Vehicle Information Processing System for Personalized Driving Assistance
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Solution Overview
Problem
Existing driving assistance systems provide uniform assistance based on pre-specified stop positions, which can be irritating to drivers as stop positions and deceleration timing vary by vehicle and driver, leading to discomfort and inadequate assistance.
Innovation Solution
A vehicle information processing system that associates driver and vehicle operation data with position information, learning each driver's typical deceleration and acceleration patterns to specify personalized assistance areas, allowing for tailored assistance based on frequent stop locations and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform assistance is provided based on pre-specified stop positions, then the assistance system can operate simply, but the assistance becomes irritating to drivers as it does not account for individual driving patterns
Solution Approach 1:
The system performs preliminary learning of driver behavior patterns by storing and analyzing driving operation information and position information in a database before providing assistance. This preliminary data collection and analysis enables the system to adapt to individual driver patterns without adding complexity to the real-time assistance operation.
Solution Approach 2:
The assistance system transitions from a static, uniform approach to a dynamic, adaptive approach by continuously learning and updating driver-specific patterns. The system adjusts assistance parameters based on learned individual behaviors, making the assistance comfortable for each driver while maintaining manageable system complexity through structured learning processes.
2Ease of operation
If personalized assistance is provided based on learned driver patterns, then driver comfort is improved, but the system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The system segments the assistance function into distinct modules: data collection, data storage, pattern learning, and assistance execution. This segmentation allows each module to handle specific tasks independently, managing overall system complexity while enabling personalized assistance through coordinated operation of these segmented functions.
Solution Approach 2:
The system performs self-learning by automatically collecting, storing, and analyzing driving patterns without requiring external intervention. This self-service capability enables the system to adapt to individual drivers autonomously, improving comfort while containing data processing complexity within the system's own operational framework.
3Adaptability or versatility
If stop position is uniformly specified from map or traffic information, then the assistance system can be implemented simply, but it fails to account for vehicle-specific and driver-specific variations in stopping behavior
Solution Approach 1:
The system implements feedback by continuously monitoring actual driving operations and position data, comparing them with learned patterns, and adjusting assistance accordingly. This feedback mechanism enables adaptation to individual driving patterns while managing information processing complexity through structured comparison and adjustment protocols.
Solution Approach 2:
The system performs preliminary learning of individual stopping patterns by storing and analyzing historical driving data before providing adapted assistance. This preliminary action enables the system to account for vehicle-specific and driver-specific variations without adding complexity to the real-time assistance delivery.
Data Source
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AI summary
A vehicle information processing system includes a database that mutually associates and stores driving operation information of a driver and position information of a vehicle for each candidate of an assistance area; and a specification unit that determines a vehicle stop based on the information stored in the database, and specifies an assistance area based on stop frequency in the same area. By this configuration, the assistance area can be specified by learning a driving action of the driver based on the database which stores information for each vehicle, and an assistance that is appropriate for the driver can be performed for each vehicle.