EP-3856596-B1 - INTELLIGENT TRANSPORTATION SYSTEMS

Inventors

• CELLA, Charles Howard

Dates

Publication Date

20260506

Application Date

20190930

Claims (4)

1. A vehicle system, comprising: a vehicle (2910), one or more sensors (2925) deployed in the vehicle for capturing emotional stateindicative physiological data (29125) of a rider (2944) occupying the vehicle (2910), a vehicle control system, and a hybrid neural network system (2947) for optimizing rider satisfaction, comprising: a recurrent neural network (2922) configured to detect an emotional state (2966) of the rider and to provide an indication of a change in the emotional state (2966) by recognizing patterns in the physiological data (29125) , and a radial basis function neural network (2920) configured to optimize at least one operational parameter (29124) of the vehicle in response to the indication of change in the emotional state of the rider (2944) to induce a favorable emotional state of the rider (2944), wherein: the operational parameter (29124) affects at least one of a speed, an acceleration or a deceleration of the vehicle, a proximity to objects along the route, or a proximity to other vehicles along the route, and the radial basis function neural network (2920) is configured to interact with the vehicle control system to adjust the operational parameter (29124).
2. The vehicle system of claim 1, wherein the radial basis function neural network (2920) is further to optimize the operational parameter (29124) based on a correlation between a vehicle operating state (2945) and the emotional state (2966) of the rider (2944).
3. The vehicle system of claim 1, wherein the radial basis function neural network (2920) optimizes the operational parameter (29124) in real time responsive to the detecting of the detected emotional state of the rider (2944) by the recurrent neural network (2922).
4. The vehicle system of claim 1, wherein the recurrent neural network (2922) comprises a plurality of connected nodes that form a directed cycle, the recurrent neural network (2922) further facilitating bi-directional flow of data among the connected nodes.

Description

TECHNICAL FIELD The present disclosure relates to a vehicle system, more particularly relates to inter-connectivity and optimization of user experiences in such a vehicle system. BACKGROUND As artificial intelligence, cognitive networking, sensor technologies, storage technologies (e.g., blockchain and other distributed ledger technologies) and other technologies progress, opportunities exist for development of systems that enable improved mobility and transportation for passengers and for objects, such as freight, goods, animals and the like. A need exists for improved transportation systems that take advantage of such technologies and their capabilities. Some applications of artificial intelligence have been, at least to a degree, effective at accomplishing certain tasks, such as tasks involving recognition and classification of objects and behavior, such as in natural language processing (NLP) and computer vision systems. However, in complex, dynamic systems that involve interactions of elements, such as transportation systems that involve sets of complex chemical processes (e.g., involving combustion processes, heating and cooling, battery charging and discharging), mechanical systems, and human systems (involving individual and group behaviors), significant challenges exist in classifying, predicting and optimizing system-level interactions and behaviors. A need exists for systems apply specialized capabilities of different types of neural networks and other artificial intelligence technologies and for systems that enable selective deployment of such technologies, as well as various hybrids and combinations of such technologies. The documents EP 2 942 012 A1, US 2018/229674 A1, US 2006/011399 A1, US 2008/269958 A1 and US 2015/314681 A1 are relevant prior art documents. SUMMARY The present disclosure relates to a vehicle system, as defined by claim 1. BRIEF DESCRIPTION OF THE FIGURES In the accompanying figures, like reference numerals refer to identical or functionally similar elements throughout the separate views and together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the example of systems and methods disclosed herein. Fig. 1 is a diagrammatic view that illustrates an architecture for a transportation system showing certain illustrative components and arrangements relating to various embodiments of the present disclosure.Fig. 2 is a diagrammatic view that illustrates use of a hybrid neural network to optimize a powertrain component of a vehicle relating to various embodiments of the present disclosure.Fig. 3 is a diagrammatic view that illustrates a set of states that may be provided as inputs to and/or be governed by an expert system/Artificial Intelligence (AI) system relating to various embodiments of the present disclosure.Fig. 4 is a diagrammatic view that illustrates a range of parameters that may be taken as inputs by an expert system or AI system, or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and/or one or more sensors, cameras, or external systems relating to various embodiments of the present disclosure.Fig. 5 is a diagrammatic view that illustrates a set of vehicle user interfaces relating to various embodiments of the present disclosure.Fig. 6 is a diagrammatic view that illustrates a set of interfaces among transportation system components relating to various embodiments of the present disclosure.Fig. 7 is a diagrammatic view that illustrates a data processing system, which may process data from various sources relating to various embodiments of the present disclosure.Fig. 8 is a diagrammatic view that illustrates a set of algorithms that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 9 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 10 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 11 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 12 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 13 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 14 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.Fig. 15 is a diagrammatic view that