Patent Publication Number: US-2022227353-A1

Title: Road surface condition guided decision making and prediction

Description:
FIELD OF THE INVENTION 
     This description relates to road surface condition guided decision making and prediction. 
     BACKGROUND 
     Surfaces on which vehicles drive can vary along vehicle paths. For example, road surfaces along a vehicle path can include asphalt, concrete, rock, etc. These surfaces can also dynamically change under different conditions, such as weather conditions (e.g., rain, snow, sleet, etc.). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an example of an autonomous vehicle having autonomous capability. 
         FIG. 2  shows an example “cloud” computing environment. 
         FIG. 3  shows a computer system. 
         FIG. 4  shows an example architecture for an autonomous vehicle. 
         FIG. 5  shows an example of inputs and outputs that can be used by a perception module. 
         FIG. 6  shows an example of a LiDAR system. 
         FIG. 7  shows the LiDAR system in operation. 
         FIG. 8  shows the operation of the LiDAR system in additional detail. 
         FIG. 9  shows a block diagram of the relationships between inputs and outputs of a planning module. 
         FIG. 10  shows a directed graph used in path planning. 
         FIG. 11  shows a block diagram of the inputs and outputs of a control module. 
         FIG. 12  shows a block diagram of the inputs, outputs, and components of a controller. 
         FIG. 13A ,  FIG. 13B , and  FIG. 13C  show block diagrams of example systems for surface guided decision making. 
         FIG. 14  shows a flowchart of an example method. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present 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, 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. 
     Further, in the drawings, where connecting elements, such as solid or dashed lines or arrows, are used to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not shown in the drawings so as not to obscure the disclosure. In addition, for ease of illustration, a single connecting element is used to represent multiple connections, relationships, or associations between elements. For example, where a connecting element represents a communication of signals, data, or instructions, it should be understood by those skilled in the art that such element represents one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication. 
     Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the 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 so as not to unnecessarily obscure aspects of the 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. Surface Guided Decision Making       

     General Overview 
     Behavior of a vehicle is adapted based on dynamically changing road surfaces and conditions that impact safety and drivability. For example, sensor measurements are used to identify and categorize road surfaces. Based on the road surface category, the vehicle can determine the drivability properties of the surface, and can make appropriate planning decisions. Additionally, based on the drivability properties of the surface, the vehicle can predict the behavior of other vehicles that are driving on the surface and can proactively adjust its behavior accordingly. In this way, the vehicle can exhibit behaviors similar to that of a human driver in hazardous conditions, such as following existing tracks on the road when it is snowing or raining, avoiding ice patches, reducing speed, biasing within lanes, or changing lanes to avoid an obstacle on the road. 
     Adapting the behavior of a vehicle based on the dynamically changing road surfaces and conditions improves the safety and reliability of the vehicle, particularly when driving in hazardous environments. Additionally, recognizing that the behavior of other vehicles changes based on the dynamically changing road surfaces and conditions improves the accuracy of predicting the behavior of the other vehicles. This, in turn, reduces the chances of collisions and improves vehicle reliability and safety. 
     System Overview 
       FIG. 1  shows an example of an autonomous vehicle  100  having 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. A lane is sometimes identified based on lane markings. For example, a lane 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 or, e.g., natural obstructions to be avoided in an undeveloped area. A lane could also be interpreted independent of lane markings or physical features. For example, a lane could be interpreted based on an arbitrary path free of obstructions in an area that otherwise lacks features that would be interpreted as lane boundaries. In an example scenario, an AV could interpret a lane through an obstruction-free portion of a field or empty lot. In another example scenario, an AV could interpret a lane through a wide (e.g., wide enough for two or more lanes) road that does not have lane markings. In this scenario, the AV could communicate information about the lane to other AVs so that the other AVs can use the same lane information to coordinate path planning among themselves. 
     The term “over-the-air (OTA) client” includes any AV, or any electronic device (e.g., computer, controller, IoT device, electronic control unit (ECU)) that is embedded in, coupled to, or in communication with an AV. 
     The term “over-the-air (OTA) update” means any update, change, deletion or addition to software, firmware, data or configuration settings, or any combination thereof, that is delivered to an OTA client using proprietary and/or standardized wireless communications technology, including but not limited to: cellular mobile communications (e.g., 2G, 3G, 4G, 5G), radio wireless area networks (e.g., WiFi) and/or satellite Internet. 
     The term “edge node” means one or more edge devices coupled to a network that provide a portal for communication with AVs and can communicate with other edge nodes and a cloud based computing platform, for scheduling and delivering OTA updates to OTA clients. 
     The term “edge device” means a device that implements an edge node and provides a physical wireless access point (AP) into enterprise or service provider (e.g., VERIZON, AT&amp;T) core networks. 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. 
     “One or more” includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above. 
     It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this description, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     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 environment  200  described below with respect to  FIG. 2 . 
     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&#39;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&#39;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. 
     Autonomous vehicles have advantages over vehicles that require a human driver. One advantage is safety. For example, in 2016, the United States experienced 6 million automobile accidents, 2.4 million injuries, 40,000 fatalities, and 13 million vehicles in crashes, estimated at a societal cost of $910+ billion. U.S. traffic fatalities per 100 million miles traveled have been reduced from about six to about one from 1965 to 2015, in part due to additional safety measures deployed in vehicles. For example, an additional half second of warning that a crash is about to occur is believed to mitigate 60% of front-to-rear crashes. However, passive safety features (e.g., seat belts, airbags) have likely reached their limit in improving this number. Thus, active safety measures, such as automated control of a vehicle, are the likely next step in improving these statistics. Because human drivers are believed to be responsible for a critical pre-crash event in 95% of crashes, automated driving systems are likely to achieve better safety outcomes, e.g., by reliably recognizing and avoiding critical situations better than humans; making better decisions, obeying traffic laws, and predicting future events better than humans; and reliably controlling a vehicle better than a human. 
     Referring to  FIG. 1 , an AV system  120  operates the vehicle  100  along a trajectory  198  through an environment  190  to a destination  199  (sometimes referred to as a final location) while avoiding objects (e.g., natural obstructions  191 , vehicles  193 , pedestrians  192 , cyclists, and other obstacles) and obeying rules of the road (e.g., rules of operation or driving preferences). 
