Abstract: A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle, can receive from a data store or a graphical user interface, ranges for one or more external parameters. The computer implemented method can determine optimum values for external parameters of the vehicle by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the vehicle parameter as determined by the computer implemented method.

Abstract: Systems and methods of a vehicle for partially controlling operation of a vehicle based on operational constraints of the vehicle and/or contextual constraints of the vehicle are disclosed.

Abstract: Systems and methods for controlling a vehicle using operational constraints, including friction estimates are disclosed. Friction estimation include estimating a tire-road coefficient of friction using bins and envelopes. Bounding envelopes are configured to ensure that stability of the vehicle is maintained. The friction estimate is used to define the bounding envelopes. Further, the bounding envelopes are received as feedback into the friction estimation, itself. Based on the bounding envelope, the friction estimation can be adjusted. Then, the adjusted friction estimation can be fed back to reshape the bounding envelopes. Multiple bins can be used to evaluate an operating range of friction. Each bin can be used to compare actual vehicle dynamics with expected dynamics based on the estimation using the range assigned to that bin. Multiple bins and multiple controllers can run in parallel to re-estimate friction considering the vehicle dynamics over time.

Abstract: Systems and methods of using a common control scheme to autonomously controlling a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (MPC).

Abstract: Systems and methods to improve ride comfort for users within a vehicle during operation of the vehicle are disclosed. Exemplary implementations may: generate output signals; determine the current operational information regarding the vehicle; determine a current set of forces operating on one or more of the vehicle and one or more of the users within the vehicle; compare a characteristic of the current set of forces to a comfort threshold level; and responsive to the characteristic breaching the comfort threshold level, effectuate a modification in the operation of the vehicle such that a subsequent change in the characteristic that corresponds to the modification reduces and/or remedies the breach of the comfort threshold level.

Abstract: System, methods, and other embodiments described herein relate to improving the prediction efficiency of autonomous/semi-autonomous vehicles. In one embodiment, the system generates a provisional prediction according to sensor data from at least one sensor of a subject vehicle. The prediction can be associated with an aspect relating to operating the subject vehicle along a path. The system analyzes the provisional prediction in relation to a subsequent prediction about the aspect to determine a correspondence between the provisional prediction and the subsequent prediction. In response to determining that the correspondence satisfies an inaccuracy threshold, the system can store the provisional prediction and the sensor data associated with the provisional prediction to log potential inaccuracies in generating predictions based, at least in part, on the sensor data.

Abstract: A system for transitioning a vehicle from an autonomous mode in response to a handover event, the system includes one or more processors and a memory device operably coupled with the one or more processors. The memory device stores a driver assessment module, an operational condition adjustment module, and a transitioning module. The driver assessment module configures the one or more processors to determine an ability level and a comfort level of a driver of the vehicle in response to the handover event. The operational condition adjustment module configures the one or more to adjust at least one operating condition of the vehicle to be within the ability level of the driver and comfort level. The transitioning module configures the one or more processors to transition the vehicle from the autonomous mode to a driver input mode.

Abstract: A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle can receive from a data store or a graphical user interface, ranges for one or more operational parameters. The computer implemented method can determine optimum values for vehicle parameters of the vehicle of one or more other parameters by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of a parameter as determined by the computer implemented method.

Abstract: A computer implemented method for determining optimal values for controls parameters for a model predictive controller for controlling a vehicle can receive from a data store or a graphical user interface, ranges for one or more operational parameters. The computer implemented method can determine optimum values for controls parameters by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the controls parameter as determined by the computer implemented method.

Abstract: A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle, can receive from a data store or a graphical user interface, ranges for one or more external parameters. The computer implemented method can determine optimum values for external parameters of the vehicle by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the vehicle parameter as determined by the computer implemented method.

Abstract: System, methods, and other embodiments described herein relate to selectively intervening in manual control of a vehicle by a driver. In one embodiment, a method includes predicting a future state of the vehicle according to at least a current state and a control input. The current state defines at least one attribute of a current trajectory of the vehicle, and the control input defines at least one driver input for controlling the vehicle. The method includes comparing the future state with a state constraint indicating a range within which a target path of the vehicle is acceptable. The target path defines a subsequent trajectory for the vehicle. The method includes selectively modifying the target path according to whether the future state violates the state constraint. The method includes controlling the vehicle according to the target path.

Abstract: Systems and methods of a vehicle for visually displaying a current state of a vehicle in regards to operational constraints of the vehicle are disclosed. Exemplary implementations may: generate output signals conveying operational information regarding the vehicle; visually present information; determine, based on the output signals, the operational information; determine, based on the operational information, a current vehicle state of the vehicle; determine, based on the operational information, predicted boundaries of the current vehicle state; determine a metric value of a metric representing a difference between the current vehicle state and the predicted boundaries of the current vehicle state; and effectuate display of the metric upon the visual display unit.

