A control apparatus includes a risk area specification unit to specify a risk area outside a movable object, a transmission control unit to perform control to transmit alert information related to a position of the risk area to an outside of the movable object, a reception control unit to perform control to receive response information including information related to a position of another movable object that is response information to the alert information which is transmitted by the other movable object, and a control unit to execute control of the movable object based on position information of the risk area and the information related to the position of the other movable object which is acquired, in which the alert information includes information for controlling a mode related to a transmission cycle of the response information to be transmitted by the other movable object which has received the alert information.

A system for increasing the safety of voice conversations between drivers and remote parties is shown. The system includes an in-vehicle subsystem and a remote subsystem. The system includes a plurality of sensors which are configured to generate monitoring data. The system includes a computing device, which may be distributed between the subsystems and is configured to calculate a risk level as a function of the monitoring data. The computing device may engage an automatic safety response as a function of the risk level, that may include suspension or termination of on-going conversations among the parties, together with notification about the status of the communication channel. The safety response may be communicated to the driver by generating an alert. The in-vehicle and the remote subsystems communicate using a wireless connection and collaborate in engaging the automatic safety response and communicating any alerts to the driver and remote party using notifications.

The present application discloses a method and an apparatus for autonomous driving control, an electronic device, and a storage medium; the application relates to the technical field of autonomous driving. A specific implementation solution is: obtaining movement data of a pedestrian, where the movement data includes a velocity component of the pedestrian along a width direction of a lane and a time of duration that the pedestrian cuts into a driving path of the autonomous vehicle from one side; determining a movement direction of the pedestrian according to the movement data and the movement information of the pedestrian; and generating a driving strategy for the autonomous vehicle according to the movement direction of the pedestrian. Therefore, the movement direction of the pedestrian can be accurately predicted, which facilitates the autonomous vehicle to avoid the pedestrian and insures driving safety.

Systems and methods for clustering human trust dynamics are provided. In one embodiment, a computer implemented method for clustering human trust dynamics is provided. The computer implemented method includes receiving trust data for a plurality of participants interacting with one or more agents in an interaction. The computer implemented method also includes identifying a plurality of phases for the interaction. The computer implemented method further includes extracting features characterizing trust dynamics from the trust data for at least one interaction for each participant of the plurality participants. The at least one interaction is between the participant and an agent of the one or more agents. The computer implemented yet further includes assigning the features characterizing trust dynamics to a phase of the plurality of phases. The computer implemented method includes grouping a subset of the participant of the plurality of participants based on the on features characterizing trust dynamics.

A method and an apparatus for controlling personal mobility device (PMD) depending on congestion includes identifying a target area according to an expected route of the PMD, acquiring a degree of congestion of the target area based on at least one of a type of objects, a movement of the objects, a distance between objects, the number of objects that is expected, and a speed limit of the target area, and controlling speed of the PMD according to the degree of congestion.

The present disclosure provides a hybrid decision-making method for autonomous driving, including the following steps: acquiring real-time traffic environment information of an autonomous vehicle during the running at a current moment; establishing a local decision-making model for autonomous driving based on the traffic environment information; based on the local decision-making model for autonomous driving, learning, by using a method based on deep reinforcement learning, a driving behavior of the autonomous vehicle, and extracting driving rules; sharing the driving rules; augmenting an existing expert system knowledge base; and determining whether there is an emergency: if yes, making a decision by using a machine learning model; and if not, adjusting the machine learning model based on the augmented existing expert system knowledge base, and making a decision by the machine learning model. The decision-making method uses two existing policies to complement each other to overcome the shortcomings of a single policy, thereby making decisions effectively for different driving scenarios.

An alert control apparatus includes: an alert control unit to issue a first alert to an occupant in a moving body if an object in a particular category is present within a region to which the moving body is headed, and issue a second alert to the occupant if an object in a category other than the particular category is present within the region; a reception control unit to perform, when the moving body enters a new movement section on a movement route, control for receiving a category of an object present within the new movement section from an external apparatus. If there is an object for which an alert is to be issued during movement within the new section, the alert control unit, based on the category received from the external apparatus, controls as to which of the first and second alerts is to be issued.

Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.

A control apparatus includes an area identification unit to identify an area for which an analysis is not required based on information of an image captured from a position of a movable object, a first time period calculation unit to identify a first time period that is a time period for the movable object to reach the identified area, a second time period calculation unit to calculate a second time period that is an arrival time period to reach a movement area of the movable object from a position in the identified area, a risk area setting unit to set, in the identified area, a risk area where an object approaching the movable object may be present based on the first time period and the second time period and on the identified area, and a transmission control unit to control transmission of risk area information.

An information processing device is configured to acquire first data related to a driving operation performed in a first vehicle and second data related to a surrounding condition of the first vehicle, and perform driving evaluation for the first vehicle based on the first data and the second data.