     In an embodiment, the AV system  120  includes devices  101  that are instrumented to receive and act on operational commands from the computer processors  146 . We use the term “operational command” to mean an executable instruction (or set of instructions) that causes a vehicle to perform an action (e.g., a driving maneuver). Operational commands can, without limitation, including instructions for a vehicle to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate, decelerate, perform a left turn, and perform a right turn. In an embodiment, computing processors  146  are similar to the processor  304  described below in reference to  FIG. 3 . Examples of devices  101  include a steering control  102 , brakes  103 , gears, accelerator pedal or other acceleration control mechanisms, windshield wipers, side-door locks, window controls, and turn-indicators. 
     In an embodiment, the AV system  120  includes sensors  121  for measuring or inferring properties of state or condition of the vehicle  100 , such as the AV&#39;s position, linear and angular velocity and acceleration, and heading (e.g., an orientation of the leading end of vehicle  100 ). Example of sensors  121  are GPS, 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 sensors  121  also include sensors for sensing or measuring properties of the AV&#39;s environment. For example, monocular or stereo video cameras  122  in the visible light, infrared or thermal (or both) spectra, LiDAR  123 , RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors. 
     In an embodiment, the AV system  120  includes a data storage unit  142  and memory  144  for storing machine instructions associated with computer processors  146  or data collected by sensors  121 . In an embodiment, the data storage unit  142  is similar to the ROM  308  or storage device  310  described below in relation to  FIG. 3 . In an embodiment, memory  144  is similar to the main memory  306  described below. In an embodiment, the data storage unit  142  and memory  144  store historical, real-time, and/or predictive information about the environment  190 . In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment  190  is transmitted to the vehicle  100  via a communications channel from a remotely located database  134 . 
     In an embodiment, the AV system  120  includes communications devices  140  for communicating measured or inferred properties of other vehicles&#39; states and conditions, such as positions, linear and angular velocities, linear and angular accelerations, and linear and angular headings to the vehicle  100 . 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 devices  140  communicate 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 devices  140  include 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 database  134  to AV system  120 . In an embodiment, the remotely located database  134  is embedded in a cloud computing environment  200  as described in  FIG. 2 . The communication devices  140  transmit data collected from sensors  121  or other data related to the operation of vehicle  100  to the remotely located database  134 . In an embodiment, communication devices  140  transmit information that relates to teleoperations to the vehicle  100 . In some embodiments, the vehicle  100  communicates with other remote (e.g., “cloud”) servers  136 . 
     In an embodiment, the remotely located database  134  also stores and transmits digital data (e.g., storing data such as road and street locations). Such data is stored on the memory  144  on the vehicle  100 , or transmitted to the vehicle  100  via a communications channel from the remotely located database  134 . 
     In an embodiment, the remotely located database  134  stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory  198  at similar times of day. In one implementation, such data can be stored on the memory  144  on the vehicle  100 , or transmitted to the vehicle  100  via a communications channel from the remotely located database  134 . 
     Computer processors  146  located on the vehicle  100  algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system  120  to execute its autonomous driving capabilities. 
     In an embodiment, the AV system  120  includes computer peripherals  132  coupled to computer processors  146  for providing information and alerts to, and receiving input from, a user (e.g., an occupant or a remote user) of the vehicle  100 . In an embodiment, peripherals  132  are similar to the display  312 , input device  314 , and cursor controller  316  discussed below in reference to  FIG. 3 . The coupling is wireless or wired. Any two or more of the interface devices can be integrated into a single device. 
     In an embodiment, the AV system  120  receives and enforces a privacy level of a passenger, e.g., specified by the passenger or stored in a profile associated with the passenger. The privacy level of the passenger determines how particular information associated with the passenger (e.g., passenger comfort data, biometric data, etc.) is permitted to be used, stored in the passenger profile, and/or stored on the cloud server  136  and associated with the passenger profile. In an embodiment, the privacy level specifies particular information associated with a passenger that is deleted once the ride is completed. In an embodiment, the privacy level specifies particular information associated with a passenger and identifies one or more entities that are authorized to access the information. Examples of specified entities that are authorized to access information can include other AVs, third party AV systems, or any entity that could potentially access the information. 
     A privacy level of a passenger can be specified at one or more levels of granularity. In an embodiment, a privacy level identifies specific information to be stored or shared. In an embodiment, the privacy level applies to all the information associated with the passenger such that the passenger can specify that none of her personal information is stored or shared. Specification of the entities that are permitted to access particular information can also be specified at various levels of granularity. Various sets of entities that are permitted to access particular information can include, for example, other AVs, cloud servers  136 , specific third party AV systems, etc. 
     In an embodiment, the AV system  120  or the cloud server  136  determines if certain information associated with a passenger can be accessed by the AV  100  or another entity. For example, a third-party AV system that attempts to access passenger input related to a particular spatiotemporal location must obtain authorization, e.g., from the AV system  120  or the cloud server  136 , to access the information associated with the passenger. For example, the AV system  120  uses the passenger&#39;s specified privacy level to determine whether the passenger input related to the spatiotemporal location can be presented to the third-party AV system, the AV  100 , or to another AV. This enables the passenger&#39;s privacy level to specify which other entities are allowed to receive data about the passenger&#39;s actions or other data associated with the passenger. 
       FIG. 2  shows 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 to  FIG. 2 , the cloud computing environment  200  includes cloud data centers  204   a ,  204   b , and  204   c  that are interconnected through the cloud  202 . Data centers  204   a ,  204   b , and  204   c  provide cloud computing services to computer systems  206   a ,  206   b ,  206   c ,  206   d ,  206   e , and  206   f  connected to cloud  202 . 
     The cloud computing environment  200  includes one or more cloud data centers. In general, a cloud data center, for example the cloud data center  204   a  shown in  FIG. 2 , refers to the physical arrangement of servers that make up a cloud, for example the cloud  202  shown in  FIG. 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 in  FIG. 3 . The data center  204   a  has many computing systems distributed through many racks. 
