ML Steering Mode Selection for Real-Time Adaptive Calibration

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Solution Overview

Problem

Existing vehicle systems require manual driver intervention to switch operating modes, which can be distracting and impractical in dynamic situations, such as roadblocks or emergencies, and do not adapt quickly to changes in vehicle components like tires or suspension.

Innovation Solution

A steering system with a control module that dynamically determines operating modes based on lateral acceleration and handwheel position signals, using machine learning to automatically switch between modes without manual input, and adapts to changes in vehicle components by recalibrating the steering system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual mode selection is provided, then driver can choose operating mode, but driver distraction and slow response time occur

Engineering Contradiction:

Improvemode selectionVSAvoidresponse time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system automatically detects driving conditions and selects operating modes without driver intervention. The control module monitors sensor inputs (acceleration, steering angle, vehicle speed) and autonomously determines the appropriate mode, eliminating the need for manual selection while maintaining optimal performance.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical interaction (buttons, knobs) with an electronic sensor-based detection system. Sensors continuously monitor vehicle dynamics and communicate with the control module, which processes the data and automatically adjusts operating modes, substituting physical driver actions with automated electronic control.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual mode switching is used, then driver control is maintained, but system adaptability to dynamic conditions deteriorates

Engineering Contradiction:

Improvemode adaptationVSAvoiddriver control

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system continuously monitors vehicle sensor data (acceleration, steering angle, speed) and uses this feedback to dynamically adjust operating modes. The control module processes real-time sensor inputs and modifies vehicle configuration accordingly, creating a closed-loop system that adapts to changing driving conditions while maintaining driver awareness through natural vehicle behavior.

Inventive Principle:

Principle #23Feedback

3Extent of automation

If physical user interface elements are used for mode selection, then driver can manually change modes, but driver distraction and safety are compromised

Engineering Contradiction:

Improveautomatic mode switchingVSAvoiddriver distraction

Core Design Contradiction:

Extent of automationVSObject-affected harmful factors

Solution Approach 1:

The patent removes physical mode selection interfaces (buttons, knobs, switches) from the driver's reach. The automatic detection and mode selection functionality is extracted and integrated into the vehicle's existing sensor network and control systems, eliminating the need for separate manual controls while maintaining full mode switching capability.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11254351B2Steering mode selection using machine learning

Publication Date: 2022.02.22 STEERING SOLUTIONS IP HOLDING CORP

AI summary

Technical features of a steering system include a control module that dynamically determines an operating mode based on a set of input signal values such as lateral acceleration signal values and corresponding handwheel position values. The control module dynamically determines and learns classification boundaries between multiple operating modes based the input signal values. The control module further calibrates the steering system according to operating mode that is determined using the classification boundaries.