The present invention provides for a cognitive system using an autonomous vehicle includes a plurality of sensors configured to obtain the weather forecast for a pollution detectable area; a cognitive input to determine the pollution detectable area having highest sensitivity of pollution; a light detecting and ranging system configured to spatially probe pollution levels distributed in the pollution detectable area; an evaluation system to evaluate the probed pollution levels in the pollution detectable area; and a recommendation system for recommending an action to be taken based on evaluation system results of the probed pollution levels in the pollution detectable area, wherein the pollution levels are detected based light emitted by the light detecting and ranging system.

A method for determining passage of an autonomous vehicle includes: acquiring information about an intersection on a driving route of the autonomous vehicle, wherein the information about the intersection comprises lane data; acquiring a historical trajectory of an obstacle in the intersection within a specific time; acquiring, by matching the historical trajectory of the obstacle with center lines of respective lanes in the lane data, a lane with smallest matching error; and determining that the lane with the smallest matching error is in a passable state, wherein the lane with the smallest matching error is a lane where the obstacle is located.

A server includes a dangerous driving level calculator and a server-side communicator. The calculator analyzes patterns of driving behavior of analyzed vehicles that are included in data of images based on sent-to-server data to calculate dangerous driving levels of the analyzed vehicles, and compares the levels of the analyzed vehicles with a predetermined value to categorize each analyzed vehicle into a danger vehicle if its level is higher than the predetermined value. The server-side communicator transmits sent-to-vehicle data to vehicle-side communicators of alerted vehicles at second predetermined timing, and receives the sent-to-server data at first timing. The sent-to-vehicle data includes the level of the danger vehicle and positional information that represents a position at which an image of the danger vehicle is captured. The level of the danger vehicle can be indicated on road maps on vehicle-side displays of the alerted vehicles in accordance the position of the danger vehicle.

An optical fiber sensing system according to the present disclosure includes: an optical fiber (10) provided along a road R to detect vibrations; a detection unit (21) configured to detect a vibration pattern of a vibration caused by a traffic accident that has occurred on the road R from optical signals received from the optical fiber (10); and an estimation unit (22) configured to estimate a situation of the traffic accident based on the vibration pattern.

A vehicle control device includes an external situation recognition unit configured to recognize a crossing person who crosses over a path of a vehicle and acquire information on the crossing person and information on an environment where the crossing person crosses, a scheduled departure time deciding unit configured to decide a scheduled departure time of the vehicle based on the information on the crossing person and the environment where the crossing person crosses when the crossing person is recognized by the external situation recognition unit, and an informing controller configured to perform a control to inform an outside of the vehicle of the scheduled departure time. The scheduled departure time deciding unit predicts a crossing completion time at which the crossing person recognized by the external situation recognition unit completes the crossing and decides the scheduled departure time based on the crossing completion time.

The present invention is a method for determining the amount of pollutant emissions (Q) from at least one vehicle over a road network section, wherein a pollutant emission model (MFE) is constructed by machine learning (APP) using macroscopic data (MAC) of a learning road network and traffic data (TRA). This model (MFE) is then applied to a road network section.

There is provided a decentralized system and method for distributed traffic management comprising: a plurality of intersection computing agents connected across a communication network, each intersection computing agent located at a particular intersection communicating with a plurality of corresponding local link computing agents comprising sensors located on each respective road link directly connected to the particular intersection, to receive a link status report comprising speed and number of vehicles on said each respective road link; and said each intersection computing agent calculating an estimated travel time for said each respective road link from said link status report and receiving link information packet comprising the estimated travel time for said each respective road link from a first plurality of intersection computing agents located at a first plurality of intersections physically located downstream to create a network travel time matrix for routing vehicles at said particular intersection.

Reckless behavior of drivers like, speeding, sudden acceleration and swerving through lanes can cause fatality and financial loss. Conventional methods mainly focus on driving style classification. The conventional methods mainly focus on driver classification and are not able to provide trip classification of a driver. Hence there is a challenge in trip classification of the driver based on acceleration data. The present disclosure for trip classification addresses the problem of end to end trip classification based on the acceleration data. Here, a journey is segmented into a plurality of sub-journey segments and each sub-journey segment is associated with a plurality of driving events. An event score is calculated for each sub-journey and a normalization is performed on the event score. Further, the journey is classified into at least one of good, average or bad based on the normalized data by utilizing a fuzzy based classification.

Aspects of the present disclosure include methods, systems, and non-transitory computer readable media for receiving a plurality of images of a plurality of street locations from a plurality of image capturing devices, identifying an event at a street location of the plurality of street locations based on one or more of the plurality of images, determining at least one new route for at least one vehicle based on the event and one or more of the plurality of images, and transmitting at least one indication indicating the at least one new route to at least one receiving device.

Systems and methods for determining utilization of an area for vehicle parking are provided. For example, a method for determining utilization of an area for vehicle parking includes determining an expected level of utilization of an area for vehicle parking. The method also includes based on the determined expected level of utilization, analyzing one or more characteristics associated the area for vehicle parking. The method also includes based on the analysis, providing information for modifying the utility of the area for vehicle parking.