     The cloud  202  includes cloud data centers  204   a ,  204   b , and  204   c  along with the network and networking resources (for example, networking equipment, nodes, routers, switches, and networking cables) that interconnect the cloud data centers  204   a ,  204   b , and  204   c  and help facilitate the computing systems&#39;  206   a - f  access 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 systems  206   a - f  or cloud computing services consumers are connected to the cloud  202  through network links and network adapters. In an embodiment, the computing systems  206   a - f  are 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 systems  206   a - f  are implemented in or as a part of other systems. 
       FIG. 3  shows a computer system  300 . In an implementation, the computer system  300  is 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 can 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 can 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 system  300  includes a bus  302  or other communication mechanism for communicating information, and a processor  304  coupled with a bus  302  for processing information. The processor  304  is, for example, a general-purpose microprocessor. The computer system  300  also includes a main memory  306 , such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus  302  for storing information and instructions to be executed by processor  304 . In one implementation, the main memory  306  is used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor  304 . Such instructions, when stored in non-transitory storage media accessible to the processor  304 , render the computer system  300  into a special-purpose machine that is customized to perform the operations specified in the instructions. 
     In an embodiment, the computer system  300  further includes a read only memory (ROM)  308  or other static storage device coupled to the bus  302  for storing static information and instructions for the processor  304 . 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 the bus  302  for storing information and instructions. 
     In an embodiment, the computer system  300  is coupled via the bus  302  to a display  312 , 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 device  314 , including alphanumeric and other keys, is coupled to bus  302  for communicating information and command selections to the processor  304 . Another type of user input device is a cursor controller  316 , such as a mouse, a trackball, a touch-enabled display, or cursor direction keys for communicating direction information and command selections to the processor  304  and for controlling cursor movement on the display  312 . 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 system  300  in response to the processor  304  executing one or more sequences of one or more instructions contained in the main memory  306 . Such instructions are read into the main memory  306  from another storage medium, such as the storage device  310 . Execution of the sequences of instructions contained in the main memory  306  causes the processor  304  to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions. 
     The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media includes non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, solid-state drives, or three-dimensional cross point memory, such as the storage device  310 . Volatile media includes dynamic memory, such as the main memory  306 . Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NV-RAM, or any other memory chip or cartridge. 
     Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus  302 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications. 
     In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to the processor  304  for 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 system  300  receives 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 bus  302 . The bus  302  carries the data to the main memory  306 , from which processor  304  retrieves and executes the instructions. The instructions received by the main memory  306  can optionally be stored on the storage device  310  either before or after execution by processor  304 . 
     The computer system  300  also includes a communication interface  318  coupled to the bus  302 . The communication interface  318  provides a two-way data communication coupling to a network link  320  that is connected to a local network  322 . For example, the communication interface  318  is 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 interface  318  is 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 interface  318  sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information. 
     The network link  320  typically provides data communication through one or more networks to other data devices. For example, the network link  320  provides a connection through the local network  322  to a host computer  324  or to a cloud data center or equipment operated by an Internet Service Provider (ISP)  326 . The ISP  326  in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet”  328 . The local network  322  and Internet  328  both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link  320  and through the communication interface  318 , which carry the digital data to and from the computer system  300 , are example forms of transmission media. In an embodiment, the network  320  contains the cloud  202  or a part of the cloud  202  described above. 
     The computer system  300  sends messages and receives data, including program code, through the network(s), the network link  320 , and the communication interface  318 . In an embodiment, the computer system  300  receives code for processing. The received code is executed by the processor  304  as it is received, and/or stored in storage device  310 , or other non-volatile storage for later execution. 
     Autonomous Vehicle Architecture 
       FIG. 4  shows an example architecture  400  for an autonomous vehicle (e.g., the vehicle  100  shown in  FIG. 1 ). The architecture  400  includes a perception module  402  (sometimes referred to as a perception circuit), a planning module  404  (sometimes referred to as a planning circuit), 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 role in the operation of the vehicle  100 . Together, the modules  402 ,  404 ,  406 ,  408 , and  410  can be part of the AV system  120  shown in  FIG. 1 . In some embodiments, any of the modules  402 ,  404 ,  406 ,  408 , and  410  is 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). Each of the modules  402 ,  404 ,  406 ,  408 , and  410  is sometimes referred to as a processing circuit (e.g., computer hardware, computer software, or a combination of the two). A combination of any or 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 representing a destination  412  and determines data representing a trajectory  414  (sometimes referred to as a route) that can be traveled by the vehicle  100  to reach (e.g., arrive at) the destination  412 . In order for the planning module  404  to determine the data representing the trajectory  414 , the planning module  404  receives data from the perception module  402 , the localization module  408 , and the database module  410 . 
     The perception module  402  identifies nearby physical objects using one or more sensors  121 , e.g., as also shown in  FIG. 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 objects  416  is provided to the planning module  404 . 
     The planning module  404  also receives data representing the AV position  418  from the localization module  408 . The localization module  408  determines the AV position by using data from the sensors  121  and data from the database module  410  (e.g., a geographic data) to calculate a position. For example, the localization module  408  uses 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 module  408  includes 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. In an embodiment, the high-precision maps are constructed by adding data through automatic or manual annotation to low-precision maps. 
     The control module  406  receives the data representing the trajectory  414  and the data representing the AV position  418  and operates the control functions  420   a - c  (e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the vehicle  100  to travel the trajectory  414  to the destination  412 . For example, if the trajectory  414  includes a left turn, the control module  406  will operate the control functions  420   a - c  in a manner such that the steering angle of the steering function will cause the vehicle  100  to turn left and the throttling and braking will cause the vehicle  100  to pause and wait for passing pedestrians or vehicles before the turn is made. 
     Autonomous Vehicle Inputs 
       FIG. 5  shows an example of inputs  502   a - d  (e.g., sensors  121  shown in  FIG. 1 ) and outputs  504   a - d  (e.g., sensor data) that is used by the perception module  402  ( FIG. 4 ). One input  502   a  is a LiDAR (Light Detection and Ranging) system (e.g., LiDAR  123  shown in  FIG. 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 output  504   a . 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 environment  190 . 
     Another input  502   b  is 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 system produces RADAR data as output  504   b . For example, RADAR data are one or more radio frequency electromagnetic signals that are used to construct a representation of the environment  190 . 
