An example operation includes one or more of determining a level of risk of a collision of a transport, wherein the level of risk is related to an occupant of the transport and an environment of the transport, accumulating an amount of data based on the level of risk, preparing the accumulated data for sending to one or more recipients when the level of risk exceeds a risk threshold, and sending the prepared data to the one or more recipients when the collision occurs.

An emergency response system provides a map-based interface for a telecommunicator to view information about an incident and coordinate a response to the incident. The emergency response system gathers supplemental data regarding locations and other information that may be relevant in assisting with the incident. The interface may automatically select relevant supplemental information based on the incident and provide this incident-specific information for display on the interface. The user may then select a response on the interface such as a unit to dispatch that the system automatically implements, providing unified information and control for incidents, supplemental information, and incident response.

An example operation includes one or more of determining a characteristic of an occupant in a transport and a current driving environment of the transport, wherein the characteristic includes a position of the occupant, determining that the current driving environment will lead to a collision of the transport, and based on a predicted result of the collision, sending a predicted state of the occupant based on the position, to an emergency service node.

An information processing apparatus according to the present application includes an acquiring unit and a deciding unit. The acquiring unit acquires attribute information on a user. The deciding unit decides, based on the attribute information acquired by the acquiring unit, a placement position in which a virtual riding point that corresponds to a riding point that is used for the user to ride in a moving vehicle and that is virtually set is placed.

Systems and methods are disclosed for monitoring or affecting movement of a vehicle using a traffic device. An event data source may have a processor and/or a transceiver. The event data source may transmit, via the transceiver and to a vehicle and infrastructure computing device, information indicative of an event affecting a portion of road. The vehicle and infrastructure computing device may comprise a vehicle and infrastructure control computer. The vehicle and infrastructure computing device may receive, from the event data source, the information indicative of the event affecting the portion of road. The computing device may determine one or more traffic devices associated with the portion of road and configured to control traffic for the portion of road. Based on the information indicative of the event affecting the portion of road, the computing device may send, to the one or more traffic devices associated with the portion of road, instructions to change one or more characteristics of the one or more traffic devices.

Computationally implemented methods and systems that are designed for receiving one or more first directives that direct a transportation vehicle unit to transport a first end user; receiving, while the transportation vehicle unit is en route to or is transporting the first end user, one or more second directives that direct the transportation vehicle unit to transport a second end user while transporting the first end user, the transportation vehicle unit having been determined to be able to accommodate transport of the second end user while transporting the first end user; and verifying that compliance with the one or more second directives will not conflict with one or more obligations to transport the first end user by the transportation vehicle unit. In addition to the foregoing, other aspects are described in the claims, drawings, and text.

A vehicle-searching guidance method includes: displaying a vehicle-searching guidance interface in response to a first trigger operation on a first control, the vehicle-searching guidance interface being used for providing guidance information for vehicle searching; transmitting a multimedia content obtaining request to an electronic device in response to a second trigger operation on a second control on the vehicle-searching guidance interface; receiving multimedia guidance content returned by the electronic device according to the multimedia content obtaining request, the multimedia guidance content including environmental information of a parking space in which a vehicle is parked; and presenting the multimedia guidance content for a user to search for the vehicle.

Techniques for providing real-time vehicle surveillance is disclosed. An in-vehicle surveillance device continuously captures images from the surroundings of a vehicle and the interior of the vehicle and transmits them to a surveillance management system. The images are processed in real-time using machine learning modules to determine primary, secondary, and adverse events. Upon determining the events, alerts are generated and sent to a display unit provided on the in-vehicle surveillance device to improve the safety of the passengers. The techniques further allow vehicle-to-vehicle communication and vehicle to third party device communication upon determining an event.

A leading system has an output unit configured to output a predetermined signal to a first area around a vehicle, and a terminal carried by a person. The terminal has a receiving unit configured to receive the predetermined signal when the person enters the first area and a first notification unit configured to automatically perform a first notification to the person when the predetermined signal is received by the receiving unit.

An apparatus, method and computer program product are provided for predicting wrong-way-driving events. A WWD event defines an event in which a vehicle travels in a direction opposing a traffic direction designated for a portion of a road. In one example, the apparatus estimates a likelihood of portion of the road inducing a wrong-way-driving (WWD) event by using a machine learning model as a function of map data, sensor data, or a combination thereof. If the likelihood satisfies a threshold, the apparatus updates a map layer to indicate the WWD event at the portion of the road.