A lead autonomous vehicle (AV) includes a sensor configured to observe a field of view in front of the lead AV. Following AVs are on the same road behind the lead AV. A processor of the lead AV is configured to detect a construction zone. The processor sends a first message to an operation server, indicating that the construction zone is detected. The processor updates driving instructions of the lead AV to navigate around the construction zone. While navigating around the construction zone, the processor sends sensor data associated with the construction zone to the operation server. The operation server determines an extent of the construction zone. If the operation server determines that the construction zone is extensive, it sends re-routing instructions to the AVs. If the operation server determines that the construction zone is not extensive, it sends instructions to navigate around the construction zone to the AVs.

A technology for remote object imaging processing. An example of the technology can include calculating geographic coordinates for an object using in part a line-of-sight distance to the object from a camera coupled to a vehicle, calculating an angle of the sun relative to the object using sun position data that corresponds to a time and date and the geographic coordinates for the object, determining a white balance setting based in part on the angle of the sun relative to the object, and applying the white balance setting as a parameter used to process an image of the object captured using the camera coupled to the vehicle.

A vehicle control method, a vehicle control apparatus, an electronic device and a self-driving vehicle all relates to the field of self-driving and intelligent transportation technologies. The method includes: when a vehicle is moving, in a case that an occluding object is detected, determining a hard brake speed limit point and a potential collision point according to a planned path of the vehicle and position information of the occluding object; calculating a speed limit value of the hard brake speed limit point based on a distance between the hard brake speed limit point and the potential collision point; in a case that a planned speed of the vehicle at the hard brake speed limit point is less than or equal to the speed limit value, controlling the vehicle to move at the planned speed.

A driving assist method reduces a possibility of missing an opportunity to correct a host vehicle position set on map data. In the driving assist method using a controller that sets a target inter-vehicular distance from a host vehicle to a preceding vehicle, whether there is a request to execute a correction of a host vehicle position set on electronic map data is determined based on a result of comparing position information on a road sign described on the electronic map data with position information on the road sign acquired by using a camera installed in the host vehicle. If an execution request has not been made, the target inter-vehicular distance is set to a preset first target inter-vehicular distance. If an execution request has been made, the target inter-vehicular distance is set to a second target inter-vehicular distance, which is longer than the first target inter-vehicular distance.

In an aspect, a vehicle apparatus monitors, via a set of sensors communicatively coupled to the vehicle apparatus, a field of view (FOV) of the vehicle apparatus. The vehicle apparatus transmits, based on the monitoring, a first message that requests sensor data from one or more neighboring devices and indicates at least one sensor data filtering condition for the requested sensor data. A communication device receives the first message, and determines whether sensor data is available at the communication device which satisfies the at least one sensor data filtering condition for the requested sensor data. If so, the communication device filters its sensor data based on the at least one sensor data filtering condition, and transmits, to the vehicle apparatus, a second message including the filtered sensor data.

The disclosure describes various embodiments for online system-level validation of sensor synchronization. According to an embodiment, an exemplary method of analyzing sensor synchronization in an autonomous driving vehicle (ADV) include the operations of acquiring raw sensor data from a first sensor and a second sensor mounted on the ADV, the raw sensor data describing a target object in a surrounding environment of the ADV; and generating an accuracy map based on the raw sensor data in view of timestamps extracted from the raw sensor data. The method further includes the operations of generating a first bounding box and a second bounding box around the target object using the raw sensor data; and performing an analysis of the first and second bounding boxes and the accuracy map using a predetermined algorithm in view of one or more pre-configured sensor settings to determine whether the first sensor and the second sensor are synchronized with each other.

The present disclosure relates to systems and methods for host vehicle navigation. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a first flow of traffic and a second flow of traffic; determine a presence of at least one navigational state characteristic indicative of an alternating merging of the first flow of traffic and the second flow of traffic into a merged lane; cause at least a first navigational change to allow one target vehicle from the first flow of traffic to proceed ahead of the host vehicle; and cause at least a second navigational change to cause the host vehicle to follow the target vehicle into the merged lane.

The present subject matter especially relates to a traveling control apparatus, a corresponding traveling control method, an own vehicle including the traveling control apparatus, and a computer program product which is adapted to carry out the traveling control method. The control allows keeping an object, which is located on or close to the driving path/route of the own vehicle, continuously within the field of view of a sensor, a camera or the like which is installed at the own vehicle. This improves the reliability of the traveling control of the own vehicle and the driver can enjoy higher driving comfort and safety.

Systems and methods are disclosed for contextual driver monitoring. In one implementation, one or more first inputs are received and processed to determine a state of a driver present within a vehicle. One or more second inputs are receiving and processed to determine navigation condition(s) associated with the vehicle, the navigation condition(s) including a temporal road condition received from a cloud resource or a behavior of the driver. Based on the navigation condition(s), a driver attentiveness threshold is computed. One or more actions are initiated in correlation with the state of the driver and the driver attentiveness threshold.

A server holds a blockchain in which transaction information related to an inspection of each of the plurality of vehicles is recorded in a chain manner; selects a target vehicle of inspection from the plurality of vehicles and adds transaction information related to an inspection of the selected target vehicle to the blockchain; and transmits an execution instruction of a transaction related to the inspection to the target vehicle. The server registers an inspection result in the added transaction information related to the inspection of the target vehicle based on an execution result of the transaction related to the inspection of the target vehicle based on the execution instruction and an execution result of a transaction related to the inspection of a peripheral vehicle other than the target vehicle.