     Another input  502   c  is 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 output  504   c . 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 some embodiments, the camera system is configured to “see” objects far, e.g., up to a kilometer or more ahead of the AV. Accordingly, in some embodiments, the camera system has features such as sensors and lenses that are optimized for perceiving objects that are far away. 
     Another input  502   d  is 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 output  504   d . 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 vehicle  100  has access to all relevant navigation information provided by these objects. For example, the viewing angle of the TLD system is about 120 degrees or more. 
     In some embodiments, outputs  504   a - d  are combined using a sensor fusion technique. Thus, either the individual outputs  504   a - d  are provided to other systems of the vehicle  100  (e.g., provided to a planning module  404  as shown in  FIG. 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. 6  shows an example of a LiDAR system  602  (e.g., the input  502   a  shown in  FIG. 5 ). The LiDAR system  602  emits light  604   a - c  from a light emitter  606  (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 light  604   b  emitted encounters a physical object  608  (e.g., a vehicle) and reflects back to the LiDAR system  602 . (Light emitted from a LiDAR system typically does not penetrate physical objects, e.g., physical objects in solid form.) The LiDAR system  602  also has one or more light detectors  610 , which detect the reflected light. In an embodiment, one or more data processing systems associated with the LiDAR system generates an image  612  representing the field of view  614  of the LiDAR system. The image  612  includes information that represents the boundaries  616  of a physical object  608 . In this way, the image  612  is used to determine the boundaries  616  of one or more physical objects near an AV. 
       FIG. 7  shows the LiDAR system  602  in operation. In the scenario shown in this figure, the vehicle  100  receives both camera system output  504   c  in the form of an image  702  and LiDAR system output  504   a  in the form of LiDAR data points  704 . In use, the data processing systems of the vehicle  100  compares the image  702  to the data points  704 . In particular, a physical object  706  identified in the image  702  is also identified among the data points  704 . In this way, the vehicle  100  perceives the boundaries of the physical object based on the contour and density of the data points  704 . 
       FIG. 8  shows the operation of the LiDAR system  602  in additional detail. As described above, the vehicle  100  detects the boundary of a physical object based on characteristics of the data points detected by the LiDAR system  602 . As shown in  FIG. 8 , a flat object, such as the ground  802 , will reflect light  804   a - d  emitted from a LiDAR system  602  in a consistent manner. Put another way, because the LiDAR system  602  emits light using consistent spacing, the ground  802  will reflect light back to the LiDAR system  602  with the same consistent spacing. As the vehicle  100  travels over the ground  802 , the LiDAR system  602  will continue to detect light reflected by the next valid ground point  806  if nothing is obstructing the road. However, if an object  808  obstructs the road, light  804   e - f  emitted by the LiDAR system  602  will be reflected from points  810   a - b  in a manner inconsistent with the expected consistent manner. From this information, the vehicle  100  can determine that the object  808  is present. 
     Path Planning 
       FIG. 9  shows a block diagram  900  of the relationships between inputs and outputs of a planning module  404  (e.g., as shown in  FIG. 4 ). In general, the output of a planning module  404  is a route  902  from a start point  904  (e.g., source location or initial location), and an end point  906  (e.g., destination or final location). The route  902  is 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 vehicle  100  is 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 route  902  includes “off-road” segments such as unpaved paths or open fields. 
     In addition to the route  902 , a planning module also outputs lane-level route planning data  908 . The lane-level route planning data  908  is used to traverse segments of the route  902  based on conditions 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  includes trajectory planning data  910  that the vehicle  100  can 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 data  908  includes speed constraints  912  specific to a segment of the route  902 . For example, if the segment includes pedestrians or un-expected traffic, the speed constraints  912  may limit the vehicle  100  to 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 module  404  includes database data  914  (e.g., from the database module  410  shown in  FIG. 4 ), current location data  916  (e.g., the AV position  418  shown in  FIG. 4 ), destination data  918  (e.g., for the destination  412  shown in  FIG. 4 ), and object data  920  (e.g., the classified objects  416  as perceived by the perception module  402  as shown in  FIG. 4 ). In some embodiments, the database data  914  includes rules used in planning. Rules are specified using a formal language, e.g., using Boolean logic. In any given situation encountered by the vehicle  100 , 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 vehicle  100 , 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. 10  shows a directed graph  1000  used in path planning, e.g., by the planning module  404  ( FIG. 4 ). In general, a directed graph  1000  like the one shown in  FIG. 10  is used to determine a path between any start point  1002  and end point  1004 . In real-world terms, the distance separating the start point  1002  and end point  1004  may 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 graph  1000  has nodes  1006   a - d  representing different locations between the start point  1002  and the end point  1004  that could be occupied by an vehicle  100 . In some examples, e.g., when the start point  1002  and end point  1004  represent different metropolitan areas, the nodes  1006   a - d  represent segments of roads. In some examples, e.g., when the start point  1002  and the end point  1004  represent different locations on the same road, the nodes  1006   a - d  represent different positions on that road. In this way, the directed graph  1000  includes 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 point  1002  and the end point  1004  are 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 vehicle  100 . 
     The nodes  1006   a - d  are distinct from objects  1008   a - b  which cannot overlap with a node. In an embodiment, when granularity is low, the objects  1008   a - b  represent regions that cannot be traversed by automobile, e.g., areas that have no streets or roads. When granularity is high, the objects  1008   a - b  represent physical objects in the field of view of the vehicle  100 , e.g., other automobiles, pedestrians, or other entities with which the vehicle  100  cannot share physical space. In an embodiment, some or all of the objects  1008   a - b  are a 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 nodes  1006   a - d  are connected by edges  1010   a - c . If two nodes  1006   a - b  are connected by an edge  1010   a , it is possible for an vehicle  100  to travel between one node  1006   a  and the other node  1006   b , e.g., without having to travel to an intermediate node before arriving at the other node  1006   b . (When we refer to an vehicle  100  traveling between nodes, we mean that the vehicle  100  travels between the two physical positions represented by the respective nodes.) The edges  1010   a - c  are often bidirectional, in the sense that an vehicle  100  travels from a first node to a second node, or from the second node to the first node. In an embodiment, edges  1010   a - c  are unidirectional, in the sense that an vehicle  100  can travel from a first node to a second node, however the vehicle  100  cannot travel from the second node to the first node. Edges  1010   a - c  are 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 module  404  uses the directed graph  1000  to identify a path  1012  made up of nodes and edges between the start point  1002  and end point  1004 . 
