Patent Description:
The present invention relates generally to operation of an autonomous vehicle.

An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little to no human input. In particular, an autonomous vehicle can observe its surrounding environment using a variety of sensors and can attempt to comprehend the environment by performing various processing techniques on data collected by the sensors. This can allow an autonomous vehicle to navigate without human intervention and, in some cases, even omit the use of a human driver altogether. <CIT>A proposes determining the presence of precipitation and determining at least one autonomous control action for a vehicle based at least in part on the precipitation. <CIT> proposes an autonomous navigation system which can navigate a vehicle through an environment according to a selected comfort profile, where the comfort profile associates a particular set of occupant profiles and a particular set of driving control parameters, so that the vehicle is navigated based on the particular set of driving control parameters.

Aspects and advantages of embodiments of the present invention will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

According to a first aspect of the present invention, there is provided a computer-implemented method as set out in claim <NUM>.

The method may further include obtaining data indicating operating constraints of the autonomous vehicle, and wherein generating the motion plan comprises generating the motion plan for the autonomous vehicle based at least in part on the one or more constraints and operating constraints. The method may further include wherein the operating constraints include an expected transient acceleration of the autonomous vehicle over time. The method may further include providing, by the computing system, data indicative of the motion plan to one or more vehicle control systems to control a movement for the autonomous vehicle.

According to a second aspect of the present invention, there is provided a computing system as set out in claim <NUM>.

According to a third aspect of the present invention, there is provided an autonomous vehicle as set out in claim <NUM>.

The above aspects can help improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein. Moreover, the above aspects can help improve the ability of an autonomous vehicle to effectively provide vehicle services to others and support the various members of the community in which the autonomous vehicle is operating, including persons with reduced mobility and/or persons that are underserved by other transportation options. Additionally, the above aspects may reduce traffic congestion in communities as well as provide alternate forms of transportation that may provide environmental benefits.

These and other features, aspects, and advantages of various embodiments will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments and, together with the description, serve to explain the related principles.

Reference now will be made in detail to embodiments, one or more example(s) of which are illustrated in the drawings. Each example is provided by way of explanation of the embodiments, not limitation of the present invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments without departing from the scope of the present invention as defined in the claims.

Example aspects are directed to motion planning for an autonomous vehicle. In particular, the systems and methods disclosed herein provide for using surface friction data (e.g., friction coefficient estimation, etc.) during motion planning to account for road surface conditions in constraints (e.g., based on vehicle dynamics, comfort limits, traction limits, and/or the like) when planning vehicle trajectories. For instance, an autonomous vehicle can be a vehicle that can drive, navigate, operate, etc. with minimal and/or no interaction from a human operator. To do so, the autonomous vehicle can receive sensor data from one or more sensor(s) onboard the vehicle, attempt to comprehend the vehicle's surrounding environment based on the sensor data, and generate an appropriate motion plan through the vehicle's surrounding environment. According to example embodiments of the present disclosure, the autonomous vehicle takes surface friction data (e.g., estimations, based in part on vehicle dynamics, etc.) into consideration when generating a motion plan to produce feasible trajectories which consider the road surface and assist to keep an autonomous vehicle within its traction limits.

More particularly, an autonomous vehicle (e.g., a ground-based vehicle, air-based vehicle, and/or other vehicle type) can include a variety of systems onboard the autonomous vehicle to control the operation of the vehicle. For instance, the autonomous vehicle can include one or more data acquisition systems (e.g., sensors, image capture devices), one or more vehicle computing systems (e.g. for providing autonomous operation), one or more vehicle control systems, (e.g., for controlling acceleration, braking, steering, etc.), and/or the like. The data acquisition system(s) can acquire sensor data (e.g., lidar data, radar data, image data, etc.) associated with one or more objects (e.g., pedestrians, vehicles, etc.) that are proximate to the autonomous vehicle and/or sensor data associated with the vehicle path (e.g., path shape, boundaries, markings, etc.). The sensor data can include information that describes the location (e.g., in three-dimensional space relative to the autonomous vehicle) of points that correspond to objects within the surrounding environment of the autonomous vehicle (e.g., at one or more times). The data acquisition system(s) can provide such sensor data to the vehicle computing system.

