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Intelligent Vehicle Accident Mitigation Collaboration System

IP.com Disclosure Number: IPCOM000246609D
Publication Date: 2016-Jun-20
Document File: 2 page(s) / 79K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system use predictive data analytics and cognitive analysis based on driver history and profile to mitigate accident risk.

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This is the abbreviated version, containing approximately 52% of the total text.

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Intelligent Vehicle Accident Mitigation Collaboration System

Automobile manufacturers include safety related features such as air bags, anti-lock brake systems (ABS), stability control, inattentiveness detection, etc. However, current safety features provide limited capability to drivers, particularly inexperienced drivers, to mitigate risk prior to entering a situation in which an accident might occur.

The novel contribution is an Intelligent Vehicle Accident Mitigation Collaboration System. This method and system use predictive data analytics and cognitive analysis based on driver history and profile to mitigate accident risk. The system mitigates driving risks prior to the driver entering a situation in which an accident might occur.

This system provides risk mitigation techniques to drivers in real time, in context, so drivers, particularly the inexperienced drivers, can actively mitigate risk to prevent accidents. That is, this system is not about providing a mechanism for auto-braking or steering, or tensioning seatbelts based on imminent collision detection. It is also not a passive system, nor is it about identifying transient road issues that are already provided by global positioning systems (GPS) or other systems. This system helps the driver actively avoid risks and gradually increase exposure to driving difficulty, thus reducing the chance of accidents.

Following are the components and process for implementing the Intelligent Vehicle

Accident Mitigation Collaboration System:


1. Capture driver experiences

A. For specific situations (e.g., highway, rotaries, city traffic, heavy traffic, rain, snow, etc.)

B. With the type of vehicle (e.g., motorcycle, size of car, type of truck, etc.); this may require manual input


C. Time driving the current vehicle


D. Tendencies/habits (e.g., quick stops, hard acceleration, changing lanes

without signaling, etc.)

    E. Time spent driving in specific areas
2. Capture driver profile info that may influence route preference (e.g., age, work

commuter, vacationer, re...