     An edge  1010   a - c  has an associated cost  1014   a - b . The cost  1014   a - b  is a value that represents the resources that will be expended if the vehicle  100  chooses that edge. A typical resource is time. For example, if one edge  1010   a  represents a physical distance that is twice that as another edge  1010   b , then the associated cost  1014   a  of the first edge  1010   a  may be twice the associated cost  1014   b  of the second edge  1010   b . Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges  1010   a - b  may represent the same physical distance, but one edge  1010   a  may require more fuel than another edge  1010   b , e.g., because of road conditions, expected weather, etc. 
     When the planning module  404  identifies a path  1012  between the start point  1002  and end point  1004 , the planning module  404  typically 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. 11  shows a block diagram  1100  of the inputs and outputs of a control module  406  (e.g., as shown in  FIG. 4 ). A control module operates in accordance with a controller  1102  which includes, for example, one or more processors (e.g., one or more computer processors such as microprocessors or microcontrollers or both) similar to processor  304 , short-term and/or long-term data storage (e.g., memory random-access memory or flash memory or both) similar to main memory  306 , ROM  308 , and storage device  310 , and instructions stored in memory that carry out operations of the controller  1102  when the instructions are executed (e.g., by the one or more processors). 
     In an embodiment, the controller  1102  receives data representing a desired output  1104 . The desired output  1104  typically includes a velocity, e.g., a speed and a heading. The desired output  1104  can be based on, for example, data received from a planning module  404  (e.g., as shown in  FIG. 4 ). In accordance with the desired output  1104 , the controller  1102  produces data usable as a throttle input  1106  and a steering input  1108 . The throttle input  1106  represents the magnitude in which to engage the throttle (e.g., acceleration control) of an vehicle  100 , e.g., by engaging the steering pedal, or engaging another throttle control, to achieve the desired output  1104 . In some examples, the throttle input  1106  also includes data usable to engage the brake (e.g., deceleration control) of the vehicle  100 . The steering input  1108  represents 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 output  1104 . 
     In an embodiment, the controller  1102  receives feedback that is used in adjusting the inputs provided to the throttle and steering. For example, if the vehicle  100  encounters a disturbance  1110 , such as a hill, the measured speed  1112  of the vehicle  100  is lowered below the desired output speed. In an embodiment, any measured output  1114  is provided to the controller  1102  so that the necessary adjustments are performed, e.g., based on the differential  1113  between the measured speed and desired output. The measured output  1114  includes a measured position  1116 , a measured velocity  1118  (including speed and heading), a measured acceleration  1120 , and other outputs measurable by sensors of the vehicle  100 . 
     In an embodiment, information about the disturbance  1110  is detected in advance, e.g., by a sensor such as a camera or LiDAR sensor, and provided to a predictive feedback module  1122 . The predictive feedback module  1122  then provides information to the controller  1102  that the controller  1102  can use to adjust accordingly. For example, if the sensors of the vehicle  100  detect (“see”) a hill, this information can be used by the controller  1102  to prepare to engage the throttle at the appropriate time to avoid significant deceleration. 
       FIG. 12  shows a block diagram  1200  of the inputs, outputs, and components of the controller  1102 . The controller  1102  has a speed profiler  1202  which affects the operation of a throttle/brake controller  1204 . For example, the speed profiler  1202  instructs the throttle/brake controller  1204  to engage acceleration or engage deceleration using the throttle/brake  1206  depending on, e.g., feedback received by the controller  1102  and processed by the speed profiler  1202 . 
     The controller  1102  also has a lateral tracking controller  1208  which affects the operation of a steering controller  1210 . For example, the lateral tracking controller  1208  instructs the steering controller  1210  to adjust the position of the steering angle actuator  1212  depending on, e.g., feedback received by the controller  1102  and processed by the lateral tracking controller  1208 . 
     The controller  1102  receives several inputs used to determine how to control the throttle/brake  1206  and steering angle actuator  1212 . A planning module  404  provides information used by the controller  1102 , for example, to choose a heading when the vehicle  100  begins operation and to determine which road segment to traverse when the vehicle  100  reaches an intersection. A localization module  408  provides information to the controller  1102  describing the current location of the vehicle  100 , for example, so that the controller  1102  can determine if the vehicle  100  is at a location expected based on the manner in which the throttle/brake  1206  and steering angle actuator  1212  are being controlled. In an embodiment, the controller  1102  receives information from other inputs  1214 , e.g., information received from databases, computer networks, etc. 
     Surface Guided Decision Making 
       FIG. 13A ,  FIG. 13B , and  FIG. 13C  show block diagrams of example systems for surface guided decision making. These systems are configured to classify surfaces along a path of a vehicle (e.g., vehicle  100  shown in  FIG. 1 ). These systems are also configured to control the vehicle based on the classification of the surfaces. The components of the systems can be located onboard the vehicle or remote from the vehicle. In some examples, one or more components are implemented using a computer system similar to the computer system  300  described in  FIG. 3 . Additionally or alternatively, one or more components can be implemented on a cloud computing environment similar to the cloud computing environment  200  described in  FIG. 2 . Note that the systems are shown for illustration purposes only, as the systems can include additional components and/or have one or more components removed without departing from the scope of the disclosure. Further, the various components of the systems can be arranged and connected in any manner. Although the following discussion describes the systems in the context of classifying one surface along a vehicle path, the systems can simultaneously or consecutively classify more than one surface along the vehicle path. 
       FIG. 13A  shows an example system  1300  that is configured to use known surface information to classify a surface along a vehicle path. As shown in  FIG. 13A , the system  1300  includes sensors  1302  (e.g., sensors that are the same as, or similar to, sensors  121  of  FIG. 1 ), surface classifier  1304 , motion planner  1306  (e.g., a motion planner that is the same as, or similar to, planning module  404  described  FIG. 4 ,  FIG. 9 , and  FIG. 10 ), and controller  1308  (e.g., a controller that is the same as, or similar to, control module  406  described in  FIG. 4 ,  FIG. 11 , and  FIG. 12 ). 
