The present invention discloses a method, apparatus, device and computer-readable medium for trajectory compression, and relates to the technical field of computers. One specific implementation of the method comprises: screening, among trajectory points of a traveling trajectory, reserved trajectory points of the traveling trajectory according to deviation degrees of the trajectory points from roads; determining, in conjunction with traveling periods of the reserved trajectory points, the reserved trajectory points as key trajectory points, the traveling period being a time interval from a previous trajectory point of the reserved trajectory point to a next trajectory point of the reserved trajectory point; adding the starting point of the traveling trajectory, the key trajectory points and the ending point of the traveling trajectory to a compressed trajectory so as to compress the traveling trajectory. This implementation can reduce the time complexity of compression of trajectory data, and thereby decrease the time consumption for compression.

A system for monitoring the condition of a road surface travelled by a plurality of vehicles, each including at least one sensor, is provided. The system includes a central processing arrangement arranged to: map at least a part of the road surface with a number of cells; receive road surface data for the cells, which road surface data is based on measurements made by the sensors as the plurality of vehicles travel on the road surface; and calculate a probability for at least one road surface parameter for each cell travelled by the plurality of vehicles based at least on road surface data received from the plurality of vehicles.

The disclosure includes embodiments for a sensor system for multiple perspective sensor data sets. In some embodiments, a method executed by an ego vehicle includes receiving, from a set of remote vehicles, a set of sensor data describing a set of preliminary heatmaps for a roadway environment. The method includes reconciling discrepancies in the set of heatmaps and a preliminary heatmap of the ego vehicle to form a combined heatmap that describes the objects in the roadway environment as collectively observed by the onboard sensors of the set of remote vehicles and the ego vehicle. The method includes providing the combined heatmap to one or more of the remote vehicles included in the set of remote vehicles. The method includes modifying an operation of the ego vehicle based on the combined heatmap. For example, an operation of an autonomous driving system of the ego vehicle is modified.

Embodiments include methods performed by a processor of a vehicle control unit for managing a driving condition anomaly. In some embodiments, the vehicle may receive a first driving condition based on data from a first vehicle sensor, receive a second driving condition based on data from another data source, determine a driving condition anomaly based on the first driving condition and the second driving condition, send a request for information to a driving condition database remote from the vehicle, receive the requested information from the driving condition database, and resolve the driving condition anomaly based on the requested information from the driving condition database.

Systems and methods are provided for creating more organic lane change models for autonomous or semi-autonomous operation of a vehicle. A plurality of data associated with a plurality of driver-performed lane change maneuvers is collected from a plurality of different vehicle. Driver-performed lane change maneuvers are discarded when determined to fall outside a threshold of safety. A generic model is generated from the non-discarded data for average lane change maneuvers. Specific models can be generated for different drivers, vehicle types, and other metrics by comparison with the generic model.

A method for supporting a vehicle drive of a vehicle in driving of the vehicle during travel is disclosed, comprising the following steps: providing inattentiveness information from multiple vehicle drivers of vehicles, wherein the inattentiveness information characterizes at least one type of inattentiveness with regard to traffic observation of a vehicle driver on at least one route section of a roadway, determining a current position of the vehicle by during travel, providing the position of the at least one route section and/or detecting the position of the route section, supporting the vehicle driver of the vehicle in driving of the vehicle by supporting the attentiveness of the vehicle driver in the zone around the route section if the current position of the vehicle is identified as being in said zone around the route section.

A method for estimating an accident risk of an autonomous driving unit produces helpful results with fewer autonomous driving cycles. From driving values, which have been determined from monitoring at least one driving parameter of the driving unit during autonomous driving, an autonomous-driving quantity is determined quantifying an autonomous-driving quality of the driving of the driving unit. The autonomous-driving quantity is associated with a plurality of manual-driving quantities, which have been determined from the same driving parameter during manual driving periods of different driving units. An autonomous-driving accident rate value is determined from accident rate values associated to those manual-driving quantities.

An autonomous driving prediction method based on big data, wherein the autonomous driving prediction method based on big data includes steps of: providing a plurality of prediction algorithm models associated with a target road; obtaining sensing data of sensors, the sensing data including a current position of the autonomous driving vehicle, surrounding environment data of the autonomous driving vehicle, and, driving data of the autonomous driving vehicle; obtaining current scene data of the autonomous driving vehicle; loading the optimal prediction algorithm model; calculating current scene data of the autonomous driving vehicle by the optimal prediction algorithm model to obtain prediction data; generating a control command based on the prediction data; and controlling the autonomous driving vehicle to drive according to the control command.

Embodiments include methods performed by a processor of a vehicle control unit for managing a driving condition anomaly. In some embodiments, the vehicle may receive a first driving condition based on data from a first vehicle sensor, receive a second driving condition based on data from another data source, determine a driving condition anomaly based on the first driving condition and the second driving condition, send a request for information to a driving condition database remote from the vehicle, receive the requested information from the driving condition database, and resolve the driving condition anomaly based on the requested information from the driving condition database.

A method and an apparatus for automated driving, a device, and a computer-readable storage medium are provided. The method for automated driving includes: obtaining data associated with a parameter and an external environment of a vehicle, the data being collected according to a collection policy for a target scenario for the vehicle; generating an automated driving model for the target scenario based on the obtained data; and updating the collection policy by testing the automated driving model.