Predictive Steering Torque Compensation for Vehicle Disturbances
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Solution Overview
Problem
Conventional advanced driver-assistance systems (ADAS) and autonomous driving (AD) systems do not adequately mitigate vehicle disturbances such as road banking, cross-winds, and uneven vehicle loads, requiring continuous driver steering input to counteract these conditions.
Innovation Solution
A method and system that combines predicted driving condition data from geospatial and remote vehicle databases with real-time vehicle state data to generate a weighted steering torque request, which is applied to the power steering assist system to compensate for disturbances, using data from cameras, radar, lidar, and inertial measurement units, and optionally employing artificial intelligence algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional ADAS uses forward-looking camera and perception sensor to generate steering torque overlay for lane-keeping, then lane-centering functionality is achieved, but vehicle disturbances from road banking and cross-winds are not adequately mitigated
Solution Approach 1:
The system obtains predicted driving condition data from geospatial databases and remote vehicle data before the vehicle actually encounters the disturbance conditions. This predictive information allows the system to prepare compensation strategies in advance, combining them with real-time vehicle state data to generate proactive steering torque requests that mitigate disturbances before they significantly impact vehicle handling.
Solution Approach 2:
The system integrates multiple data sources including geospatial road condition data, weather data, and remote vehicle data from multiple vehicles into a unified disturbance compensation framework. This multi-functional approach allows the same system to handle various disturbance types (road banking, cross-winds, uneven loads) using a comprehensive data fusion methodology.
2Reliability
If driver provides continuous steering input to counteract road banking and cross-winds, then vehicle control is maintained, but driver steering effort and fatigue increase
Solution Approach 1:
The system enables the vehicle to self-compensate for disturbances by automatically generating and applying steering torque requests based on predicted and real-time data. The power steering assist system autonomously adjusts steering torque to counteract detected disturbances, reducing the need for continuous driver intervention and minimizing driver steering effort while maintaining vehicle control stability.
Solution Approach 2:
The system continuously monitors real-time vehicle state data from IMUs and other sensors, comparing actual vehicle behavior against predicted conditions. This feedback loop allows the system to dynamically adjust steering torque requests, ensuring stable vehicle control while minimizing unnecessary driver steering input through automatic compensation.
3Measurement precision
If system combines multiple data sources including geospatial data and remote vehicle data, then disturbance prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system merges multiple data sources including geospatial road condition data, weather data, and remote vehicle data from a database into a unified predictive model. By combining these diverse data streams and processing them through an integrated system that generates comprehensive steering torque requests, the patent achieves high disturbance prediction accuracy despite the inherent complexity of handling multiple data sources.
Data Source
AI summary
A method and system for compensating for vehicle disturbances during vehicle operation, including: an algorithm for obtaining predicted driving condition data from a database, wherein the database includes one or more of geospatial data and remote vehicle data; an algorithm for obtaining real-time vehicle state data from equipment communicatively connected to a vehicle; an algorithm for combining the predicted driving condition data and the real-time vehicle state data to formulate a desired steering torque request necessary to compensate for predicted and actual driving conditions experienced by the vehicle; and an algorithm for providing the desired steering torque request to a power steering assist system of the vehicle to compensate for the predicted and actual driving conditions experienced by the vehicle.