     In an embodiment, the sensors  1302  are configured to capture sensor data associated with a surface along a vehicle path. In some examples, the sensors  1302  and the captured sensor data are the same as, or similar to, the inputs  502   a - d  and outputs  504   a - d  of  FIG. 5 , respectively. The possible sensor data includes image data, location data (e.g., GPS coordinates, spatial location, or triangulation data), perception sensor data, weather data (e.g., temperature, humidity, precipitation), wheel rotation sensor data, IMU (e.g., gyroscope and/or accelerometer) data, geometric data (e.g., shape, elevation, dimensions, etc.), and point clouds, among other examples. The surface can be along any portion of the vehicle path. The surface is, for example, along a portion of the vehicle path over which at least one wheel of the vehicle is scheduled to travel. In the example shown in  FIG. 13A , the sensors  1302  are configured to send the sensor data to the surface classifier  1304 . 
     In an embodiment, the surface classifier  1304  is configured to use known surface information to classify a surface based on the captured sensor data. The known surface information includes known surface classifications and known properties (e.g., sensor measurements, ranges of sensor measurements, previously labeled data for road surface conditions, and/or the like that were previously generated based on a vehicle moving over the known surface) of the known classifications. In one example, the known surface information is used to train the surface classifier  1304  to classify surfaces. The surface classifier  1304  can be trained using machine-learning algorithms, such as supervised learning. In supervised learning, inputs and corresponding outputs of interest are provided to the surface classifier  1304 . The surface classifier  1304  adjusts its functions (e.g., in the case of a neural network, one or more weights associated with two or more nodes of two or more different layers) based on a comparison of the output of the surface classifier  1304  and an expected output in order to provide the desired output when subsequent inputs are provided. Examples supervised learning algorithms include deep neural networks, similarity learning, linear regression, random forests, k-nearest neighbors, support vector machines, and decision trees. 
     In another example, the surface classifier  1304  classifies a surface by comparing the sensor data to properties of known surface classifications. In this example, if the surface classifier  1304  identifies a threshold similarity between the sensor data and the properties of a known surface classification, then the surface classifier  1304  classifies the surface with that surface classification. The threshold similarity is a measure of similarity between the sensor readings and the known properties that is greater than a predetermined threshold (e.g., the sensor readings and the known properties are greater than 90% similar). For instance, a k-nearest neighbors algorithm can be used to compare the sensor data to properties of known surface classifications. 
     In another example, the surface classifier  1304  uses regression to quantify a road surface. In this example, after the surface classifier  1304  classifies a surface as having a particular property, the surface classifier  1304  can quantify an extent that the surface has that property. For instance, a surface can be classified “icy,” and then regression is used to estimate an extent of “iciness”, perhaps on a scale of 0-10. Alternatively, regression can be used to directly estimate the coefficient of friction. Regression can be trained in a similar manner as classification. For example, supervised learning can be used. More specifically, the training data consists of road surfaces that are labeled with ground truth properties (e.g., coefficient of friction, water depth, etc.). Example regression models include (deep) neural networks, linear regression, and support vector machines (SVMs). 
     In an embodiment, the surface classification is based on a surface composition or a surface property. The surface composition is a manufactured material (e.g., asphalt, concrete, tar, bricks) or a naturally occurring element (e.g., rain, snow, sand, rocks). As such, the possible surface classifications include an asphalt surface, a concrete surface, a tar surface, a brick surface, a rain surface, a snow surface, a sand surface, a rock surface, among other examples. The surface property is a shape of the surface, whether the vehicle can drive over the surface (e.g., an obstacle), a coefficient of friction, or any property of the surface having a value with respect to a threshold. As such, a surface can be classified based on its shape, whether or not it is an obstacle, or whether the surface has a property value greater than, equal to, or less than a threshold. The surface classification of temporary surfaces (e.g., a temporary natural element, such as snow or rain) includes a temporal description. For example, a snow surface is classified according to a length of time that it has existed (e.g., freshly packed snow, day old snow, etc.). 
     In some scenarios, the surface classifier  1304  determines that a classification cannot be determined for the surface based on the known surface information (e.g., the surface classifier  1304  does not identify a known surface classification with similar properties to the surface). In these scenarios, the surface classifier  1304  classifies the surface as an unknown surface. As shown in  FIG. 13A , the surface classifier  1304  provides the surface classification to the motion planner  1306 . In some examples, the surface classifier  1304  also provides the motion planner  1306  with the sensor data associated with the surface. 
     In an embodiment, the motion planner  1306  is configured to determine a vehicle behavior based on the surface classification. In an example, the motion planner  1306  first determines drivability properties of the surface based on the surface classification. Drivability properties can include physical characteristics that affect the manner in which a vehicle drives over the surface. Example drivability properties include friction, traction, road grip, resistance, rolling resistance, obstruction, among other properties. If the surface classification is known to the system  1300 , the motion planner  1306  obtains from a database of drivability properties associated with the known surface classification. In some examples, the motion planner  1306  also generates a surface map that includes a list of geometric descriptions of the surface (e.g., generated based on the sensor data) and/or a distribution of the drivability properties of the surface. 
     In an embodiment, the motion planner  1306  determines the vehicle behavior based on the drivability properties of the surface. In one example, the motion planner  1306  determines the vehicle behavior based on known vehicle behaviors (e.g., historical vehicle behaviors). More specifically, the motion planner  1306  determines the vehicle behavior based on a known vehicle behavior associated with the known surface classification or a surface with similar drivability properties. Example vehicle behaviors include: following existing tracks (e.g., on a rainy or snowy surface), avoiding certain surfaces (e.g., avoiding ice patches), adjusting vehicle speed or torque, biasing within a lane, changing lanes, and defining a new center lane (e.g., to increase friction on a road surface that is partially covered with snow or rain). In examples where the surface classification is unknown, the motion planner  1306  determines a precautionary vehicle behavior (e.g., reducing speed and avoiding the surface if possible). Once the motion planner  1306  determines the vehicle behavior, the motion planner  1306  provides the determined vehicle behavior to the controller  1308 . The controller  1308  then controls the vehicle based on the determined vehicle behavior. 
