Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Avinash Balachandran

Michael Thompson

Yan Ming Jonathan Goh

John Subosits

Alexander Russell Green

Carrie Bobier-Tiu

Arjun Bhargava

Manuel Ahumada

Selina Pan

Sarah Koehler

Logan Michael Ellis

Huihua Zhao

Manuel Ludwig Kuehner

Hiroshi Yasuda

Frank Permenter

Soonho Kong

Kevin M Zaseck

Minoru Brandon Araki

Carrie G Bobier-tiu

Hanh Nguyen

Yun Jung Lee

Shawn Manuel

Systems and methods are provided for determining vehicle configurations. The system can receive simulation data or actual driving data of a driving track corresponding to a driver and associate the simulation data or actual driving data with one or more driving parameters. A driver style can be determined based on the one or more driving parameters. A vehicle configuration can be determined for a vehicle of the driver based on the determined driver style. The system can display one or more vehicle settings associated with the vehicle configuration on a user interface.

Customizable car setup optimization and strategy based on individual driving style and preferences

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.

Systems and methods for updating the parameters of a model predictive controller with learned controls parameters generated using simulations and machine learning

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.

Adaptive dynamic driver training systems and methods

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.

Systems and methods for updating the parameters of a model predictive controller with learned external parameters generated using simulations and machine learning

A method for controlling a vehicle includes controlling, at a first time period, the vehicle based on a first velocity profile constrained by an acceleration profile that includes a minimum acceleration, a maximum acceleration, and a maximum velocity. The method also includes generating an adjusted acceleration profile by adjusting one or more of the minimum acceleration, the maximum acceleration, or the maximum velocity associated with the acceleration profile based on determining an object is approaching the vehicle. The method further includes controlling, at a second time period, the vehicle based a second velocity profile constrained by the adjusted acceleration profile.

Driver-centric model predictive controller

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.

Systems and methods for automatically generating solver code for nonlinear model predictive control solvers

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.

Purposeful brake-induced wheel lockup for vehicle stability control in autonomous vehicles

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.

Vehicle dynamics emulation

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.

Vehicular performance enhancement

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.

Systems and methods for skid recovery using front wheel slip

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.

Operator drift maintenance

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.

System and method for training an operator of a vehicle

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.

Purposeful brake-induced wheel lockup for vehicle stability control

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. Exemplary implementations may: generate output signals conveying operational information regarding the vehicle; generate output signals conveying contextual information regarding the vehicle; determine, based on the output signals, the operational information; determine, based on the output signals, the contextual information; determine, based on the operational information and/or the contextual information, a current vehicle state of the vehicle; determine, based on the current vehicle state of the vehicle, a future vehicle state; determine, based on the operational information, predicted boundaries of the operational information; determine, based on the operational information and contextual information, trajectory threshold values of trajectory metric; and control the vehicle partially, based on the future vehicle states determined, the predicted boundaries, and the trajectory threshold values.

Growing community of inventors

Sunnyvale, CA, United States of America

Avinash Balachandran