A method of distributing driving force of a four wheel drive (4WD) eco-friendly vehicle includes determining a first allowable range of driving force for each driving force based on determination of travel stability, determining a second allowable range of driving force for each driving wheel based on system limitations of at least one of the first driving source or the second driving source, determining a range of available driving force of the first driving wheel based on the first allowable range of driving force and the second allowable range of driving force, determining first target driving force of the first driving wheel in consideration of efficiency of the first driving source within the range of available driving force, and determining second target driving force of the second driving wheel based on the first target driving force and requested torque.

Systems and methods are disclosed for collecting driving data from simulated autonomous driving vehicle (ADV) driving sessions and real-world ADV driving sessions. The driving data is processed to exclude manual (human) driving data and to exclude data corresponding to the ADV being stationary (not driving). Data can further be filtered based on driving direction: forward or reverse driving. Driving data records are time stamped. The driving data can be aligned according to the timestamp, then a standardized set of metrics is generated from the collected, filtered, and time-aligned data. The standardized set of metrics are used to grade the performance the control system of the ADV, and to generate an updated ADV controller, based on the standardized set of metrics.

An automotive electronic dynamics control system for an automated motor-vehicle. The electronic dynamics control system is designed to implement two distinct Model Predictive Control (MPC)-based Trajectory Planners comprising a Longitudinal Trajectory Planner designed to compute a planned longitudinal trajectory for the automated motor-vehicle; and a Lateral Trajectory Planner designed to compute a planned lateral trajectory for the automated motor-vehicle. The electronic dynamics control system is further designed to cause the planned longitudinal trajectory to be computed before the planned lateral trajectory.

A method for scoring driving behavior using vehicle inputs and outputs is implemented in an electronic device. The method includes obtaining historical input data and output data of a vehicle; establishing an output regression model according to the historical output data; determining a boundary of the output regression model; establishing an input regression model according to the historical input data; determining a boundary of the input regression model by calculating boundary limits of the input regression model; obtaining real-time input data and output data of the vehicle; calculating a first ratio of data points outside the boundary of the input regression model to total data points in the real-time input data, and a second ratio of data points outside the boundary of the output regression model to total data points in the real-time output data; scoring driving behavior of a driver according to the first ratio and the second ratio.

The disclosure relates to the process of ascertaining an input variable of a vehicle actuator using a model-based predictive control. According to one exemplary arrangement, a processor unit is designed to access trajectory information and a state data set, which represents a state of surroundings of a vehicle and/or the state of the vehicle and/or a driving state of the vehicle, by an interface. The processor unit carries out a secondary condition algorithm in order to calculate a secondary condition and an MPC algorithm for a model-based predictive control. By carrying out the secondary condition algorithm, a secondary condition is ascertained for the MPC algorithm on the basis of the trajectory information and on the basis of the state data set. By carrying out the MPC algorithm, an input variable is ascertained for an actuator of the vehicle on the basis of the secondary condition. This is carried out in particular such that in a future predicted trajectory, the vehicle follows the specified trajectory with a specified degree of reliability.

The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

Systems providing a comparison of results of simulations of operation of a simulated autonomous vehicle may include a processor to: perform a first simulation of operation of a simulated autonomous vehicle based on first autonomous vehicle control code, receive second autonomous vehicle control code that includes a second version of software code associated with controlling operations of the simulated autonomous vehicle, the second version including a modification of a first version of software code associated with controlling operations of the simulated autonomous vehicle, perform a second simulation of operation of the simulated autonomous vehicle based on the second autonomous vehicle control code, and display an indication that second values of one or more metrics that resulted from the second simulation are different from one or more first values of the one or more metrics that resulted from the first simulation. Methods, computer program products, and autonomous vehicles are also disclosed.

A lane departure suppression device including a control unit that executes a lane departure suppression control (automatic steering of the steering wheel and/or a warning being issued) when it is determined that there is a possibility that a vehicle departs from a lane. The control unit does not execute the lane departure suppression control when the control unit determines that a lateral speed and a lateral acceleration of the vehicle are increased within a predetermined time from a time point at which acceleration and deceleration of the vehicle is started, and that there is an adjacent lane on a side with respect to the lane in which the lateral speed and the lateral acceleration are increased.

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.

Devices, systems, and methods related to prediction of tire performance using existing CAN data to improve overall vehicle performance. Machine learning tools are applied to CAN data, for example pilot data and/or vehicle dynamics data, to predict tire performance factors for use in a vehicle control system to provide vehicle lateral guidance control.