Volvo Car Corporation

Jonas Fenn

Usha Nookala

Krishna Sankar

Vedad Cajic

Peter Winzell

The present disclosure provides a set of tools that enable traffic safety developments across multiple participants and industries, with location and other safety related data being shared in an anonymized fashion. Specifically, a location based server is provided that implements a vulnerable road user protocol. This vulnerable road user protocol avoids the use of global vehicle identification numbers and does not utilize persistent storage for such information, thereby mitigating location based privacy concerns. Further, the present disclosure provides a 'hybrid' simulation engine that can be used to tailor and provide specific road user alerts, and specifies a risk assessment module that acts as a logic for quantifying the risk level associated with each unique road user relationship. Finally, the present disclosure provides an alert methodology for certain vulnerable road users based on noise-cancelling headphone and 3D audio algorithm technologies.

Systems and methods for protecting vulnerable road users

Distributing resources in a predetermined geographical area, including: retrieving a set of metrics indicative of factors of interest related to operation of the resources for at least two parties, each having a plurality of resources, retrieving optimization policies indicative of preferred metric values for each party, retrieving at least one model including strategies for distributing resources in the predetermined area, the at least one model based on learning from a set of scenarios for distributing resources, retrieving context data from real time systems indicative of at least a present traffic situation, establishing a Nash equilibrium between the metrics in the optimization policies of the at least two parties taking into account the at least one model and the context data, distributing the resources in the geographical area according to the outcome of the established Nash equilibrium.

Method and system for orchestrating multi-party services using semi-cooperative nash equilibrium based on artificial intelligence, neural network models,reinforcement learning and finite-state automata

Predicting transportation demand in a predetermined area, based on estimating a present mobility demand and based on user group preferences. Generated transportation need requests include at least a time stamp, a pick-up coordinate, a drop-off coordinate, a user group indication, a pick-up venue category based on the pick-up coordinate, and a drop-off venue category based on the drop-off coordinate. A signal indicative of the transportation need request is provided.

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Mountain View, CA, United States of America

Jonas Fenn