In addition to the sensor data, the vehicle computing system can obtain map data that provides other detailed information about the surrounding environment of the autonomous vehicle. For example, the map data can provide information regarding: the identity and location of various roadways, road segments, buildings, or other items; the location and direction of traffic lanes (e.g. the boundaries, location, direction, etc. of a travel lane, parking lane, a turning lane, a bicycle lane, and/or other lanes within a particular travel way); traffic control data (e.g., the location and instructions of signage, traffic signals, and/or other traffic control devices); and/or any other map data that provides information that can assist the autonomous vehicle in comprehending and perceiving its surrounding environment and its relationship thereto.

The vehicle computing system comprises one or more computing devices and can include various subsystems that can cooperate to perceive the surrounding environment of the autonomous vehicle and determine a motion plan for controlling the motion of the autonomous vehicle. For instance, the vehicle computing system can include a perception system, a predication system, and a motion planning system. The vehicle computing system can receive and process the sensor data to generate an appropriate motion plan through the vehicle's surrounding environment.

The perception system can detect one or more objects that are proximate to the autonomous vehicle based on the sensor data. In particular, in some implementations, the perception system can determine, for each object, state data that describes a current state of such object. As examples, the state data for each object can describe an estimate of the object's: current location (also referred to as position); current speed/velocity; current acceleration; current heading; current orientation; size/footprint; class (e.g., vehicle class versus pedestrian class versus bicycle class, etc.); and/or other state information. In some implementations, the perception system can determine state data for each object over a number of iterations. In particular, the perception system can update the state data for each object at each iteration. Thus, the perception system can detect and track objects (e.g., vehicles, bicycles, pedestrians, etc.) that are proximate to the autonomous vehicle over time, and thereby produce a presentation of the world around an autonomous vehicle along with its state (e.g., a presentation of the objects within a scene at the current time along with the states of the objects).

The prediction system can receive the state data from the perception system and predict one or more future locations for each object based on such state data. For example, the prediction system can predict where each object will be located within the next <NUM> seconds, <NUM> seconds, <NUM> seconds, etc. As one example, an object can be predicted to adhere to its current trajectory according to its current speed. As another example, other, more sophisticated prediction techniques or modeling can be used.

The motion planning system can determine a motion plan for the autonomous vehicle based at least in part on predicted one or more future locations for the object provided by the prediction system and/or the state data for the object provided by the perception system. Stated differently, given information about the classification and current locations of objects and/or predicted future locations of proximate objects, the motion planning system can determine a motion plan for the autonomous vehicle that best navigates the autonomous vehicle along the determined travel route relative to the objects at such locations.

As one example, in some implementations, the motion planning system can determine a cost function for each of one or more candidate motion plans for the autonomous vehicle based at least in part on the current locations and/or predicted future locations of the objects. For example, the cost function can describe a cost (e.g., over time) of adhering to a particular candidate motion plan. For example, the cost described by a cost function can increase when the autonomous vehicle approaches impact with another object and/or deviates from a preferred pathway (e.g., a predetermined travel route).

Thus, given information about the classifications, current locations, and/or predicted future locations of objects, the motion planning system can determine a cost of adhering to a particular candidate pathway. The motion planning system can select or determine a motion plan for the autonomous vehicle based at least in part on the cost function(s). For example, the motion plan that minimizes the cost function can be selected or otherwise determined. The motion planning system then can provide the selected motion plan to a vehicle controller that controls one or more vehicle controls (e.g., actuators or other devices that control acceleration, steering, braking, etc.) to execute the selected motion plan.

The vehicle computing obtains surface friction data (e.g., friction coefficient estimation, etc.) and can use such data in motion planning to produce trajectories that take road surface into account and assist to ensure the autonomous vehicle operates within traction limits. In particular, the vehicle computing system (e.g., the motion planning system, etc.) can take vehicle dynamics into consideration in generating a motion plan (e.g., a trajectory), for example, in constraints in an optimization to select a motion plan trajectory. The vehicle computing system takes into account comfort limits and optionally tire traction limits in determining constraints for the optimization, for example, ensuring that the resulting motion plans will not exceed certain lateral acceleration. However, road surface conditions can significantly affect traction limits, for example, where slick road surface conditions can reduce traction limits. Thus, according to the invention, the vehicle computing system obtains road surface friction data comprising estimates of road surface friction and provides the friction data for use in an optimization to produce motion plans (e.g., planned trajectories) that ensure appropriate vehicle performance based on current road surface conditions.

