International Business Machines Corporation

University College Dublin

Rodrigo Hernan Ordonez-Hurtado

Yassine Lassoued

Julien Monteil

Martin Mevissen

Robert Shorten

Giovanni Russo

Claudio Gambella

Wynita M Griggs

Jakub Marecek

Jonathan P Epperlein

Sergiy Zhuk

Anton Dekusar

Sergio Cabrero Barros

Jeffrey L Rogers

Bo Wen

Nigel Hinds

Wynita May Griggs

Jonathan Peter Epperlein

Embodiments for implementing intelligent risk detection and mitigation in a transport network by a processor. Data gathered from a plurality of data sources relating to an entity and a selected region of interest may be analyzed. Behavior of an entity, in relation to a risk event, may be learned and interpreted based on a plurality of identified contextual factors, geographical data, current data, historical data, a learned risk event model, or a combination thereof. One or more mitigation actions may be performed to mitigate risk of occurrence or a possible negative impact of the risk event caused at least in part by the behavior of the entity.

Detecting and mitigating risk in a transport network

Generating parking area statistics for identifying parking areas within a geographic region. Navigation support for reaching a top-ranked parking area according to collected parking area data, user preferences, parking feedback, and real-time traffic conditions. Selection of a top-ranked parking area and monitoring progress during a parking event improve future identification and ranking of parking areas.

Identifying available parking areas

In an approach for determining the illumination necessary along a vehicle's planned trajectory, a processor receives information relevant to the trajectory of a vehicle. A processor estimates the position of the vehicle at the particular time on the trajectory of the vehicle, using the data from the sensors. A processor estimates an illumination of the trajectory of the vehicle, using the model of the illumination of the road network and the data from the sensors, wherein a threshold is employed. A processor determines the threshold is exceeded. A processor changes a setting of the at least one headlight on the vehicle. A processor determines the threshold is exceeded. A processor alerts the user of the vehicle when adjustments to the at least one headlight that are necessary to illuminate the trajectory of the vehicle exceed physical limitations of the at least one headlight.

Adaptive headlights for the trajectory of a vehicle

A system and method for cognitive journey monitoring are presented. Embodiments comprise journey prediction, parsing of data sources, risk assessment and mitigation, and natural-language user interaction by a cognitive processor. Data is gathered from a plurality of data sources and analyzed in the context of one or more of the user's intention(s). A dialogue with the user, in natural language, aims to provide and select one or more suggestions relating to the one or more user intention(s) such that the risk(s) relating to the one or more user's intention(s) is reduced. During the dialogue, cognitive reasoning may be performed, wherein the cognitive reasoning includes the ability to justify each suggestion and the ability to infer information from the interaction such as, for example, data obtained in a dialogue may inform subsequent inferences. The embodiments may use speech synthesis and speech recognition in an interactive spoken dialogue.

Cognitive journey companion system

In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

Mitigating risk behaviors

Embodiments for planning vehicular driving actions in the presence of non-recurrent events by a processor. One or more dynamics of non-recurrent events in a transport network may be learned according to one or more contextual factors. One or more vehicle-specific factors may be learned in relation to a historical journey and current journey of a vehicle. One or more action responses associated with the one or more non-recurrent events and the one or more vehicle-specific factors may be generated.

Vehicular driving actions in the presence of non-recurrent events

Embodiments for testing embedded systems and their applications in an Internet of Things (IoT) environment by a processor, denoted as a Hardware-in-the-Loop as a Service (HiLaaS). In a simulated environment, one or more simulated entities and one or more real entities in a networked system may be tested in real-time according to received control parameters, for a price. The price is estimated by the system, based on other parameters, and offered to the user to accept or reject. Alternatively, the user may specify the price, the system estimates control parameters, and the user can accept or reject the control parameters. One or more properties of the one or more entities, the network system, or combination thereof may be estimated based on the testing of the one or more simulated entities, when the price and control parameters are accepted.

Testing embedded systems and application using hardware-in-the-loop as a service (HILAAS)

A system and method for cognitive risk mitigation are presented. Embodiments comprise journey prediction, parsing of data sources, risk assessment and mitigation, and natural-language user interaction by a cognitive processor. Data is gathered from a plurality of data sources and analyzed in the context of one or more of the user's intention(s). A dialogue with the user, in natural language, aims to provide and select one or more suggestions relating to the one or more user intention(s) such that the risk(s) relating to the one or more user's intention(s) is reduced. During the dialogue, cognitive reasoning may be performed, wherein the cognitive reasoning includes the ability to justify each suggestion and the ability to infer information from the interaction such as, for example, data obtained in a dialogue may inform subsequent inferences. The embodiments may use speech synthesis and speech recognition in an interactive spoken dialogue.

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Maynooth, Ireland

Rodrigo Hernan Ordonez-Hurtado