Apparatuses, systems, methods, and computer-readable media are provided for the preemptive logical configuration of vehicle control systems. A vehicle control computer may determine a location of a vehicle. The vehicle control computer may query a historical data source server for historical incident data corresponding to a first vicinity around the first location of the vehicle. Based on the historical incident data, the vehicle control computer may identify one or more driving danger areas in the first vicinity around the first location of the vehicle, wherein each of the one or more driving danger areas are associated with one or more driving hazards. The vehicle control computer may generate a configuration for vehicle operation in the first vicinity around the first location of the vehicle based on the one or more driving danger areas and may update driving logic of the vehicle with the configuration.

A method of processing data for a driving automation system, the method comprising steps of: obtaining image data from a camera of an autonomous vehicle, AV; image processing the image data to obtain a vehicle registration mark, VRM, of another vehicle within the surrounding area of the AV; looking up the VRM in a vehicle information database to obtain information indicative of the make, the model and the date of manufacture of the other vehicle; looking up information indicative of the make, the model and the date of manufacture of the other vehicle in a vehicle dimensions database to obtain at least one dimension of the other vehicle; and updating a context of the autonomous vehicle based on said at least one dimension of the other vehicle.

A method, a computer program with instructions, and an apparatus for configuring a control system for an at least partially autonomous transportation vehicle. Data relating to the driving situation are captured, selection criteria are then determined from the available data, two or more AI modules are selected from a library of AI modules during driving based on selection criteria, and combined execution of the selected two or more AI modules by the control system is initiated.

A learning device includes a planner configured to generate information indicating an action of a vehicle, and a reward deriver configured to derive a plurality of individual rewards obtained by evaluating each of a plurality of pieces of information to be evaluated, which include feedback information obtained from a simulator or an actual environment by inputting information based on the information indicating the action of the vehicle to the simulator or the actual environment, and derive a reward for the action of the vehicle on the basis of the plurality of individual rewards. The planner performs reinforcement learning that optimizes the reward derived by the reward deriver.

A server includes a processor, programmed to receive a vehicle location from a vehicle, receive a weather condition of the vehicle location from a cloud server, responsive to receiving a calendar of a user including an event, calculate a departure time based on the vehicle location and a location of the event, responsive to receiving a desired cabin temperature of the user from a mobile device, calculate a minimum time for the vehicle to reach the desired cabin temperature based on the weather condition, calculate a vehicle start time using the departure time and the minimum time, and send an instruction to the vehicle to start at the vehicle start time.

A control method is provided for controlling a lateral position of a motor vehicle. The control method includes calculating a sighting distance of a detector means embedded in the vehicle, calculating a first component of a steering angle setpoint of a steered wheels of the vehicle, and calculating a second component of the steering angle setpoint. The first component is an open loop component of a control system, while the second component is a closed loop component of the control system. The first component is weighted by a gain that is a decreasing function of the sighting distance.

A driving assistance device to properly recognize a relative positional relationship between mobile objects without the use of map information. The driving assistance device includes a relative position judgment section for judging a relative positional relationship of an object of interest and a first neighboring object relative to a second neighboring object, based on object-of-interest information and neighbor information, to make a judgment as a first judgment on a relative positional relationship between the object of interest and the first neighboring object, based on the aforementioned judgment result.

A method adapts vehicle functionality that is automatically carried out by an assistance system. In a method in which misunderstandings between driver and vehicle are prevented in predetermined driving situations, when the driver aborts the automatically carried out functionality, this abortion is recorded and saved. If the abortion frequency exceeds a predetermined threshold value, the aborted functionality is replaced by a modified or new functionality.

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

In a turn signal cancelation system for a two-wheeled vehicle, a controller is adapted to automatically cancel the turn signal of the vehicle upon: (a) a determined distance traveled by the vehicle after an occurrence of the predetermined operation of the vehicle exceeding a first distance threshold and a determined yaw angle traversed by the vehicle after the occurrence of the predetermined operation of the vehicle exceeding a yaw angle threshold; and (b) the determined distance traveled by the vehicle after the occurrence of the predetermined operation of the vehicle exceeding a second distance threshold, different from the first distance threshold, independent of the determined yaw angle traversed by the vehicle after the occurrence of the predetermined operation of the vehicle.