e.g., in real time) applies a feedforward compensation torque signal to reduce or eliminate the deviation between the estimated steering angle and the actual steering angle. can do.

In another example, the planning module 404 may apply a compensation torque signal based on the passenger profile of one or more passengers of the AV 100. The planning module 404 may store general driving preferences applicable to any passenger of the AV 100. General driving preferences can specify the driving preferences of passengers, such as taking the shortest route to the destination, avoiding highways or tolls, to name a few. Based on the general driving preferences, the planning module 404 may determine a set of vehicle driving conditions to be applied to meet the general driving preferences. Further, the planning module 404 may determine a compensation torque signal to be applied to the steering column for a set of vehicle driving conditions. Over time, the planning module 404 may modify general driving preferences based on each passenger using the AV 100. Subsequently, the planning module 404 identifies the passengers aboard the vehicle, identifies the passenger profile for that passenger, and the compensation torque to be applied to the steering column for a set of vehicle driving conditions that will meet the driving preferences of that passenger. The signal can be determined. The planning module 404 may apply the determined compensation torque signal in real time.

In one embodiment, the amount of the compensation torque signal to be applied to the steering column may be determined by utilizing a model trained for AV 100. The trained model can be created by applying machine learning techniques to past traversal data in which a previous compensation torque signal has been generated and applied. The trained model may also take into account time of day, traffic conditions, pedestrian density based on audiovisual or other data, and weather, among other factors. For example, a vehicle driving condition and a compensation torque signal applied to the vehicle driving condition may be generated and stored. The technique described here for determining the amount of the compensation torque signal can be applied to past traversal data. Machine learning techniques may be implemented to train the planning module 404 to determine the amount of compensating torque signals for any combination of vehicle conditions from past traversal data. The amount determined by machine learning can be improved using the amount determined in real time. Additionally, historical traversal data may be updated to improve machine learning techniques.

In some embodiments, process steps 1416-1426 may be implemented by a computer system remote to the vehicle, or may be implemented by a data set transmitted to a planning module 404 and a computer system remote to the vehicle. . In such a case, the compensation torque to be applied to the EPS system 1602 may be received by the planning module 404 from a remote computer system.

In some embodiments, the planning module 404 may compensate for a deviation between the expected steering angle and the actual steering angle up to a predetermined steering angle threshold, eg, 10° or some other angle. Deviations exceeding a predetermined threshold are indicative of a mechanical problem in the vehicle that requires repair at the workshop. In such a case, ie upon determining that the deviation exceeds a predetermined threshold, the planning module 404 may operate the vehicle to a target position, eg, a workshop or other target position. In some embodiments, the planning module 404 may implement operations other than operating the vehicle to a target position. Additional actions may include, for example, sending an alert to be displayed to a passenger in the vehicle, to a target location, to a different location, limiting the commands that can be sent to the EPS system 1602, or combinations thereof. I can.

In the foregoing description, embodiments of the invention have been described with reference to a number of specific details that may vary from implementation to implementation. Accordingly, the detailed description and drawings should be viewed from an exemplary point of view rather than a restrictive point of view. It is the only exclusive indication of the scope of the present invention, and that the applicant intends to be the scope of the present invention is the literary equivalent scope of the set of claims appearing in a specific form in this application, and the specific form in which such claim appears is subject to any subsequent amendments. Includes. Any definitions expressly recited herein for terms included in such claims determine the meaning of those terms used in the claims. Additionally, when the term “more including” is used in the foregoing description and in the claims below, what follows this phrase may be an additional step or entity, or a previously mentioned step or sub-step/sub-entity of the entity. .

Claims (16)

As a computer-implemented method,
Steering of the vehicle 100 by a planning circuit 404 mounted onboard of the vehicle 100 and connected to an electric power steering (EPS) system 1602 of the vehicle 100 Determining (1404) a compensatory torque signal to modify the actual steering angle of the wheel 1310 to match the expected steering angle of the steering wheel 1310;
Sending (1402) a base torque signal by means of the planning circuit and to a control circuit, causing the torque applied to the steering wheel;
Modifying the base torque signal according to the compensation torque signal (1408)
-At least one of the compensation torque signal and the base torque signal is a voltage;
Transmitting (1406), by the planning circuit, the compensation torque signal to the control circuit which controls the steering angle of the steering wheel;
Modifying the actual steering angle based on the compensation torque signal by the EPS system 1602 to cause a corrected steering angle (1408); And
Using the control circuit, operating the vehicle 100 based on the modified steering angle (1412)
Including, the method,
Generating (1414) a plurality of compensation torque signals-each generated compensation torque signal includes a respective torque signal to be transmitted to the EPS system 1602 to apply torque to the steering wheel 1310- Including, the step of generating the plurality of compensation torque signals (1414), for each generated compensation torque signal,
Determining (1416) an expected steering angle of the steering wheel (1310) for the respective torque signal;
Applying the respective torque signals to the EPS system 1602 to cause a steering angle (1418);
Operating the vehicle 100 at the steering angle at a predetermined speed (1420);
Determining an actual steering angle of the steering wheel 1310 for the respective torque signal (1422); And
Storing the generated compensation torque signal and the actual steering angle as a data pair (1426).
Characterized in that it comprises a, computer-implemented method. The method of claim 1, wherein modifying the base torque signal according to the compensation torque signal (1408) comprises adding a magnitude of the compensation torque signal to the magnitude of the base torque signal. , Way. The method according to claim 1 or 2, wherein the compensation torque signal is transmitted to the control circuit as a feedforward signal. 3. A polynomial surface (1416) according to claim 1 or 2, wherein determining the expected steering angle of the steering wheel (1310) for the respective torque signal (1416) relates the vehicle speed and steering angle to the applied torque ( The method comprising the step of curve fitting (curve fitting) a polynomial surface. The method according to claim 1 or 2,
Determining a difference between the expected steering angle and the actual steering angle; And
In response to determining that the difference exceeds a predetermined threshold, operating the vehicle 100 to a target position
The method further comprising. The method according to claim 1 or 2,
Determining, by the planning circuit, operating conditions of the vehicle (100); And
Modifying the compensation torque signal according to the operating conditions
The method further comprising. The method of claim 6, wherein the compensation torque signal is modified in real time according to the operating conditions. The method according to claim 6, wherein the operating conditions include weather conditions in which the vehicle (100) is operated. The method according to claim 6, wherein the operating conditions include road conditions in which the vehicle (100) is driven. 7. The method according to claim 6, wherein the operating conditions comprise a load carried by the vehicle (100). 7. The method according to claim 6, wherein the operating conditions comprise driving conditions stored by the planning circuit in a passenger profile comprising driving preferences of one or more passengers of the vehicle (100). The method of claim 6, further comprising: receiving, by the planning circuit, a command to modify the compensation torque signal from a remote location remote to the vehicle (100), the compensation torque signal receiving the command from the remote location Modified in response to-further comprising. As the vehicle 100,
Planning circuit 404;
Memory 144; And
At least one program stored in the memory, the at least one program comprising instructions executable by the planning circuit 404 to perform the method of claim 1 or 2-
Including, vehicle (100). A computer-readable storage medium containing at least one program for execution by the planning circuit 404 of the device, the at least one program being executed by the planning circuit 404, wherein the vehicle 100 is A computer-readable storage medium comprising instructions that cause the method of claim 1 or claim 2 to be performed. delete delete

Images