       FIG. 13B  illustrates an example system  1310  that is configured to use known surface information and feedback from a vehicle controller to classify a surface along a vehicle path. As shown in  FIG. 13B , like the system  1300 , the system  1310  includes the sensors  1302 , the motion planner  1306 , and the controller  1308 . However, unlike the surface classifier  1304 , a surface classifier  1312  of the system  1310  receives feedback from the controller  1308 . 
     In an embodiment, in addition to using known surface information to classify surfaces, the surface classifier  1312  also uses feedback from the controller  1308 . The feedback is used to generate new surface classifications or to refine known surface classifications. The feedback includes sensor measurements captured when the vehicle was near a surface (e.g., within a threshold distance) or driving on the surface. In examples where the surface has a known classification, the surface classifier  1312  uses the feedback to update the properties of the classification (that is, update the output of the classification). And in examples where the surface has an unknown classification, the surface classifier  1312  uses the feedback to generate a new surface classification. The surface classifier  1312  includes the feedback as properties of the new surface classification. For example, the new surface classification includes the feedback as labels used to identify the new surface classification. The new surface classification and/or the updated surface classification is used by the surface classifier  1312  for classifying surfaces (e.g., using the techniques described above with respect to surface classifier  1304 ). 
       FIG. 13C  illustrates an example system  1320  that is configured to use known surface information, feedback from a vehicle controller, shared data across a vehicle fleet, and data from external sources to classify a surface along a path of a vehicle. The system  1320  is also configured to predict, based on the surface classification, a vehicle behavior of one or more other vehicles near or driving on the surface. The system is also configured to use the captured behavior of the other vehicles to estimate the road surface (e.g., a slipping vehicle can indicate a slippery surface) and to determine appropriate driving behaviors. Further, the system  1320  is configured to control a behavior of the vehicle based on the surface classification and/or the predicted behavior of the one or more other vehicles. As shown in  FIG. 13C , like the systems  1300  of  FIG. 13A and 1310  of  FIG. 13B , the system  1320  includes the sensors  1302 , the motion planner  1306 , and the controller  1308 . The system  1320  also includes surface classifier  1322 , shared dynamic surface map  1324 , external sources  1326 , and motion predictor  1328 . 
     In an embodiment, in addition to capturing data associated with a surface along a vehicle path, the sensors  1302  are also configured to capture sensor data indicative of the behavior of other vehicles that are near the surface or driving on the surface. As shown in  FIG. 13C , the captured behavior of other vehicles is provided to the motion predictor  1328 . As described below, the captured behavior of other vehicles is used to train the motion predictor  1328  to predict the behavior of one or more other vehicles that are near or driving on a surface along a vehicle path. 
     In an embodiment, the shared dynamic surface map  1324  is a database (e.g., a database including a map) shared across a fleet of vehicles. The shared dynamic surface map  1324  receives information from the vehicles of the fleet and shares that information in the database. For example, the shared dynamic surface map  1324  receives from vehicle controllers, such as the vehicle controller  1308 , surface property feedback and vehicle behavior feedback. The surface property feedback includes sensor measurements captured when a vehicle was near a surface or driving on a surface. The vehicle behavior feedback includes information indicative of a vehicle trajectory and/or vehicle driving settings (e.g., speed or torque) when the vehicle was near a surface or driving on a surface. In some examples, the shared dynamic surface map  1324  receives information associated with a temporary surface (e.g., a metal plate that is temporarily placed over an opening in a road surface, a grated road surface, and/or the like). In such examples, the shared dynamic surface map  1324  schedules the information to expire after a specified amount of time. The amount of time after which the information expires can be associated with (e.g., depend on) the type of the surface (e.g., information associated with a first temporary surface (e.g., a metal plate) can expire in an amount of days whereas information associated with second temporary surface (e.g., a grated road surface) can expire in an amount of days or an amount of weeks). As shown in  FIG. 13C , the surface classifier  1322  receives known surface information (e.g., known surface properties and surface classifications) from the shared dynamic surface map  1324 . The known surface information is used by the surface classifier  1322  to classify surfaces. Further, the motion predictor  1328  receives vehicle behavior feedback from the shared dynamic surface map  1324 . The vehicle behavior feedback is used by the motion predictor  1328  to predict the behavior of other vehicles. 
     In an embodiment, the external sources  1326  include databases that provide information associated with surfaces along a vehicle path. For example, the external sources  1326  include weather databases and/or construction databases that provide weather information and construction information for areas along the vehicle path. As shown in  FIG. 13C , the surface classifier  1322  receives data from the external sources. The surface classifier  1322  uses the data to classify surfaces. 
     In an embodiment, the surface classifier  1322  is configured to receive sensor data from sensors  1302 , feedback from the controller  1308 , shared data from the shared dynamic surface map  1324 , and/or external data from external sources  1326 . In an example, the feedback from the controller  1308 , shared data from the shared dynamic surface map  1324 , and/or external data from external sources  1326  is used to train the surface classifier  1322  to classify surfaces (e.g., using the techniques described above with respect to surface classifier  1304  and surface classifier  1312 ). For example, the data is used to generate new surface classifications or to refine known surface classifications. The surface classifications are used to classify a surface based on the sensor data received from the sensors  1302 . 
     In an embodiment, the surface classifier  1322  receives sensor data indicative of the behavior of another vehicle that is near the surface or driving on the surface. In this embodiment, the surface classifier  1322  uses vehicle behavior to classify the surface on which the other vehicle is driving. For example, if the vehicle is slipping or sliding, the surface classifier  1322  determines that the surface is a slippery surface. As described below, the surface classification can be used to determine a vehicle behavior, e.g., determining a top speed based on the surface classification. As shown in  FIG. 13C , the surface classifier  1322  provides the surface classification to the motion planner  1306  and the motion predictor  1328 . 