In some implementations, the vehicle computing system can provide the surface friction data along with operating constraint data and/or comfort limit data to the motion planning system for use in generating one or more planned trajectories for the autonomous vehicle. The vehicle computing system obtains surface friction data by generating or obtaining estimates of road surface friction during operation of the autonomous vehicle (e.g., based on data indicative of vehicle events, stimuli, environmental conditions, and/or the like). The vehicle computing system can use the surface friction data in determining and/or adjusting one or more constraints for use in generating one or more trajectories for a motion plan.

The vehicle computing system obtains comfort limit data and surface friction data and uses the comfort limit data and surface friction data in adjusting one or more constraints for use in generating one or more trajectories for a motion plan. In some implementations, the vehicle computing system can also obtain operating constraint data and use the operating constraint data along with the constraints based in part on the comfort limit data and surface friction data in generating one or more trajectories for a motion plan.

In some implementations, the vehicle computing system can separate lateral and longitudinal response and prioritize the lateral components as opposed to longitudinal components in applying constraints during motion planning. In some implementations, the vehicle computing system can leave a reserve in both the lateral direction and longitudinal direction, for example, to ensure remaining authority in either direction. In some implementations, the vehicle computing system can provide for using the surface friction data in determining control for acceleration, deceleration, and/or steering of an autonomous vehicle. For example, in some implementations, the surface friction data can be used in determining and/or adjusting constraints that include a longitudinal force limit for acceleration and/or a lateral force limit for steering of an autonomous vehicle. In some implementations, operating constraints used in motion planning can include an expected transient acceleration of the autonomous vehicle over time.

The systems and methods described herein provide a number of technical effects and benefits. For instance, the systems and methods enable a vehicle computing system to dynamically generate improved vehicle trajectories that can account for current road surface conditions. The improved trajectories can improve the safety of the autonomous vehicle by ensuring that a planned trajectory does not violate traction limits and that the vehicle can be appropriately controlled based on the road surface condition.

The systems and methods described herein can also provide resulting improvements to vehicle computing technology tasked with operation of an autonomous vehicle. For example, aspects of the present invention can enable a vehicle computing system to more efficiently and accurately control an autonomous vehicle's motion ensuring that the vehicle operates safely under varied road surface conditions. As another example, by considering road surface conditions in generating vehicle trajectories, the vehicle computing system can reduce processing and increase system resource availability for other tasks by minimizing the need to respond to unexpected vehicle behavior caused by road surface conditions.

With reference to the figures, example embodiments of the present disclosure will be discussed in further detail.

<FIG> depicts a block diagram of an example system <NUM> for controlling the navigation of an autonomous vehicle <NUM> according to example embodiments of the present disclosure. The autonomous vehicle <NUM> is capable of sensing its environment and navigating with little to no human input. The autonomous vehicle <NUM> can be a ground-based autonomous vehicle (e.g., car, truck, bus, etc.), an air-based autonomous vehicle (e.g., airplane, drone, helicopter, or other aircraft), or other types of vehicles (e.g., watercraft). The autonomous vehicle <NUM> can be configured to operate in one or more modes, for example, a fully autonomous operational mode, semi-autonomous operational mode, and/or a non-autonomous operational mode. A fully autonomous (e.g., self-driving) operational mode can be one in which the autonomous vehicle can provide driving and navigational operation with minimal and/or no interaction from a human driver present in the vehicle. A semi-autonomous (e.g., driver-assisted) operational mode can be one in which the autonomous vehicle operates with some interaction from a human driver present in the vehicle.

The autonomous vehicle <NUM> can include one or more sensors <NUM>, a vehicle computing system <NUM>, and one or more vehicle controls <NUM>. The vehicle computing system <NUM> can assist in controlling the autonomous vehicle <NUM>. In particular, the vehicle computing system <NUM> can receive sensor data from the one or more sensors <NUM>, attempt to comprehend the surrounding environment by performing various processing techniques on data collected by the sensors <NUM>, and generate an appropriate motion path through such surrounding environment. The vehicle computing system <NUM> can control the one or more vehicle controls <NUM> to operate the autonomous vehicle <NUM> according to the motion path. Additionally, in some implementations, the vehicle computing system <NUM> can obtain rider profile data including one or more user preferences/settings (e.g., from a remote computing system) and apply the one or more user preference/settings in determining whether a rider's trip request can be assigned to an autonomous vehicle.

