Provided are an automatic parking control method and apparatus, relating to the industrial field of vehicles. The method comprises: when a speed of a vehicle is in a preset speed range and a time that the speed of the vehicle is in the preset speed range is greater than or equal to a preset time, collecting image data and radar data around the vehicle; inputting the image data and the radar data into a preset convolutional neural network model, and outputting at least one parking slot information; selecting target parking slot information according to a received parking-in selection operation; and according to the target parking slot information, generating a vehicle parking-in track for the vehicle to automatically park according to the vehicle parking-in track.

A sensor information fusion method of an embodiment includes selecting target tracks having distances from a reference track within a predetermined distance from among tracks obtained by a plurality of sensors, and selecting a target fusion track to be fused with the reference track on the basis of sums of first calculation values calculated according to distances between the selected target tracks and the reference track and second calculation values calculated according to overlapping areas of the target tracks and the reference track.

A method for supplying data for an at least partially automated vehicle. The method including: detecting the sensor data of an infrastructure; fusing the sensor data to form an environment model; wireless transmission of the environment model data to the at least partially automated vehicle; and wireless transmission of context data, relating to a location of the at least partially automated vehicle, to the at least partially automated vehicle, it being possible to evaluate the environment model data in a defined manner with the aid of the context data.

Vehicle sensor data processing methods, apparatuses and system are provided. In an example method, some sensors are configured to output sensed raw data, and other sensors are configured to output target information determined based on sensed raw data. A fusion component performs data fusion after receiving the raw data and the target information from the sensors, to determine target information.

Technologies and techniques for the lateral control of a vehicle. Environment data of a vehicle is detected when travelling a route, and stored environment data, detected when travelling the route by a plurality of other vehicles not currently travelling the route, is received. The plausibility of the stored environment data is checked on the basis of the environment data detected. Lateral control of the vehicle is executed on the basis of the environment data checked for plausibility. The present disclosure also relates to an arrangement for the lateral control of a vehicle.

An embodiment sensor information fusion method includes estimating, by a host vehicle, reliability of shared sensor fusion information received from a neighboring vehicle, the shared sensor fusion information being generated by the neighboring vehicle, and generating, based on the estimated reliability, fusion track information of an object located near the host vehicle or the neighboring vehicle using host vehicle sensor fusion information generated by the host vehicle and the shared sensor fusion information.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

The vehicle assistance system includes an infrastructure sensor installed in a predetermined place to detect an obstacle, an onboard apparatus connected to an onboard sensor which is mounted on the vehicle and which detects an obstacle, and a server installed in a place different from that of the infrastructure sensor. In the infrastructure sensor, the abnormality determination unit compares the first obstacle information related to the obstacle detected by the sensor unit and the second obstacle information related to the obstacle detected by the onboard sensor existing in a location different from that of the sensor unit, and determines whether or not the sensor unit or the onboard sensor is abnormal based on the result of this comparison.

Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

A method for monitoring a data fusion function of an infrastructure system for the infrastructure-supported assistance of motor vehicles during an at least semi-automated driving task within an infrastructure, the infrastructure including multiple infrastructure surroundings sensors for detecting an area of the infrastructure. The method includes: receiving multiple input data sets intended for the data fusion function, each of which includes surroundings data based on the respective detection of the area, which represent the detected area; receiving output data based on a data fusion of the input data sets, output by the data fusion function; checking the input data sets and/or the output data for consistency; outputting a check result of the check. A device, a computer program, and a machine-readable memory medium are also provided.