An autonomous vehicle may include an autonomous driving control apparatus including a processor that is configured to request remote control of the autonomous vehicle to a control system when the remote control of the autonomous vehicle is required, and when receiving a driving path stored during a previous remote control of the autonomous vehicle from the control system, follows and controls the received driving path.

Systems, apparatuses, and methods disclosed provide for receiving internal information, external static information, and external dynamic information of a hybrid vehicle, and selectively enable or disable a stop/start function for the engine of the hybrid vehicle based on the internal hybrid vehicle information, external static information, and external dynamic information. The stop/start function controls selective activation and deactivation of the engine during operation of the hybrid vehicle.

Method and system for determining a route for a vehicle. The method associates a navigation module to a vehicle fitted with tires and a tire monitoring unit to at least one tire fitted to the vehicle. The monitoring unit has a sensing element to generate a sensing signal descriptive of deformations undergone by the tire. The deformations form a contact area between the tire and a rolling surface on which the tire rotates. During rotation of the tire, the sensing signal, including the sensing signal generated in correspondence of passages of the sensing element through the contact area, is undersampled for a number of passages sufficient to obtain an estimated length of the contact area. The weight of the vehicle is then estimated based on such estimated length, and at least one route among two or more routes is selected, based on such estimated weight of the vehicle.

Systems and methods for vehicle-based weather detection are disclosed herein. The systems and methods can include selecting one or more vehicles from a plurality of vehicles based on one or more network membership parameters. One or more data acquisition networks can be formed using the one or more selected vehicles. Sensor data can be received from the one or more data acquisition networks. One or more weather conditions can be predicted using the sensor data. One or more environmental elements can be updated based on the predicted weather conditions.

The described techniques relate to coordinating and managing faults of systems of a vehicle, such as an autonomous vehicle, to enable the vehicle to respond safely and appropriately to the faults. In examples, a centralized fault monitor system receives faults from different vehicle systems, maps the received faults to associated fault constraints, prioritizes different and shared parameters between the fault constraints, and communicates the constraint parameters to appropriate vehicle systems accordingly.

Provided herein is a system of a vehicle that comprises one or more sensors, one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform: selecting a trajectory along a route of the vehicle; predicting a trajectory of another object along the route; adjusting the selected trajectory based on a predicted change, in response to adjusting the selected trajectory, to the predicted trajectory of the another object, the predicted change to the predicted trajectory of the another object being stored in a model; determining an actual change, in response to adjusting the selected trajectory, to a trajectory of the another object, in response to an interaction between the vehicle and the another object; updating the model based on the determined actual change to the trajectory of the another object; and selecting a future trajectory based on the updated model.

A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, with the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19). Additionally, the processor unit (3) is configured to optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), where the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15) to be minimized.

A method for operating an at least partially automated vehicle. The method includes: supplying surroundings data detected with the aid of sensors to at least three AI computing units which are independent of one another; generating data regarding at least one object from the surroundings data; carrying out a plausibility check of the pieces of data with respect to one another with the aid of majority voting; and using the data for which the plausibility check has been carried out for controlling, at least in a semi-automated manner, a lateral and/or longitudinal guidance of the at least partially automated vehicle.

A vehicle system of a hybrid vehicle includes: a navigation device that searches a movement path to a destination of a vehicle; and a vehicle control device configured to predict driving energy of a road section included in the movement path according to a vehicle speed of the vehicle when the movement path includes an exhaust gas emission restriction zone, predict first consumption State of charge (SOC) value of the battery consumed within the exhaust gas emission restriction zone based on the driving energy for a case in which the vehicle drives in the exhaust gas emission restriction zone without driving the engine, determine a target SOC value of the battery at a time when the vehicle enters the exhaust gas emission restriction zone based on the predicted first consumption SOC, and control the operation of the vehicle.

A control apparatus includes a reception control unit configured to perform control to receive, when a movable object is located at a first point, presence information of a risk area including a plurality of position coordinates specified through image recognition by another movable object, a determination unit configured to determine whether the movable object is in a vicinity of the risk area based on the plurality of position coordinates included in the presence information of the risk area for which the reception control unit has performed the control to receive, a transmission control unit configured to perform control to transmit, when the determination unit determines that the movable object is in the vicinity of the risk area, information related to presence of the movable object, and a control unit configured to execute control of the movable object.