The vehicle computing system <NUM> can include one or more processors <NUM> and at least one memory <NUM>. The one or more processors <NUM> can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory <NUM> can include one or more non-transitory computer-readable storage mediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory <NUM> can store data <NUM> and instructions <NUM> which are executed by the processor <NUM> to cause vehicle computing system <NUM> to perform operations. In some implementations, the one or more processors <NUM> and at least one memory <NUM> may be comprised in one or more computing devices, such as computing device(s) <NUM>, within the vehicle computing system <NUM>.

In some implementations, vehicle computing system <NUM> can further include a positioning system <NUM>. The positioning system <NUM> can determine a current position of the autonomous vehicle <NUM>. The positioning system <NUM> can be any device or circuitry for analyzing the position of the autonomous vehicle <NUM>. For example, the positioning system <NUM> can determine position by using one or more of inertial sensors, a satellite positioning system, based on IP address, by using triangulation and/or proximity to network access points or other network components (e.g., cellular towers, WiFi access points, etc.) and/or other suitable techniques for determining position. The position of the autonomous vehicle <NUM> can be used by various systems of the vehicle computing system <NUM>.

As illustrated in <FIG>, in some embodiments, the vehicle computing system <NUM> can include a perception system <NUM>, a prediction system <NUM>, and a motion planning system <NUM> that cooperate to perceive the surrounding environment of the autonomous vehicle <NUM> and determine a motion plan for controlling the motion of the autonomous vehicle <NUM> accordingly.

In particular, in some implementations, the perception system <NUM> can receive sensor data from the one or more sensors <NUM> that are coupled to or otherwise included within the autonomous vehicle <NUM>. As examples, the one or more sensors <NUM> can include a Light Detection and Ranging (LIDAR) system, a Radio Detection and Ranging (RADAR) system, one or more cameras (e.g., visible spectrum cameras, infrared cameras, etc.), and/or other sensors. The sensor data can include information that describes the location of objects within the surrounding environment of the autonomous vehicle <NUM>.

As one example, for LIDAR system, the sensor data can include the location (e.g., in three-dimensional space relative to the LIDAR system) of a number of points that correspond to objects that have reflected a ranging laser. For example, LIDAR system can measure distances by measuring the Time of Flight (TOF) that it takes a short laser pulse to travel from the sensor to an object and back, calculating the distance from the known speed of light.

As another example, for RADAR system, the sensor data can include the location (e.g., in three-dimensional space relative to RADAR system) of a number of points that correspond to objects that have reflected a ranging radio wave. For example, radio waves (pulsed or continuous) transmitted by the RADAR system can reflect off an object and return to a receiver of the RADAR system, giving information about the object's location and speed. Thus, RADAR system can provide useful information about the current speed of an obj ect.

As yet another example, for one or more cameras, various processing techniques (e.g., range imaging techniques such as, for example, structure from motion, structured light, stereo triangulation, and/or other techniques) can be performed to identify the location (e.g., in three-dimensional space relative to the one or more cameras) of a number of points that correspond to objects that are depicted in imagery captured by the one or more cameras. Other sensor systems can identify the location of points that correspond to objects as well.

Thus, the one or more sensors <NUM> can be used to collect sensor data that includes information that describes the location (e.g., in three-dimensional space relative to the autonomous vehicle <NUM>) of points that correspond to objects within the surrounding environment of the autonomous vehicle <NUM>.

In addition to the sensor data, the perception system <NUM> can retrieve or otherwise obtain map data <NUM> that provides detailed information about the surrounding environment of the autonomous vehicle <NUM>. The map data <NUM> can provide information regarding: the identity and location of different travelways (e.g., roadways), road segments, buildings, or other items or objects (e.g., lampposts, crosswalks, curbing, etc.); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travelway); traffic control data (e.g., the location and instructions of signage, traffic lights, or other traffic control devices); and/or any other map data that provides information that assists the vehicle computing system <NUM> in comprehending and perceiving its surrounding environment and its relationship thereto.

