Driving Responsiveness Detection for Vehicle Maintenance
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Solution Overview
Problem
It is challenging for autonomous driving systems to detect issues such as tire wear, tire leaks, and wheel misalignment, which can affect vehicle performance, as these conditions are difficult for autonomous driving computers to identify.
Innovation Solution
A predictive driving behavior system using vehicle sensors and a computer that determines a driving responsiveness value and score by analyzing transition probability matrices derived from Hidden Markov Model algorithms, allowing for the detection of changes in driving aggressiveness and potential maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous driving computer is used to detect vehicle conditions, then automation level is improved, but detection capability for tire wear and wheel misalignment deteriorates
Solution Approach 1:
The patent introduces an intermediary system that uses sensor data (accelerometers, gyroscopes, wheel speed sensors) to detect driving behavior patterns and infer vehicle conditions. This intermediary analysis layer bridges the gap between autonomous driving control and maintenance detection, allowing the system to indirectly detect tire wear and wheel misalignment through changes in driving dynamics without requiring direct physical inspection sensors.
Solution Approach 2:
The patent replaces direct mechanical detection methods with computational analysis of sensor data. Instead of using mechanical sensors to directly measure tire wear or wheel alignment, the system uses electronic sensors to capture vehicle dynamics and employs algorithms to analyze driving behavior patterns, substituting mechanical measurement with computational inference.
2Ease of operation
If traditional maintenance scheduling is used, then ease of operation is improved, but maintenance timing precision deteriorates
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors driving behavior patterns and vehicle sensor data to dynamically adjust maintenance scheduling. The system provides feedback loops where detected changes in driving aggressiveness or vehicle response are fed back into the maintenance decision-making process, allowing for real-time adjustments to maintenance timing based on actual vehicle conditions rather than fixed schedules.
Solution Approach 2:
The patent transitions from static, predetermined maintenance schedules to dynamic maintenance planning that adapts to changing vehicle conditions and driving patterns. The system continuously updates maintenance recommendations based on real-time analysis of sensor data and detected changes in driving behavior, making the maintenance schedule flexible and responsive to actual vehicle needs.
Data Source
AI summary
A system may include a plurality of vehicle sensor and a computer comprising a processor and memory storing instructions executable by the processor. One of the instruction may comprise to determine a driving responsiveness (DR) value using a weighted sum comprising indices of a transition probability matrix (Q), Q being derived from likelihood of transition data (Λ) between a plurality of driving modes from a set of interacting multiple model (IMM) instruction.