Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Yan Ming Jonathan Goh

Avinash Balachandran

Michael Thompson

Alexander Russell Green

John Subosits

Manuel Ludwig Kuehner

Hiroshi Yasuda

Franck Djeumou

Kazunori Nimura

Minoru Brandon Araki

Hanh Nguyen

System, methods, and other embodiments described herein relate to tire models based on neural-ExpTanh parameterization. In one embodiment, a method includes operating a vehicle with a control framework incorporating an ExpTanh function; calculating prior slip data based on measurements obtained by the control framework; selecting a confidence parameter; using a first predictive model to determine ExpTanh parameters based upon the prior slip data, the measurements, and the confidence parameter; and inputting a slip parameter and the ExpTanh parameters into the ExpTanh function to estimate a tire force.

Systems and methods for neural-EXPTANH learned tire models

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

System, methods, and other embodiments described herein relate to NODE learned tire models. In one embodiment, a method includes calculating estimated tire forces based on vehicle measurements; solving a second order differential equation in a repetitive manner until an error calculation based on a tire force function and the estimated tire forces reaches a minimum value, by: using a first predictive model to provide one or more inflection points and initial conditions based on the vehicle measurements, using a second and third predictive model to act as, respectively, exponents to a positive and a negative exponential equation based on the one or more inflection points, the initial conditions, and the vehicle measurements, and integrating the exponential equations to obtain the tire force function; and applying the tire force function to new vehicle measurements to estimate current tire forces.

Systems and methods for neural ordinary differential equation learned tire models

System, methods, and other embodiments described herein relate to steering a vehicle based during loss of traction. In one arrangement, a method for steering a vehicle during loss of traction is disclosed. The method includes, responsive to detecting a slipping tire of a vehicle losing traction with a road, automatically steering the vehicle separately from an input of a steering wheel of the vehicle to cause the vehicle to follow a path. The method also includes decoupling control of a pair of front tires of the vehicle by the steering wheel. The method further includes rotating, independently of an input to the steering wheel and in parallel with steering the vehicle, the steering wheel to match an actual yaw of the vehicle.

Automated steering system during loss of traction

Systems, methods, and other embodiments described herein relate to steering a vehicle based on an intended yaw rate during loss of traction. In one arrangement, a method for steering a vehicle during loss of traction is disclosed. The method includes detecting a slipping tire of a vehicle losing traction with a road. The method also includes decoupling control of a pair of front tires of the vehicle by a steering wheel of the vehicle. The method also includes identifying an intended yaw rate for controlling the vehicle by detecting an angle of the steering wheel while the slipping tire does not have traction. The method further includes steering the vehicle separately from an input of a steering wheel of the vehicle to cause intended yaw rate.

Steering system based on intended yaw rate during loss of traction

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

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 are provided for controlling one or more brakes of the vehicle. The system may include a processor and a memory in communication with the processor with a brake control module. The brake control module includes instructions that, when executed by the processor, cause the processor to control the one or more brakes of the vehicle when the vehicle is in a first mode using a brake-by-wire system. When the vehicle is in a second mode, control the brake-by-wire system such that the one or more brakes of the vehicle are controlled using a mechanical braking system and the brake-by-wire system.

System and method for controlling one or more brakes of a vehicle

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, methods, and other embodiments described herein relate to emergency lateral maneuvers using brake-induced tire sliding. In one embodiment, a method includes determining a vehicle state for a vehicle according to sensor data about a surrounding environment. The method includes computing, using the sensor data and the vehicle state, lateral accelerations that are yaw-free for the vehicle. The method includes, in response to detecting that the vehicle state is associated with an emergency event, selecting a maneuver from the lateral accelerations. The method includes controlling the vehicle according to the maneuver.

Emergency maneuvering using lateral sliding

Systems and methods of controlling a clutch in a vehicle are provided. With the goal of enabling autonomous/assisted control of the clutch by an electronic control unit while preserving the familiar mechanical feeling at the clutch pedal that driving enthusiasts prefer, embodiments of the disclosed technology use a shuttle valve to blend control of clutch engagement between a driver and an ECU. In these embodiments, a clutch pedal in the vehicle may be mechanically connected to a piston in a first hydraulic cylinder (just like in a traditional mechanical/hydraulic clutch actuation system), and an ECU may actuate a second hydraulic cylinder. Accordingly, a shuttle valve may be used to route the fluid coming from the cylinder with the greater pressure (i.e. the driver actuated cylinder or the ECU actuated cylinder), to a third hydraulic cylinder which adjusts engagement of a clutch by a mechanical linkage.

Growing community of inventors

Palo Alto, CA, United States of America

Yan Ming Jonathan Goh