The perception system <NUM> can identify one or more objects that are proximate to the autonomous vehicle <NUM> based on sensor data received from the one or more sensors <NUM> and/or the map data <NUM>. In particular, in some implementations, the perception system <NUM> can determine, for each object, state data that describes a current state of such object. As examples, the state data for each object can describe an estimate of the object's: current location (also referred to as position); current speed; current heading (also referred to together as velocity); current acceleration; current orientation; size/footprint (e.g., as represented by a bounding shape such as a bounding polygon or polyhedron); class (e.g., vehicle versus pedestrian versus bicycle versus other); yaw rate; and/or other state information.

In some implementations, the perception system <NUM> can determine state data for each object over a number of iterations. In particular, the perception system <NUM> can update the state data for each object at each iteration. Thus, the perception system <NUM> can detect and track objects (e.g., vehicles, pedestrians, bicycles, and the like) that are proximate to the autonomous vehicle <NUM> over time.

The prediction system <NUM> can receive the state data from the perception system <NUM> and predict one or more future locations for each object based on such state data. For example, the prediction system <NUM> can predict where each object will be located within the next <NUM> seconds, <NUM> seconds, <NUM> seconds, etc. As one example, an object can be predicted to adhere to its current trajectory according to its current speed. As another example, other, more sophisticated prediction techniques or modeling can be used.

The motion planning system <NUM> can determine a motion plan for the autonomous vehicle <NUM> based at least in part on the predicted one or more future locations for the object provided by the prediction system <NUM> and/or the state data for the object provided by the perception system <NUM>. Stated differently, given information about the current locations of objects and/or predicted future locations of proximate objects, the motion planning system <NUM> can determine a motion plan for the autonomous vehicle <NUM> that best navigates the autonomous vehicle <NUM> relative to the objects at such locations.

As one example, in some implementations, the motion planning system <NUM> can determine a cost function for each of one or more candidate motion plans for the autonomous vehicle <NUM> based at least in part on the current locations and/or predicted future locations of the objects. For example, the cost function can describe a cost (e.g., over time) of adhering to a particular candidate motion plan. For example, the cost described by a cost function can increase when the autonomous vehicle <NUM> approaches a possible impact with another object and/or deviates from a preferred pathway (e.g., a preapproved pathway).

Thus, given information about the current locations and/or predicted future locations of objects, the motion planning system <NUM> can determine a cost of adhering to a particular candidate pathway. The motion planning system <NUM> can select or determine a motion plan for the autonomous vehicle <NUM> based at least in part on the cost function(s). For example, the candidate motion plan that minimizes the cost function can be selected or otherwise determined. The motion planning system <NUM> can provide the selected motion plan to a vehicle controller <NUM> (e.g., motion controller) that controls one or more vehicle controls <NUM> (e.g., actuators or other devices that control gas flow, acceleration, steering, braking, etc.) to execute the selected motion plan.

Each of the perception system <NUM>, the prediction system <NUM>, the motion planning system <NUM>, and the vehicle controller <NUM> can include computer logic utilized to provide desired functionality. In some implementations, each of the perception system <NUM>, the prediction system <NUM>, the motion planning system <NUM>, and the vehicle controller <NUM> can be implemented in hardware, firmware, and/or software controlling a general purpose processor. For example, in some implementations, each of the perception system <NUM>, the prediction system <NUM>, the motion planning system <NUM>, and the vehicle controller <NUM> includes program files stored on a storage device, loaded into a memory, and executed by one or more processors. In other implementations, each of the perception system <NUM>, the prediction system <NUM>, the motion planning system <NUM>, and the vehicle controller <NUM> includes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium such as RAM hard disk or optical or magnetic media.

<FIG> depicts a diagram <NUM> of some example input data and constraints for vehicle trajectory generation in autonomous vehicle motion planning according to example embodiments of the present invention. As illustrated in <FIG>, example systems and methods of the present invention provide for using surface friction data to modify one or more constraints used in determining motion plan trajectories for an autonomous vehicle. For example, in some implementations, a computing system, such as a vehicle computing system <NUM> of <FIG>, can obtain data indicative of one or more constraints, such as operating constraint data <NUM> (e.g., operating limits, etc.) and/or the like, for use in generating autonomous vehicle trajectories as part of motion planning. In some implementations, the operating constraint data can include data indicative of expected response, data indicative of vehicle dynamics, and/or the like. For example, in some implementations, operating constraint data can include data such as transient response expected at some time in the future, operation codes, and/or the like. In some implementations, transient response expected data can include acceleration change data, separate longitude and latitude data, can reflect fault range limits, and/or the like. In some implementations, operation code data can include separately aggregated failure and/or degraded operation codes. The operating constraint data <NUM> can be provided for use in trajectory generation <NUM>, such as by a motion planning system <NUM> of <FIG> and/or the like.

