Decoupled Vehicle Friction and Lateral Velocity Estimation
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Solution Overview
Problem
Conventional systems for estimating road surface friction coefficient and vehicular lateral velocity are intertwined, leading to potential compromises in accuracy due to necessary assumptions.
Innovation Solution
A system that includes a self-aligning torque coefficient estimating module, a road surface friction coefficient estimating module, and a lateral velocity estimating module, using sensor signals from an EPS system and IMU, with recursive least square algorithms to independently estimate these values in real-time, allowing for accurate control signal generation for vehicle features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems use intertwined estimation methods for road surface friction coefficient and lateral velocity, then the system complexity is reduced, but the measurement precision of both parameters deteriorates due to necessary assumptions
Solution Approach 1:
The patent divides the estimation problem into two independent segments: one for estimating road surface friction coefficient and another for estimating lateral velocity. Each segment has its own dedicated estimation module that processes data independently, eliminating the need for intertwined assumptions while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces a decoupled dynamical model as an intermediary framework that allows independent estimation of friction coefficient and lateral velocity. This model acts as a mediator that processes sensor data separately for each parameter, enabling accurate estimation without requiring one parameter to be estimated from the other
2Measurement precision
If independent estimation modules are used for road surface friction coefficient and lateral velocity, then the measurement precision of both parameters improves, but the device complexity increases
Solution Approach 1:
The patent creates estimation modules that are universal in nature, capable of processing multiple types of sensor data (steering torque, lateral acceleration, vehicle speed) through a unified decoupled dynamical model framework. This multi-functionality allows the system to handle different estimation tasks with consistent methodology, reducing the perceived complexity despite independent processing
Solution Approach 2:
The patent employs dynamic estimation algorithms that continuously adapt to changing driving conditions. The independent modules use real-time sensor data to dynamically adjust their estimates, allowing the system to maintain high precision across varying operational scenarios without requiring overly complex static structures
Data Source
AI summary
Systems and methods for independently estimating a road surface friction coefficient value and a vehicular lateral velocity value are provided. In one example, the system includes: a self-aligning torque coefficient estimating module configured to obtain sensor signals from an electronic power steering (EPS) system and an inertial measurement unit and estimate a first self-aligning torque coefficient value based on the sensor signals using a recursive least square algorithm; a road surface friction coefficient value estimating module configured to obtain the estimated first self-aligning torque coefficient value and estimate a first road surface friction coefficient value based on the estimated first self-aligning torque coefficient value; and a feature control module configured to generate one or more control signals configured to control features of a vehicle based on the estimated first road surface friction coefficient value.