Provided is a system for properly avoiding a waiting-for-entrance congestion caused by vehicles that stop on a road to enter a facility along the road. A map server 3 is configured to: acquire probe information for identifying a traveling lane in which each vehicle is traveling; acquire, based on the probe information, a congestion occurrence condition when traffic congestion repeatedly occurs in a specific lane at a specific location with a predetermined regularity; and generate, based on the congestion occurrence condition, specific lane congestion information about a congestion status in the specific lane. A control device of a vehicle acquires the specific lane congestion information delivered from the map server, and controls, based on the specific lane congestion information, traveling of the vehicle so as to avoid traffic congestion that is predicted to occur in the specific lane.

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, 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.

Autonomous driving includes evaluating information about an environment surrounding a vehicle, generating, based on the evaluation of the information about the environment surrounding the vehicle, a driving plan for performing a driving maneuver, and operating vehicle systems in the vehicle to perform the driving maneuver according to the driving plan. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles. Under the driving plan, a driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehiclesAutonomous driving includes identifying a driving behavior of a vehicle based on an evaluation of information about manual operation of the vehicle and information about an environment surrounding the vehicle, and operating vehicle systems in the vehicle to perform a driving maneuver according to a driving plan for performing the driving maneuver. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and operating the vehicle systems to perform the driving maneuver according to the driving plan in response to identifying that the driving behavior of the vehicle does not match the predominating driving behavior of the like population of reference vehicles. Under the driving plan, the driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.

Providing user assistance in a vehicle includes identifying a driving behavior of the vehicle based on an evaluation of information about manual operation of the vehicle and information about an environment surrounding the vehicle, and issuing an alert to a user prompting the user to implement corrective manual operation. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing the alert to a user prompting the user to implement corrective manual operation in response to identifying that the driving behavior of the vehicle does not match the predominating driving behavior of the like population of reference vehicles. Under the corrective manual operation, the driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.

A method for determining event data including: sampling a first data stream within a first time window at a first sensor of an onboard vehicle system coupled to a vehicle, extracting interior activity data from the first data stream; determining an interior event based on the interior activity data; sampling a second data stream within a second time window at a second sensor of the onboard vehicle system; extracting exterior activity data from the second image stream; determining an exterior event based on the exterior activity data; correlating the exterior event and the interior event to generate combined event data; automatically classifying the combined event data to generate an event label; and automatically labeling the first time window of the first data stream and the second time window of the second data stream with the combined event label to generate labeled event data.

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 for monitoring and managing an Automated Driving System, ADS, of a vehicle is proposed herein. In more detail, the disclosure proposes a system in the vehicle to discern the state of the environment and map it towards a statistical model used for safety assurance. Given this data it is further proposed to conduct updates to the statistical model in the ADS and to transmit the updates of the model rather than the entire sensor data, the trajectories of the surrounding objects or detailed measurements of the environment. Thus, the proposed solution allows for capturing all the events that the ADS is exposed to for updating the global model used for safety assurance of all the ADSs in the vehicle fleet.

An approach is provided for operating a vehicle using vulnerable road user data. The approach, for example, involves determining a road link on which the vehicle is traveling or expects to travel. The approach also involves querying a geographic database for a vulnerable road user attribute of the road link. The approach further involves providing a notification to activate or deactivate an autonomous driving mode of the vehicle, a vehicle sensor of the vehicle, or a combination thereof while the vehicle travels on the road link based on determining that the vulnerable road user attribute meets a threshold criterion.

The present disclosure provides a data acquisition method, apparatus, device and computer readable storage medium. According to the embodiments of the present disclosure, determination is made as to whether a preset stable driving condition is met based on acquired driving scene data of the driving scene where the vehicle is currently located, and acquired driving behavior data; if the preset stable driving condition is met, the driving scene data and the driving behavior data are acquired at a frequency lower than a preset sampling frequency. As a result, it is possible to reduce data redundancy in similar scenes and similar diving modes, reduce the amount of data, reduce occupation of the storage resources and transmission resources and facilitate subsequent analysis, and it is possible to implement the dynamic acquisition of driving scene data and driving behavior data by scenes and modes.

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.