Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: Systems and methods are directed to motion planning for an autonomous vehicle. In one example, a computer-implemented method for road surface dependent motion planning includes obtaining, by a computing system comprising one or more computing devices, surface friction data. The method further includes determining, by the computing system, one or more constraints for motion planning based at least in part on the surface friction data. The method further includes generating, by the computing system, a motion plan for an autonomous vehicle based at least in part on the one or more constraints.

Abstract: The present disclosure is directed towards a system for implementing emergency braking. In particular, a computing system can obtain current trajectory data from a motion planning system of an autonomous vehicle. The computing system can determine whether an emergency braking flag within the current trajectory data is set to true based at least in part on the current trajectory data. The computing system can, in response to determining that the emergency braking flag within the current trajectory data is set to true, change a current braking mode of the autonomous vehicle from a first braking mode to a second braking mode. While in the second braking mode, the computing system can set a current acceleration value to the second maximum stopping rate. The computing system can transmit the current acceleration value for implementation by the autonomous vehicle.

Abstract: A system and approach for a vehicle system. The vehicle system may include a vehicle, a propulsion device (e.g., a combustion engine or electric motor), and a controller. The propulsion device may at least partially power the vehicle. The controller may be in communication with the propulsion device and may control the propulsion device according to a target speed of the vehicle. The controller may include a model of energy balances of the vehicle and may use the model to estimate energy losses over a travel horizon of the vehicle. The controller may optimize a cost function over the travel horizon of the vehicle based at least in part on the estimated energy losses to set an actual speed for the vehicle. The estimated energy losses may include one or more of aerodynamic drag, vehicle friction, and conversion efficiency from the propulsion device.

Abstract: The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: In one example embodiment, a computer-implemented method includes receiving data representing a motion plan of the autonomous vehicle via a plurality of control lanes configured to implement the motion plan to control a motion of the autonomous vehicle, the plurality of control lanes including at least a first control lane and a second control lane, and controlling the first control lane to implement the motion plan. The method includes detecting one or more faults associated with implementation of the motion plan by the first control lane or the second control lane, or in generation of the motion plan, and in response to one or more faults, controlling the first control lane or the second control lane to adjust the motion of the autonomous vehicle based at least in part on one or more fault reaction parameters associated with the one or more faults.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining data representing a first trajectory including one or more states corresponding to a motion path. The method includes determining a second trajectory based at least in part on the first trajectory, the second trajectory including a first state corresponding to the motion path, and one or more secondary states corresponding to the motion path, the one or more secondary states indicating a state of the autonomous vehicle relative to the first state. The method includes determining one or more control signals based at least in part on the second trajectory. The method includes controlling a motion of the autonomous vehicle according to the motion path, based at least in part on the one or more control signals.

Abstract: The present disclosure provides a vehicle interface for an autonomous vehicle. In particular, the systems and methods of the present disclosure can, responsive to receiving, from an autonomy computing system of an autonomous vehicle, a time-based trajectory for the autonomous vehicle, verify that execution of the time-based trajectory is within parameters of the autonomous vehicle. Responsive to verifying that execution of the time-based trajectory is within the parameters of the autonomous vehicle, the time-based trajectory can be converted into a spatial path for the autonomous vehicle, and one or more controls of the autonomous vehicle can be interfaced with such that the autonomous vehicle tracks the spatial path.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: The present disclosure provides control systems and methods for an autonomous vehicle. In one example implementation, a control method includes obtaining data indicative of a motion plan for the autonomous vehicle. The method includes upsampling the data indicative of the motion plan to determine a desired vehicle state for the autonomous vehicle. The method includes obtaining data indicative of a current vehicle state for the autonomous vehicle. The method includes determining a control command for the autonomous vehicle based at least in part on the desired vehicle state and the current vehicle state. The method includes controlling the vehicle component based at least in part on the control command.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: A system and approach for a vehicle system. The vehicle system may include a vehicle, a propulsion device (e.g., a combustion engine or electric motor), and a controller. The propulsion device may at least partially power the vehicle. The controller may be in communication with the propulsion device and may control the propulsion device according to a target speed of the vehicle. The controller may include a model of energy balances of the vehicle and may use the model to estimate energy losses over a travel horizon of the vehicle. The controller may optimize a cost function over the travel horizon of the vehicle based at least in part on the estimated energy losses to set an actual speed for the vehicle. The estimated energy losses may include one or more of aerodynamic drag, vehicle friction, and conversion efficiency from the propulsion device.

Abstract: Systems and methods are directed to motion planning for an autonomous vehicle. In one example, a computer-implemented method for road surface dependent motion planning includes obtaining, by a computing system comprising one or more computing devices, surface friction data. The method further includes determining, by the computing system, one or more constraints for motion planning based at least in part on the surface friction data. The method further includes generating, by the computing system, a motion plan for an autonomous vehicle based at least in part on the one or more constraints.