Driver Risk Prediction via Segmented AI Fingerprint Analysis
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Solution Overview
Problem
Conventional machine learning processes for vehicle AI integration face challenges in handling voluminous real-time data, making it difficult to provide efficient real-time responses and extract useful information, especially in predicting driver risks due to intensive computation and data management requirements.
Innovation Solution
A system utilizing interior and exterior sensors, vehicle onboard computers, and cloud computing to generate and analyze driver fingerprints, combining current and historical driving data to predict potential risks, with features like teen driver detection, elder driver analysis, and fleet management, leveraging AI models and big data for risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning processes are used for vehicle AI integration, then model training can be performed, but real-time response becomes difficult due to intensive computation and voluminous data processing requirements
Solution Approach 1:
The patent segments the machine learning process into two distinct parts: (1) model training phase that occurs offline using historical data, and (2) real-time inference phase that occurs in the vehicle using the trained model. This segmentation allows intensive computation to be performed separately from real-time operations, resolving the contradiction between reliable real-time response and computational complexity.
2Loss of information
If voluminous real-time data is collected from sensors, then comprehensive driver behavior analysis can be achieved, but data handling and information extraction becomes difficult
Solution Approach 1:
The patent extracts only the essential features from voluminous sensor data that are relevant to driver behavior analysis. Instead of processing all raw sensor data in real-time, the system extracts key indicators such as steering patterns, braking behavior, and acceleration profiles, thereby reducing data management complexity while preserving critical information for risk assessment.
Solution Approach 2:
The system performs preliminary data processing and feature extraction offline before real-time deployment. Historical driver behavior data is pre-processed to identify characteristic patterns, which are then stored as reference profiles. During real-time operation, the system only needs to compare current sensor readings against these pre-computed profiles, significantly reducing the complexity of real-time data handling.
3Measurement precision
If extensive data collection from multiple sensors is performed, then accurate driver risk prediction can be achieved, but system complexity and data processing burden increases
Solution Approach 1:
The patent merges data from multiple sensor sources (cameras, microphones, accelerometers, steering angle sensors) into a unified driver behavior profile. By combining these diverse data streams through a standardized processing framework and integrating them with historical data from cloud storage, the system achieves comprehensive and accurate risk prediction while managing sensor integration complexity through a cohesive architectural approach.
Data Source
AI summary
One embodiment of the present invention discloses a process of providing a report predicting potential risks relating to an operator driving a vehicle using information obtained from various interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud network. After activating interior and exterior sensors mounted on a vehicle operated by a driver for obtaining data relating to external surroundings and internal environment, the data is forwarded to VOC for generating a current fingerprint associated with the driver. The current fingerprint represents current driving status in accordance with the collected real-time data. Upon uploading the current fingerprint to the cloud via a communications network, a historical fingerprint which represents historical driving information associated with the driver is retrieved. In one aspect, the process is capable of generating a driving analysis report which predicts potential risks associated with the driver according to the current and historical fingerprints.