A method for performing an at least partially automatic vehicle function of a vehicle depending on a travel route to be assessed by means of a driver assistance system is disclosed. The method comprises providing a plurality of clusters from route data with respect to at least one known travel route, wherein the clusters group the route data sectionwise according to predefined geometric parameters. The method comprises providing recorded course data that indicate a course of the travel route to be assessed and applying the clusters to the course data in order to divide the travel route to be assessed into route sections corresponding to the clusters. The method comprises determining at least one uncertainty quantity which is characteristic of an uncertainty with respect to the assignment made and determining a control quantity as a function of the uncertainty quantity and providing the control quantity for performing the vehicle function.

A control device for a hybrid vehicle is provided with a driving plan preparing part preparing a driving plan setting one or more via-points on a projected route from a starting point to a destination to divide the projected route into a plurality of driving routes and divide the driving routes further into pluralities of driving sections and setting which driving mode of an EV mode or HV mode to drive over in each driving section and with a driving mode switching part switching the driving modes according to a driving plan. The driving plan preparing part is configured to be able to prepare a driving plan setting the driving modes of all driving sections in at least one driving route to the EV mode.

A device for generating a trajectory outputs, from first data generated by a first sensor, first object information indicating the position of an object in an area around the vehicle at generation of the first data and first predictive object information indicating a predicted position of an object in the area around the vehicle at a predetermined period after generation of the first data; outputs second object information indicating the position of an object in an area around the vehicle from second data generated by a second sensor; and generates a trajectory along which the vehicle will travel, using the first object information until the predetermined period before a changing time at which an autonomous driving level will change, using the first predictive object information from the predetermined period before the changing time until the changing time, and using the second object information after the changing time.

A driving assist device includes a driving assist controller. The driving assist controller is configured to perform driving assistance when the vehicle changes a course by crossing the oncoming lane. The driving assist controller sets a predicted travel region based on behavior of the oncoming vehicle when the oncoming vehicle is approaching the vehicle that enters an intersection. When the vehicle enters the predicted travel region in a travel path of the vehicle, the driving assist controller determines whether there is a possibility of contact between the vehicle and the oncoming vehicle based on the predicted travel region and the travel path of the vehicle. The driving assist controller causes the vehicle to stop outside of the predicted travel region when there is a possibility of the contact.

A surrounding environment recognition device recognizes a surrounding environment around a vehicle. A traveling control unit centrally controls a whole of the entire vehicle. An obstacle presumer presumes presence of an obstacle ahead of the vehicle based on a behavior distribution of preceding vehicles recognized by the surrounding environment recognition device, and estimates a position and an area where the obstacle is present when the presence of the obstacle is presumed. A traveling path calculator calculates candidates for traveling path areas along which the vehicle is expected to travel while avoiding collision with the presumed obstacle. A traveling path selector selects a traveling path area from among the calculated traveling path areas and sets the selected traveling path area. The traveling control unit controls the vehicle to travel along the set traveling path area.

A driving assist device includes a driving assist controller. The driving assist controller includes an oncoming vehicle detection unit, a prediction determination unit, a predicted travel region setting unit and a stop controller. The oncoming vehicle detection unit is configured to, when a vehicle enters an intersection, determine whether an oncoming vehicle going to enter the intersection is present. The prediction determination unit is configured to, when the oncoming vehicle detection unit determines that the oncoming vehicle is going to enter the intersection, determine whether a course of the oncoming vehicle is predictable based on vehicle behavior of the oncoming vehicle. The predicted travel region setting unit is configured to set a predicted travel region of the oncoming vehicle based on the vehicle behavior. The stopping controller is configured to cause the vehicle to stop outside of the predicted travel region set by the predicted travel region setting unit.

Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.

Particular embodiments may enable configuring settings of a vehicle in a designated mode. A signal to place the vehicle in a designated mode may be received. A roll angle and a pitch angle of the vehicle as parked may be assessed based on data received from a position sensor built into the vehicle. Signals to adjust an electronically controlled suspension of the vehicle to reduce the roll angle or the pitch angle so that the vehicle is level as parked may be sent based on the assessed roll angle and pitch angle exceeding a threshold value. One or more settings of the vehicle to change default operating characteristics by the vehicle while in the designated mode may be modified.

A method for predicting a failure of a power control unit of a vehicle is provided. The method includes obtaining data from a plurality of sensors of the power control unit of a vehicle subject to simulated multi-load conditions, implementing a machine learning algorithm on the data to obtain machine learning data, obtaining new data from the plurality of sensors of power control unit of the vehicle subject to real multi-load conditions, implementing the machine learning algorithm on the new data to obtain test data, predicting a failure of the power control unit based on a comparison between the test data and the machine learning data.

Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P.sub.1, P.sub.2, P.sub.3) and arrival times of the vehicles (1, 18) at the discretization points (P.sub.1, P.sub.2, P.sub.3) are utilized to generate constraints for the model predictive control of the first vehicle (1).