Intelligent Speed Check Detection via Vehicle Data Analysis

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Solution Overview

Problem

Existing navigation systems rely heavily on manual user reports and radar detectors, which are prone to errors and interference, making it difficult to accurately and timely detect speed check zones and events, especially requiring a large active user base for effective operation.

Innovation Solution

A system that collects and analyzes various data points from vehicles, including velocity, manual input, image recognition, and braking behavior data, using machine learning and non-machine learning algorithms to identify speed check zones and events, providing real-time information to users through in-vehicle navigation systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual user reports and radar detectors are used for speed check detection, then the system can provide speed check information, but the detection accuracy is reduced and the system is prone to errors and interference

Engineering Contradiction:

Improvedetection accuracyVSAvoidspeed check zone detection precision

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent replaces manual reporting mechanisms and radar detector hardware with an automated machine learning-based system that processes velocity data, image data, and sensor data from vehicles to detect speed check zones, thereby eliminating human error and radar interference issues

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a backend server as an intermediary that collects and processes data from multiple vehicles, using machine learning algorithms to analyze patterns and identify speed check zones, rather than relying on individual vehicle detectors or manual reports

Inventive Principle:

Principle #24Intermediary (Mediator)

2Speed

If radar detectors are used for speed check detection, then real-time detection is possible, but the system suffers from interference and false detections

Engineering Contradiction:

Improvereal-time detection capabilityVSAvoiddetection reliability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The patent substitutes radar detector hardware with a software-based machine learning system that analyzes velocity data, image recognition data, and sensor data to detect speed check zones, eliminating the interference and false detection problems inherent in radar technology

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements a feedback mechanism where the system continuously collects data from multiple vehicles, processes it through machine learning algorithms, and refines its detection accuracy over time, allowing reliable real-time detection without radar interference

Inventive Principle:

Principle #23Feedback

3Device complexity

If manual user reports are used for speed check detection, then the system can operate with minimal infrastructure, but it requires a large active user base to be effective

Engineering Contradiction:

Improvesystem infrastructure complexityVSAvoidnumber of active users required

Core Design Contradiction:

Device complexityVSQuantity of substance

Solution Approach 1:

The patent merges data from multiple sources including velocity data from vehicle sensors, image recognition data from cameras, and sensor data from various vehicles into a unified analysis system that processes all information together to detect speed check zones

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces a backend server as an intermediary that aggregates and processes data from multiple vehicles, using machine learning algorithms to identify patterns that would be impossible to detect with individual vehicle data alone, thereby reducing the need for a large active user base

Inventive Principle:

Principle #24Intermediary (Mediator)

4Measurement precision

If multiple data sources and machine learning algorithms are used for speed check detection, then detection accuracy is improved, but the system complexity increases

Engineering Contradiction:

Improvespeed check zone detection precisionVSAvoidsystem structural complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses a backend server as an intermediary to manage the complexity of collecting, processing, and analyzing multiple data sources including velocity data, image data, and sensor data through machine learning algorithms, keeping the individual vehicle systems relatively simple

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20240212488A1Intelligent speed check

Publication Date: 2024.06.27 CARIAD SE

AI summary

One or more sets of velocity and vehicle data originating from one or more vehicles traversing a road segment are collected. The one or more sets of velocity data are analyzed to generate speed check analytical data for the road segment. A speed check zone on the road segment is identified based at least in part on the speed check analytical data.