Abstract: Systems and methods for automatically generating solver code for a nonlinear model predictive controller are disclosed. In one embodiment, a method of automatically generating solver code for a nonlinear model predictive control solver includes receiving an optimal control problem code, wherein the optimal control problem code represents an optimal control problem comprising a cost function, one or more constraints, and a continuous time model representing dynamics of a system. The method further includes receiving a discretization method preference, a linearization point preference, and a parameter specification, and encoding the optimal control problem into an optimization problem by discretizing the optimal control problem according to the discretization method preference, and linearizing the optimal control problem according to the linearization point preference. The method further includes generating the solver code from the optimization problem.

Abstract: Systems and methods are provided for optimizing a driver's trajectory around a race track. The system can receive driver data representing driver inputs to a vehicle. A vehicle state can be determined for at least one location on the race track based on the received driver data. Expert driver data can be determined representing inputs of an expert driver at the determined vehicle state for the at least one location. The system can predict how the expert driver would proceed from each current vehicle state based on the expert driver data and map the current vehicle state at the at least one location to a recommended trajectory from each current vehicle state based on predicting how the expert driver would proceed.

Abstract: Systems, methods, and other embodiments described herein relate to assisting a user of a vehicle driving the vehicle along a track. In one embodiment, a method includes developing a driving model for the vehicle travelling on the track. The method includes determining a current state of the vehicle along the track and outputting eye direction instruction for the user of the vehicle based on the driving model and the current state of the vehicle.

Abstract: Systems and methods are provided for optimizing vehicle setups through predictive determinations on subjective driver preferences. Examples provided herein include training a driver specific model based on first vehicle setups and subjective driver feedback of a driver on each first vehicle setup; applying the driver specific model on a second vehicle setup; generating, by the driver specific model, predicted subjective driver feedback on the second vehicle setup predictive of an opinion of the driver on the second vehicle setup; and selecting an optimal vehicle setup based on the predicted subjective driver feedback.

Abstract: Systems and methods are provided trajectory prediction that leverages game-theory to improve coverage of multi-modal predictions. Examples of the systems and methods include obtaining training data including first trajectories for a first plurality of agent devices and first map information of a first environment for a past time horizon and applying the training data to a game-theoretic mode-finding algorithm to generate a mode-finding model for each agent device that predicts modes of the first trajectories. A trajectory prediction model can be trained on the predicted modes as a coverage loss term between predicted modes. Future trajectories can be predicted for a second plurality of agent devices based on applying observed data to the trajectory prediction model. A control signal can then be generated to effectuate an autonomous driving command on an agent device of the second plurality of agent devices based on the predicted future trajectories.

Abstract: Systems and Methods for controlling an autonomous vehicle, may include: receiving sensor data, the sensor data comprising vehicle parameter information for the autonomous vehicle; using the sensor data to determine a vehicle state for the autonomous vehicle, wherein the vehicle state comprises information regarding a magnitude of an actual or predicted effective understeer gradient for the vehicle; computing a yaw moment required to correct the effective understeer gradient based on the magnitude of the effective understeer gradient; and determining a combination of one or more vehicle control inputs, including applying a brake torque, to correct the effective understeer gradient; applying the brake torque to a single wheel of the vehicle, wherein an amount of brake torque applied is sufficient to lock up the single wheel to create a yaw moment on the vehicle to achieve the computed yaw moment required to correct the effective understeer gradient.

Abstract: System, methods, and other embodiments described herein relate to emulating vehicle dynamics. In one embodiment, a method for emulating vehicle dynamics in a vehicle having a plurality of wheels and equipped with all-wheel steering, includes receiving emulation settings that indicate one or more environment parameters and/or vehicle parameters, detecting driver inputs including at least steering input and throttle input, executing a simulation model that receives the driver inputs and emulation settings, simulates the vehicle operating based on the driver inputs and the emulation settings, and outputs one or more simulated states of the vehicle based on the simulated operation of the vehicle, determining one or more actuation commands for each wheel of the vehicle to cause the vehicle to emulate the one or more simulated states, and executing the one or more actuation commands, wherein the actuation commands include at least wheel angle commands and torque commands.

Abstract: A performance enhancement system for an electric vehicle includes a battery pack, a pump system, and a control module. The battery pack contains electrolyte fluid, and the pump system is operable to redistribute the electrolyte fluid within the battery pack. The control module may be configured to identify a vehicle usage event. The vehicle usage event may be a payload event, a pitching event, a rolling event, and/or a yawing event. In response to identifying the vehicle usage event, the control module may be configured to operate the pump system to redistribute the electrolyte fluid within the battery pack to change a static center of mass of the vehicle, a dynamic center of mass of the vehicle, and/or a moment of inertia of the vehicle.

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 skid recovery for a vehicle. In one embodiment, a method for controlling a vehicle during skid includes obtaining data indicating a skid condition of the vehicle, determining whether the skid condition can be corrected by counter-steering, and executing an intervention when the skid condition cannot be corrected by counter-steering, the intervention including inducing slippage in front wheels of the vehicle to change a direction and/or magnitude of lateral forces at the front wheels.