Abstract: A system for controlling a vehicle having an autonomous mode and a semi-autonomous mode includes one or more processors and a memory in communication with the one or more processors. The memory stores a command generating module and a transmission module. The command generating module causes the one or more processors to generate, in response to an input, at least one control signal for controlling the vehicle by an envelope control system. The envelope control system utilizes a common control scheme for both the semi-autonomous mode and the autonomous mode, wherein the input is a driver input when the vehicle is in the semi-autonomous mode and the input is a pseudo-driver input when the vehicle is in the autonomous mode. The transmission module causes the one or more processors to transmit the at least one control signal to a vehicle motion controller, wherein the vehicle motion controller controls the movement of the vehicle.

Abstract: Systems and methods for controlling a vehicle using operational constraints, including friction estimates are disclosed. Friction estimation include estimating a tire-road coefficient of friction using bins and envelopes. Bounding envelopes are configured to ensure that stability of the vehicle is maintained. The friction estimate is used to define the bounding envelopes. Further, the bounding envelopes are received as feedback into the friction estimation, itself. Based on the bounding envelope, the friction estimation can be adjusted. Then, the adjusted friction estimation can be fed back to reshape the bounding envelopes. Multiple bins can be used to evaluate an operating range of friction. Each bin can be used to compare actual vehicle dynamics with expected dynamics based on the estimation using the range assigned to that bin. Multiple bins and multiple controllers can run in parallel to re-estimate friction considering the vehicle dynamics over time.

Abstract: A system for transitioning a vehicle from an autonomous mode in response to a handover event, the system includes one or more processors and a memory device operably coupled with the one or more processors. The memory device stores a driver assessment module, an operational condition adjustment module, and a transitioning module. The driver assessment module configures the one or more processors to determine an ability level and a comfort level of a driver of the vehicle in response to the handover event. The operational condition adjustment module configures the one or more to adjust at least one operating condition of the vehicle to be within the ability level of the driver and comfort level. The transitioning module configures the one or more processors to transition the vehicle from the autonomous mode to a driver input mode.

Abstract: A system for controlling a vehicle having an autonomous mode and a semi-autonomous mode includes one or more processors and a memory in communication with the one or more processors. The memory stores a command generating module and a transmission module. The command generating module causes the one or more processors to generate, in response to an input, at least one control signal for controlling the vehicle by an envelope control system. The envelope control system utilizes a common control scheme for both the semi-autonomous mode and the autonomous mode, wherein the input is a driver input when the vehicle is in the semi-autonomous mode and the input is a pseudo-driver input when the vehicle is in the autonomous mode. The transmission module causes the one or more processors to transmit the at least one control signal to a vehicle motion controller, wherein the vehicle motion controller controls the movement of the vehicle.

Abstract: Systems and methods to improve ride comfort for users within a vehicle during operation of the vehicle are disclosed. Exemplary implementations may: generate output signals; determine the current operational information regarding the vehicle; determine a current set of forces operating on one or more of the vehicle and one or more of the users within the vehicle; compare a characteristic of the current set of forces to a comfort threshold level; and responsive to the characteristic breaching the comfort threshold level, effectuate a modification in the operation of the vehicle such that a subsequent change in the characteristic that corresponds to the modification reduces and/or remedies the breach of the comfort threshold level.

Abstract: Systems and methods to improve ride comfort for users within a vehicle during operation of the vehicle are disclosed. Exemplary implementations may: generate output signals; determine the current operational information regarding the vehicle; determine a current set of forces operating on one or more of the vehicle and one or more of the users within the vehicle; compare a characteristic of the current set of forces to a comfort threshold level; and responsive to the characteristic breaching the comfort threshold level, effectuate a modification in the operation of the vehicle such that a subsequent change in the characteristic that corresponds to the modification reduces and/or remedies the breach of the comfort threshold level.

Abstract: System, methods, and other embodiments described herein relate to improving the prediction efficiency of autonomous/semi-autonomous vehicles. In one embodiment, the system generates a provisional prediction according to sensor data from at least one sensor of a subject vehicle. The prediction can be associated with an aspect relating to operating the subject vehicle along a path. The system analyzes the provisional prediction in relation to a subsequent prediction about the aspect to determine a correspondence between the provisional prediction and the subsequent prediction. In response to determining that the correspondence satisfies an inaccuracy threshold, the system can store the provisional prediction and the sensor data associated with the provisional prediction to log potential inaccuracies in generating predictions based, at least in part, on the sensor data.

Abstract: Systems and methods of a vehicle for partially controlling operation of a vehicle based on operational constraints of the vehicle and/or contextual constraints of the vehicle are disclosed.