In an embodiment, a method comprises detecting, at a processor of an autonomous vehicle, a discrepancy between a map and a property sensed by at least one sensor onboard the autonomous vehicle, the property being associated with an external environment of the autonomous vehicle. In response to detecting the discrepancy, and based on the discrepancy, an annotation for the map is generated via the processor. A signal representing the annotation is caused to be transmit to a compute device that is remote from the autonomous vehicle. A signal representing a map update is received from the compute device that is remote form the autonomous vehicle. The map update is generated based on the annotation, the map update (1) including replacement information for a region of the map associated with the annotation, and (2) not including replacement information for a remainder of the map.

The information processing device (100) includes a storing unit (120) that stores parameter information (D1) concerning parameters used for detection of the surrounding environment of a mobile body (500) and a setting unit (132) that sets, based on dynamic information (a dynamic map D100) detected outside the mobile body (500) and the parameter information (D1), the parameters used for the detection of the surrounding environment of the mobile body (500).

The information processing device (100) includes a storing unit (120) that stores parameter information (D1) concerning parameters used for detection of the surrounding environment of a mobile body (500) and a setting unit (132) that sets, based on dynamic information (a dynamic map D100) detected outside the mobile body (500) and the parameter information (D1), the parameters used for the detection of the surrounding environment of the mobile body (500).

One or more computing devices, systems, and/or methods for generating profiles, such as weather profiles, based upon signals from vehicle devices are provided. In an example, a plurality of vehicle measure signals is received from a plurality of vehicle devices. A first vehicle measure signal of the plurality of vehicle measure signals is received from a first vehicle device, of the plurality of vehicle devices, connected to a first vehicle. The first vehicle measure signal is indicative of a first location of the first vehicle and one or more first vehicle measures determined using one or more first sensors of the first vehicle. A vehicle measure profile associated with a region is generated based upon the plurality of vehicle measure signals. The vehicle measure profile is indicative of one or more second vehicle measures at one or more locations within the region.

One or more computing devices, systems, and/or methods for generating profiles, such as weather profiles, based upon signals from vehicle devices are provided. In an example, a plurality of vehicle measure signals is received from a plurality of vehicle devices. A first vehicle measure signal of the plurality of vehicle measure signals is received from a first vehicle device, of the plurality of vehicle devices, connected to a first vehicle. The first vehicle measure signal is indicative of a first location of the first vehicle and one or more first vehicle measures determined using one or more first sensors of the first vehicle. A vehicle measure profile associated with a region is generated based upon the plurality of vehicle measure signals. The vehicle measure profile is indicative of one or more second vehicle measures at one or more locations within the region.

An apparatus, method and computer program product are provided for determining a state of visibility for a road object. In one example, the apparatus receives temporal data, calculates an orientation of a light source with respect to a road object using the temporal data, and predicts a state of visibility for the road object based on the orientation of the light source. In another example, the apparatus determines an artificial light source associated with a road object, receives attribute data associated with the artificial light source, determines a state of the artificial light source using the attribute data, and predicts a state of visibility for the road object based on the state of the artificial light source.

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.

A computer-implemented method for determining an estimate of the capability of a vehicle driver to take over control of a vehicle, wherein the method comprises: determining at least one estimation parameter, the at least one estimation parameter representing an influencing factor for the capability of the vehicle driver to take over control of the vehicle; and determining an estimate on the basis of the at least one estimation parameter by means of a predefined estimation rule, the estimate representing the capability of the vehicle driver to take over control of the vehicle.

A computer-implemented method for determining an estimate of the capability of a vehicle driver to take over control of a vehicle, wherein the method comprises: determining at least one estimation parameter, the at least one estimation parameter representing an influencing factor for the capability of the vehicle driver to take over control of the vehicle; and determining an estimate on the basis of the at least one estimation parameter by means of a predefined estimation rule, the estimate representing the capability of the vehicle driver to take over control 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.