The vehicle computing system obtains comfort limit data <NUM> which can be provided for use in trajectory generation <NUM>, for example, to provide for determining one or more constraints for use in generating one or more trajectories for the autonomous vehicle. For example, in some implementations, comfort limit data can include data indicative of acceleration (e.g., ax, ay, ȧx, ȧy), force (Fx, Fy, Ḟx, Ḟy), and/or the like. In some implementations, comfort limit data is predetermined rather than determined in real time.

The vehicle computing system obtains friction data <NUM> which can be provided for use in trajectory generation <NUM>. The vehicle computing system (e.g., motion planning system <NUM> and/or the like) uses friction data <NUM> in adjusting one or more constraints to be used in trajectory generation. In some implementations, data indicative of vehicle events, stimuli, environmental conditions, and/or the like (e.g., based in part on data from one or more sensors, etc.) can be used in generating estimates of road surface friction during operation of the autonomous vehicle, which can be included in friction data <NUM> for use in trajectory generation.

<FIG> depicts a block diagram of an example of motion planning vehicle trajectory generation <NUM> according to example embodiments of the present invention. As illustrated in <FIG>, a trajectory generator <NUM> in a motion planning system, such as motion planning system <NUM> of <FIG>, can obtain vehicle data <NUM>, including data such as sensor data, perception data, prediction data, and/or the like (as described in regard to <FIG>) along with operating constraint data <NUM>, comfort limit data <NUM>, and friction data <NUM> for use in generating planned trajectory data <NUM> for use in motion planning for an autonomous vehicle. For example, in some implementations, trajectory generator <NUM> can be provided with comfort limit data <NUM> and friction data <NUM> and use such data in determining and/or adjusting one or more constraints used in generating one or more trajectories for an autonomous vehicle. In some implementations, a surface friction estimator <NUM> may be included which can obtain data indicative of vehicle events, stimuli, environmental conditions, and/or the like for use in generating estimates of road surface friction during operation of the autonomous vehicle, which can be provided as friction data <NUM> for use in trajectory generation.

<FIG> depicts a flowchart diagram of example operations <NUM> for using friction estimations in autonomous vehicle motion planning according to example embodiments of the present invention. One or more portion(s) of the operations <NUM> can be implemented by one or more computing devices such as, for example, the vehicle computing system <NUM> of <FIG>, the computing system <NUM> or <NUM> of <FIG>, and/or the like. Moreover, one or more portion(s) of the operations <NUM> can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as in <FIG> and <FIG>) to, for example, provide for using surface friction data in autonomous vehicle motion planning.

At <NUM>, one or more computing devices included within a computing system (e.g., computing system <NUM>, <NUM>, and/or the like) obtain road surface friction data (e.g., friction coefficient estimation, etc.). For example, in some implementations, the computing system can obtain data indicative of vehicle events, stimuli, environmental conditions, and/or the like and generate estimates of road surface friction during operation of an autonomous vehicle.

At <NUM>, the computing system updates one or more constraints based at least in part on the road surface friction data. For example, in some implementations, the vehicle computing system can use the road surface friction data in determining and/or adjusting one or more constraints, such as operating constraints, constraints based on comfort limits, constraints based on tire traction limits, and/or the like, to be used in generating one or more trajectories for a motion plan. The computing system obtains comfort limit data and surface friction data and uses the comfort limit data and surface friction data in determining and/or adjusting one or more constraints to be used in generating one or more trajectories for a motion plan. The computing system obtains a set of one or more predefined constraints based on comfort limit data and adjusts one or more of these predefined constraints based on the surface friction data.

At <NUM>, the computing system generates motion plan data, including one or more trajectories, based at least in part on the constraints. The computing system can then provide data indicative of the motion plan (e.g., via vehicle controller <NUM> of <FIG>, etc.) to one or more vehicle control systems (e.g., actuators or other devices that control gas flow, acceleration, steering, braking, etc.) to control operation and movement of the autonomous vehicle.

