Multi-Vehicle Prediction System Latency Segmentation
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Solution Overview
Problem
The vast amount of vehicle telematics data collected from various sources poses challenges in extracting valuable insights in a timely and accurate manner, particularly for time-sensitive collision avoidance systems, due to differences in data latency and volume.
Innovation Solution
A method and system utilizing a gateway to categorize vehicular data samples by latency, with a stream processor and rules engine deriving predictive models to anticipate incidents and transmit notifications, while also storing high-latency data for batch processing, leveraging AI and ML for real-time analysis and Big Data trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If all vehicular data samples are processed in real-time for collision avoidance, then response time is reduced, but system complexity and computational load increase significantly
Solution Approach 1:
The patent segments data processing into two distinct pathways: a stream processing path for low-latency time-sensitive data (collision avoidance) and a batch processing path for high-latency non-time-sensitive data. This segmentation allows the system to process critical safety data in real-time while deferring less critical analysis, thereby reducing the computational burden on the real-time processing components and lowering overall system complexity.
Solution Approach 2:
The patent introduces a data lake as an intermediary storage layer between data collection and analysis. Time-sensitive data flows through the stream processor to immediate analysis, while time-insensitive data is routed to the data lake for later batch processing. This intermediary structure enables the system to manage different data types with different latency requirements without creating a single complex processing pipeline.
2Measurement precision
If only low-latency data is processed for incident prediction, then real-time accuracy improves, but data completeness and predictive accuracy deteriorate
Solution Approach 1:
The patent segments predictive modeling into two complementary approaches: online modeling using stream-processed low-latency data for immediate incident prediction, and offline modeling using batch-processed high-latency data from the data lake for comprehensive pattern recognition. This segmentation ensures that real-time predictions are made with the most current data while periodic offline analysis incorporates the complete dataset for model refinement and validation.
Solution Approach 2:
The patent implements a feedback mechanism where offline batch processing results are used to refine and update the online predictive models. The batch processor analyzes complete historical data patterns and feeds insights back to improve the stream processor's predictive accuracy. This closed-loop feedback ensures that real-time predictions benefit from both immediate data and comprehensive historical analysis.
Data Source
AI summary
A method of operating an incident avoidance system for use in a vehicle comprises a gateway receiving a plurality of vehicular data samples from a plurality of data sources in a vicinity of a target vehicle. A stream processor coupled to the gateway, categorizes a first plurality of low latency data samples from the plurality of vehicular data samples based on an allowable latency of each of the plurality of vehicular data samples. A rules engine coupled to the stream processor, receives the plurality of low latency data samples. The rules engine produces a predictive model based on the plurality of low latency data samples. A notification service accesses the predictive model and situational data of the target vehicle to predict an incident. The notification service transmits a notification of the incident to the target vehicle.