     In an embodiment, the motion predictor  1328  is configured to predict, based on a classification of a surface received from the surface classifier  1322 , the behavior of another vehicle that is near the surface or driving on the surface. In one example, known vehicle behavior, other vehicle behavior feedback received from the shared dynamic surface map  1324 , and/or captured vehicle behavior of other vehicles received from the sensors  1302  is used to train the motion predictor  1328  to predict the behavior of other vehicles. More specifically, the motion predictor  1328  can implement one or more machine-learning algorithms, such as supervised learning and reinforcement learning. In such an example, the motion predictor  1328  can be trained using the known vehicle behaviors, the other vehicle behavior feedback, and/or the captured vehicle behavior of other vehicles. In another example, the motion predictor  1328  predicts the behavior of another vehicle by comparing how an observed vehicle behaves with how vehicles have historically behaved when near the surface or driving on the surface. As shown in  FIG. 13C , the motion predictor  1328  provides the predicted vehicle behavior to the motion planner  1306 . 
     In an embodiment, the motion planner  1306  is configured to determine a vehicle behavior based on the surface classification received from the surface classifier  1322  and/or the predicted vehicle behavior received from the motion predictor  1328 . More specifically, the motion planner  1306  determines the vehicle behavior based on the surface classification drivability properties of the surface. The motion planner  1306  then determines the vehicle behavior based on the drivability properties of the surface and/or the predicted vehicle behavior of another vehicle that is near the surface or driving on the surface. As an example, the motion planner  1306  determines a vehicle behavior that causes the vehicle to drive in the track of other vehicles while it is snowing (e.g., to maximize the friction and minimize the risk of losing control). As another example, the motion planner  1306  determines the vehicle behavior based on historical vehicle behaviors on surfaces with the same or similar drivability properties. As yet another example, the motion planner  1306  determines a vehicle behavior that follows or avoids the other vehicle that is reacting to the surface. In some examples, the motion planner  1306  also determines a vehicle behavior that is associated with a safety or performance value that is greater than a current safety or performance value associated with a current vehicle behavior. The motion planner  1306  provides the controller  1308  with the vehicle motion. 
     In an embodiment, the controller  1308  then controls the vehicle based on the determined vehicle behavior. As shown in  FIG. 13C , the controller  1308  also sends feedback to the surface classifier  1322 . Further, the controller  1308  sends surface property feedback and/or vehicle behavior feedback to the shared dynamic surface map  1324 . In some examples, the controller  1308  sends vehicle behavior feedback to the motion planner  1306 . 
       FIG. 14  shows a flowchart of a process  1400  for surface guided decision making. For example, the process could be carried out by the system  1300  of  FIG. 13A , the system  1310  of  FIG. 13B , or the system  1320  of  FIG. 13C . Sensor data associated with a surface (e.g., image of the surface, scan of the surface, or a location of the surface) along a path to be traveled by a vehicle is received  1402  from at least one sensor (e.g., a camera, LiDAR [described in  FIG. 6 ,  FIG. 7 , and  FIG. 8 ], radar, or a location sensor) of the vehicle. 
     A surface classifier is used to determine  1404  a classification of the surface (e.g., type of surface or material on the surface, such as snow, ice, sand, chemicals [e.g., oil or paint], pebbles, rock, dust, or any other material that changes the drivability of the surface) based on the sensor data. Drivability properties of the surface (e.g., such as friction, traction, road grip, resistance, rolling resistance, obstruction) are determined  1406  based on the classification of the surface. 
     A behavior of the vehicle when driving near the surface or on the surface is planned based on the drivability properties of the surface at  1408 . Examples of the behavior include determining a motion that accounts for the drivability properties, determining the motion based on previous vehicle motion (either the vehicle or another vehicle) on surfaces with the same or similar drivability properties, determining the motion based on the predicted motion of another vehicle driving on the surface, following existing tracks on the road when it is snowing or raining, avoiding ice patches, reducing speed, biasing within a lane, changing lanes to avoid an obstacle, defining a new center lane (baseline) path to increase friction on road surface that is partially covered with snow, reduce speed/torque over compromised road segments, follow or avoid another vehicle that is reacting to the surface. The vehicle is controlled based on the behavior of the vehicle at  1410 . 
     In some implementations, determining, based on the surface classification, drivability properties of the surface involves generating a surface map that includes at least one of: a list of geometric descriptions of the surface or a distribution of the drivability properties on the path of the vehicle. 
     In some implementations, the surface classification includes a known surface, and determining, based on the surface classification, drivability properties of the surface involves obtaining, from a database, drivability properties associated with the known surface. 
     In some implementations, the surface classification is an unknown surface, and determining, based on the surface classification, drivability properties of the surface involves: determining, from a database, sensor measurements included in a label of the unknown surface, wherein the sensor measurements are historical sensor measurements associated with the unknown surface; and determining the drivability properties of the unknown surface based on the sensor measurements. 
     In some implementations, the historical sensor measurements are measured by the vehicle or received from another vehicle. 
     In some implementations, planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface involves determining, based on the drivability properties, a vehicle motion that is associated with a safety or performance value that is greater than a current safety or performance value associated with a current vehicle motion. 
     In some implementations, the surface is a first surface, and planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface involves determining a historical vehicle motion performed on a second surface that has properties similar to the drivability properties of the first surface. 
     In some implementations, the vehicle is a first vehicle, and planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface involves detecting a second vehicle in proximity of the first vehicle; determining, based on the drivability properties of the surface, an expected motion of the second vehicle; and determining, based on the expected motion of the second vehicle, the behavior of the first vehicle. 
     In some implementations, the surface classifier receives, from the at least one sensor, sensor measurements performed when the vehicle drives over the surface. 
     In some implementations, the surface classification is a known surface classification, and the process  1400  further involves updating, based on the sensor measurements, a classifier associated with the surface classification. 
     In some implementations, the surface classification is an unknown surface, and wherein the process  1400  further involves adding the sensor measurements to a label associated with the unknown surface. 
     In some implementations, the process  1400  further involves receiving from a shared dynamic database at least one of a road surface classification information or known surface property information. 
     In some implementations, the vehicle is a first vehicle, and the method  1400  further involves capturing, using the at least one sensor, a motion of a second vehicle that is driving on the surface. 
     In some implementations, the process  1400  further involves sending to a shared dynamic database at least one of: surface property feedback or vehicle motion feedback when the vehicle drives on the surface. 
     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 comprising”, 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.