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 controlling a vehicle in a stable drift are provided. With the goal of enhancing the driver experience, the disclosed drift control systems provide an interactive drift driving experience for the driver of a vehicle. In some embodiments, a driver is allowed to take manual control of a vehicle after a stable drift is initiated. For safety reasons, an assisted driving system may provide corrective assistance to prevent the vehicle from entering an unstable/unsafe drift. In other embodiments, an autonomous driving system retains control of the vehicle throughout the drift. However, the driver may perform “simulated drift maneuvers” such as counter-steering, and clutch kicking in order to communicate their desire to drift more or less aggressively. Accordingly, the autonomous driving system will effectuate the driver's communicated desire in a manner that keeps the vehicle in a safe/stable drift.

Abstract: Systems and methods of trajectory planning for an autonomous vehicle are disclosed. Exemplary implementations may: determine a first trajectory plan for the vehicle traveling along a first spatial location at a first point in time, the first trajectory plan being a reference trajectory plan; compute an optimal sequence for the vehicle traveling along a second spatial location at a second point in time subsequent the first point in time; and calculate a second trajectory plan for the vehicle by updating the first trajectory plan with information from the computed optimal sequence.

Abstract: Systems and methods are provided for dynamic driver training, and may include: a communication interface to receive sensor data, the sensor data comprising driver biometric data and driver performance data for a driver operating a vehicle; a driver inference circuit to infer a skill level and emotional state of the driver operating the vehicle; and a driver training circuit to, based on the inferred skill level and emotional state of the driver operating the vehicle, dynamically adjust a driver training level for the driver while the driver is operating the vehicle.

Abstract: System, methods, and other embodiments described herein relate to adjusting a prediction model for control at handling limits associated with a projected trajectory during automated driving. In one embodiment, a method includes adjusting parameters of a prediction model using friction estimates and sideslip costs associated with a projected trajectory of a vehicle, the friction estimates being derived from Kalman filtering. The method also includes scaling, using the prediction model, handling limits of the vehicle for the projected trajectory according to a friction circle. The method also includes generating, by the prediction model, vehicle dynamics using a load transfer and a brake distribution, the vehicle dynamics being associated with estimated road conditions and the handling limits. The method also includes outputting, by the prediction model using the vehicle dynamics, a driving command to the vehicle for the projected trajectory.

Abstract: Systems and methods of controlling a vehicle in a stable drift are provided. With the goal of enhancing the driver experience, the disclosed drift control systems provide an interactive drift driving experience for the driver of a vehicle. In some embodiments, a driver is allowed to take manual control of a vehicle after a stable drift is initiated. For safety reasons, an assisted driving system may provide corrective assistance to prevent the vehicle from entering an unstable/unsafe drift. In other embodiments, an autonomous driving system retains control of the vehicle throughout the drift. However, the driver may perform “simulated drift maneuvers” such as counter-steering, and clutch kicking in order to communicate their desire to drift more or less aggressively. Accordingly, the autonomous driving system will effectuate the driver's communicated desire in a manner that keeps the vehicle in a safe/stable drift.

Abstract: A system for training an operator of a vehicle includes a processor and a memory in communication with the processor, which includes a safety module and a training module. The safety module has instructions that, when executed by the processor, cause the processor to determine when the vehicle is operating within a safe area based on at least one of: a location of the vehicle and a location of one or more objects with respect to the vehicle. The training module has instructions that, when executed by the processor, cause the processor to apply at least one brake of the vehicle when the vehicle is operating within the safe area to cause the vehicle to engage in an oversteer event, and collect operator response information when the vehicle engages in the oversteer event.

Abstract: Systems and methods for controlling a vehicle may include receiving sensor data from a plurality of sensors, the sensor data including vehicle parameter information for the vehicle; using the sensor data to determine a vehicle state for a vehicle negotiating a corner, wherein the vehicle state comprises information regarding a magnitude of an effective understeer gradient for the vehicle; computing a yaw moment required to correct the effective understeer gradient based on the magnitude of the effective understeer gradient; and applying a brake torque to a single wheel of the vehicle, wherein an amount of brake torque applied is sufficient to lock up the single wheel to create a yaw moment on the vehicle to achieve the computed yaw moment required to correct the effective understeer gradient.

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 an autonomous vehicle, may include: receiving sensor data, the sensor data comprising vehicle parameter information for the autonomous vehicle; using the sensor data to determine a vehicle state for the autonomous vehicle, wherein the vehicle state comprises information regarding a magnitude of an actual or predicted effective understeer gradient for the vehicle; computing a yaw moment required to correct the effective understeer gradient based on the magnitude of the effective understeer gradient; and determining a combination of one or more vehicle control inputs, including applying a brake torque, to correct the effective understeer gradient; applying the brake torque to a single wheel of the vehicle, wherein an amount of brake torque applied is sufficient to lock up the single wheel to create a yaw moment on the vehicle to achieve the computed yaw moment required to correct the effective understeer gradient.