Individual Driving Behavior Model for Collision Avoidance
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Solution Overview
Problem
Existing driving control systems rely on average driving behavior models, which fail to accurately predict individual driver tendencies, leading to potential collisions due to variations in driving style, reaction time, and comfortability.
Innovation Solution
A system that identifies and generates an individual driving behavior model specific to a driver based on their historical behavior, allowing vehicles to transmit and receive this model for improved collision avoidance by considering the specific driver's tendencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If average driving behavior models are used, then device complexity is reduced, but measurement precision of driver tendencies deteriorates
Solution Approach 1:
The patent segments the general driving behavior model into individual driver-specific models. Each driver receives a personalized behavior model that captures their unique driving tendencies, reaction times, and comfort preferences. This segmentation allows the system to maintain lower overall complexity while achieving high prediction accuracy for each individual driver through specialized, tailored models rather than a single complex general model.
2Reliability
If individual driving behavior models are generated and transmitted, then collision avoidance accuracy is improved, but loss of information increases due to data transmission requirements
Solution Approach 1:
The patent creates simplified copies of driver behavior models that can be transmitted between vehicles and infrastructure. Instead of transmitting raw, detailed behavioral data, the system generates condensed model representations that capture essential driving characteristics. These copied models enable accurate collision avoidance predictions while minimizing data transmission requirements and information loss.
3Measurement precision
If historical driving behavior data is analyzed for each driver, then measurement precision of individual tendencies is improved, but loss of time for model generation increases
Solution Approach 1:
The patent implements preliminary action by pre-generating and storing individual driving behavior models before they are needed for collision avoidance decisions. The system continuously analyzes historical driving data in the background to build and update driver-specific models proactively. When a collision risk situation arises, the pre-computed models are already available for immediate use, eliminating delays that would occur from real-time analysis during critical moments.
Data Source
AI summary
Driving control systems and methods include a first vehicle having one or more processors programmed to receive an individual driving behavior model specific to a driver of a second vehicle proximate the first vehicle. The individual driving behavior model is based on historical driving behavior of the driver of the second vehicle. The one or more processors are programmed to identify a motion plan for the first vehicle based on the individual driving behavior model specific to the driver of the second vehicle, and to control movement of the first vehicle according to the identified motion plan.