<FIG> depicts a block diagram of an example computing system <NUM> according to example embodiments of the present invention. The example computing system <NUM> illustrated in <FIG> is provided as an example only. The components, systems, connections, and/or other aspects illustrated in <FIG> are optional and are provided as examples of what is possible, but not required, to implement the present invention. In some implementations, the example computing system <NUM> can include the vehicle computing system <NUM> of the autonomous vehicle <NUM> and a computing system <NUM> (e.g., an operations computing system), including one or more computing device(s) <NUM>, that is remote from the autonomous vehicle <NUM>. The vehicle computing system <NUM> of the autonomous vehicle <NUM> and the computing system <NUM> can be communicatively coupled to one another over one or more networks <NUM>. The computing system <NUM> can, for example, be associated with a central operations system and/or an entity associated with the autonomous vehicle <NUM> such as, for example, a vehicle owner, vehicle manager, fleet operator, service provider, etc..

The computing device(s) <NUM> of the computing system <NUM> can include processor(s) <NUM> and a least one memory <NUM>. The one or more processors <NUM> can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory <NUM> can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

The memory <NUM> can store information that can be accessed by the one or more processors <NUM>. For instance, the memory <NUM> (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can include computer-readable instructions <NUM> can be executed by the one or more processors <NUM>. The instructions <NUM> can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions <NUM> can be executed in logically and/or virtually separate threads on processor(s) <NUM>.

For example, the memory <NUM> can store instructions <NUM> that when executed by the one or more processors <NUM> cause the one or more processors <NUM> to perform operations such as any of the operations and functions of the computing device(s) <NUM> or for which the computing device(s) <NUM> are configured, as described herein including, for example, operations of <FIG>.

The memory <NUM> can store data <NUM> that can be obtained, received, accessed, written, manipulated, created, and/or stored. The data <NUM> can include, for instance, sensor data, map data, service request data (e.g., trip and/or user data), operational data, etc., as described herein. In some implementations, the computing device(s) <NUM> can obtain data from one or more memory device(s) that are remote from the computing system <NUM>.

The computing device(s) <NUM> can also include one or more communication interfaces <NUM> used to communicate with one or more other system(s) associated with the computing system <NUM> and/or another computing device that is remote from the computing system <NUM>, such as the vehicle computing system <NUM> of the autonomous vehicle <NUM>, for example. The communication interface <NUM> can include any circuits, components, software, etc. for communicating with one or more networks (e.g., <NUM>). In some implementations, the communication interface <NUM> can include for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software, and/or hardware for communicating data.

The network(s) <NUM> can be any type of network or combination of networks that allows for communication between devices. In some embodiments, the network(s) can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link, and/or some combination thereof, and can include any number of wired or wireless links. Communication over the network(s) <NUM> can be accomplished, for instance, via a communication interface using any type of protocol, protection scheme, encoding, format, packaging, etc..

The vehicle computing system <NUM> of the autonomous vehicle can include one or more computing devices, such as described in regard to <FIG>. The computing devices can include components (e.g., processor(s), memory, instructions, data, etc.) similar to that described herein for the computing device(s) <NUM>, and as described in regard to <FIG>. Moreover, the vehicle computing system <NUM> can be configured to perform one or more operations, as described herein including, for example, operations of <FIG>.

Computing tasks discussed herein as being performed at computing device(s) remote from the autonomous vehicle can instead be performed at the autonomous vehicle (e.g., via the vehicle computing system), or vice versa. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implements tasks and/or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.

Claim 1:
A computer-implemented method for road surface dependent motion planning comprising:
obtaining, by a computing system (<NUM>;<NUM>) comprising one or more computing devices (<NUM>, <NUM>, <NUM>), a first set of predetermined constraints determined based on data indicating comfort limits (<NUM>; <NUM>) for a rider in an autonomous vehicle (<NUM>),
wherein the first set of predetermined constraints further comprises a combination of a longitudinal force limit for acceleration and a lateral force limit for steering of the autonomous vehicle;
obtaining, by the computing system, surface friction data (<NUM>), wherein the surface friction data comprises estimates of road surface friction during operations of the autonomous vehicle;
adjusting, based on the surface friction data, one or more of the first set of predetermined constraints to determine one or more constraints;
and
generating, by the computing system, a motion plan for the autonomous vehicle based at least in part on the one or more constraints.