Vehicle Sensor Neutral Point Learning via Magnetic Markers

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Solution Overview

Problem

Steering angle and yaw rate sensors on vehicles face accuracy issues due to individual vehicle differences and environmental factors, leading to impaired measurement accuracy, particularly during straight-ahead movement and in varying temperatures.

Innovation Solution

A learning system and method for vehicles that sets specific conditions for learning the neutral point of measurement sensors, including a predetermined threshold for lateral shift fluctuations on constant-shape roads, ensuring stable sensor measurements and accurate neutral point learning by using magnetic markers and sensors to determine suitable learning conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If neutral point learning is performed without considering vehicle stability conditions, then learning can be performed at any time, but measurement accuracy deteriorates due to sensor drift and individual vehicle differences

Engineering Contradiction:
Improvemeasurement accuracyVSAvoidlearning condition judgment complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary judgment of learning conditions by evaluating vehicle stability (lateral acceleration threshold) and road geometry (curvature threshold) before executing neutral point learning. This ensures learning only occurs when the vehicle is in a stable, suitable state, improving measurement accuracy while maintaining a manageable level of complexity through predefined thresholds.

Inventive Principle:
Principle #10Preliminary action

2Productivity

If learning is performed during vehicle maneuvers or on curved roads, then more learning opportunities are available, but measurement accuracy deteriorates due to lateral acceleration and curvature effects

Engineering Contradiction:
Improvelearning opportunity frequencyVSAvoidneutral point learning accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The system changes the operational parameters by setting specific thresholds for lateral acceleration and road curvature to determine suitable learning conditions. By monitoring these parameters and only permitting learning when both remain below their respective thresholds, the system balances learning opportunity frequency with measurement accuracy, avoiding learning during maneuvers or on curved roads.

Inventive Principle:
Principle #35Parameter changes

3Ease of operation

If neutral point learning is performed without road curvature consideration, then learning can occur on any road, but measurement accuracy deteriorates due to curvature-induced sensor drift

Engineering Contradiction:
Improvelearning condition simplicityVSAvoidneutral point measurement accuracy
Core Design Contradiction:
Ease of operationVSMeasurement precision

Solution Approach 1:

The system incorporates feedback by continuously monitoring road curvature through magnetic marker detection and comparing it against a predetermined threshold. This feedback mechanism ensures learning only occurs on straight roads where curvature is minimal, maintaining measurement accuracy while preserving ease of operation through automatic condition assessment.

Inventive Principle:
Principle #23Feedback

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables highly accurate neutral point learning, improving measurement accuracy and stability, which enhances course prediction and adaptive cruise control by identifying suitable learning conditions based on lateral shift fluctuations and road curvature.

Implementation Method 1

a marker detection part having a plurality of magnetic sensors Cn (n is an integer from 1 to 15) arrayed on a straight line along a vehicle width direction

Methodology Applied
Scientific EffectMagnetic field detection: Magnetic Field

Data Source

PatentEP3508822B1Learning system and learning method for vehicle
Publication Date: 2023.10.11 AICHI STEEL CORP
  • EP3508822B1 patent drawingFigure 1
  • EP3508822B1 patent drawingFigure 2
  • EP3508822B1 patent drawingFigure 3

AI summary

A learning system (1) for vehicles for learning a neutral point of a measurement sensor equipped in a vehicle by using a magnetic marker disposed in a traveling road includes a sensor unit (11) which detects the magnetic marker and measures a lateral shift amount of the vehicle with respect to the magnetic marker, a route information acquiring part which acquires route information indicating a shape of the traveling road, and a learning determination part which determines whether a learning condition as a condition for performing learning of the neutral point of the measurement sensor is satisfied, wherein a fluctuation range of a lateral shift amount measured by the sensor unit (11) when the vehicle is traveling a learning road as a traveling road in a constant shape is equal to or smaller than a predetermined threshold is set at least as the learning condition.