A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines that the railroad crossing is closed from the sensor data. The control device determines one or more re-routing options from map data. The control device determines whether at least one re-routing option reaches a predetermined destination of the autonomous vehicle. In response to determining that the at least one re-routing option reaches the predetermined destination, the control device selects a particular re-routing option based at least on determining that the autonomous vehicle is able to travel according to the particular re-routing option autonomously. The control device instructs the autonomous vehicle to re-route according to the particular re-routing option.

A method for controlling torque reduction of a hybrid electric vehicle including a motor and an engine as a power source includes: determining whether a request for a driving torque limit by a traction control system (TCS) is generated; calculating a motor torque command when the request for the driving torque limit by the TCS is generated; calculating an engine torque command based on the calculated motor torque command; calculating an available amount of charging of the motor according to a state of charge (SOC) of a battery of the hybrid electric vehicle; and determining a final motor torque command and a final engine torque command based on the calculated available amount of charging of the motor.

A drive mode hierarchy includes drive mode categories and drive modes belonging to each category. Each drive mode defines values for attributes of the drivetrain and/or suspension of a vehicle. Each drive mode may further have towing and non-towing sub-modes with the towing sub-mode being used when a trailer is detected. An interface is provided for selecting drive modes from the hierarchy and modifying the attributes. The vehicle may define a plurality of trailer profiles including attributes of trailers, such as weight and aerodynamic drag. A user may select among trailer profiles and define the attributes of the trailer profiles. Interfaces displayed by the vehicle may vary according to the selected drive mode and may include a chassis view with interface elements displaying real-time information of components of the vehicle, such as wheels, suspension, and battery. Tiles including real-time information may be displayed according to the selected drive mode.

Methods and systems are provided for controlling and diagnosing a mechanical vehicle component. In one example, a method may include determining an input device state and an electric machine torque at a diagnostic controller, and identifying a fault condition based on these determinations. Further, the diagnostic controller may trigger an active fault state of the mechanical vehicle component to avoid unintended vehicle acceleration, particularly at low speeds.

An apparatus for escaping an uneven driving surface for an electric vehicle includes a maximum traction identification portion configured to search for a steering angle at which maximum traction is generated by applying driving torque while rotating the steering wheel. The maximum traction identification portion is also configured to identify maximum wheel torque able to be applied after rotating the steering wheel by the steering angle at which maximum traction is generated. The apparatus also includes an axle weight computation portion configured to repeatedly calculate an axle weight applied to front and rear wheels of a vehicle in which various pitching motions are implemented. The apparatus also includes a driveaway controller configured to receive information from the maximum traction identification portion and the axle weight computation portion and to control escape of the vehicle.

According to the method of estimating a maximum road friction coefficient, artificial braking or driving-related control is conducted and a maximum road friction coefficient is estimated based on a difference in wheel speeds between front and rear wheels, compensated for slip of tires.

A method for controlling the traveling of a vehicle includes determining, by a control unit, a basic torque command based on vehicle operating information collected during traveling of a vehicle; obtaining, by the control unit, vertical load information of a left wheel and a right wheel of the vehicle in real time during traveling of the vehicle based on information collected in the vehicle; determining, by the control unit, a partial braking amount from the determined real-time basic torque command and the obtained real-time vertical load information; and performing, by the control unit, a partial braking control controlled by an inner wheel braking device so that a braking force corresponding to the partial braking amount is applied to a turning inner wheel among the left wheel and the right wheel.

A method of estimating a performance characteristic of a wheel of a vehicle, includes selecting relevant input features based on wheel dynamics and tire behavior, and collecting experimental data for each of the relevant input features at each of a plurality of vehicle operating conditions. The method further includes manually identifying and labeling wheel stability status over time from the experimental data and calculating tractive limit over time from the experimental data. The method also includes training a tractive limit model and training a wheel stability status model. The method further includes receiving a plurality of testing inputs, wherein each of the plurality of testing inputs is received from a sensor on-board the vehicle or from a controller on-board the vehicle and, processing the received testing inputs in a predetermined machine learning process to calculate in one or more data processors a prediction of the performance characteristic.

An apparatus for estimating a vehicle weight includes a memory storing computer-executable instructions and at least one processor that accesses the memory and executes the instructions. The at least one processor applies a real velocity and real acceleration to a trained real-to-virtual transformation model to obtain a virtual output set at a target time point, which includes a virtual gradient of a vehicle, a virtual velocity of the vehicle, and virtual longitudinal acceleration of the vehicle, and applies the virtual output set at the target time point and a real wheel torque at the target time point to a trained mass estimation model to obtain weight information of the vehicle. The real-to-virtual transformation model includes a first sub-model for obtaining an initial virtual velocity, a second sub-model for obtaining the virtual longitudinal acceleration, and a third sub-model for obtaining the virtual gradient.

A computer-implemented method for handling an axle configuration of a vehicle is provided. The axle configuration is indicative of a number of axles of the vehicle. The method includes obtaining vehicle condition data indicative of at least one load applied to the vehicle. The method includes obtaining the axle configuration of the vehicle. The method includes obtaining estimating a current weight of the vehicle based on the obtained vehicle condition data and based on the axle configuration of the vehicle. The method includes obtaining a previously estimated weight of the vehicle. The method includes, based on a difference between the previously estimated weight and the current weight of the vehicle, predicting whether there has been a change in the axle configuration of the vehicle.