Autonomous Vehicle Impact Detection for Damage-Based Routing
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently detecting and assessing damage from severe road events, leading to costly and inefficient inspections, as existing sensor systems may inaccurately identify the need for servicing or fail to detect damage, potentially causing further damage or safety risks.
Innovation Solution
A system and method that utilizes a remote computing system to analyze sensor data from autonomous vehicles, creating algorithms to classify the severity of impact events, determining whether maintenance is needed, and providing routing instructions for inspections, while avoiding false positives and scheduling necessary repairs based on machine learning models and sensor data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor systems are used to detect impact incidents in autonomous vehicles, then detection capability is improved, but false positives and false negatives occur leading to inaccurate damage assessment
Solution Approach 1:
The system segments the damage assessment process into multiple independent analysis components: initial impact detection from sensor data, severity classification through machine learning models, and final determination of servicing needs. Each segment handles a specific aspect of the assessment, reducing errors in any single component and improving overall reliability while maintaining detection precision.
2Reliability
If all autonomous vehicles undergo regular inspections to check for damage, then damage detection reliability is improved, but inspection costs and time consumption increase
Solution Approach 1:
The system performs preliminary damage assessment actions continuously in the background using sensor data and machine learning models before formal inspections are scheduled. By pre-classifying impact severity and identifying vehicles that likely need inspection, the system filters out vehicles that can wait for routine scheduled inspections, reducing immediate inspection time and costs while maintaining reliable damage detection.
3Reliability
If frequent inspections are conducted to ensure vehicle safety, then safety reliability is improved, but operational productivity decreases
Solution Approach 1:
The system applies partial inspection action by using machine learning models to assess only the necessary portion of vehicle damage based on sensor data, rather than conducting full physical inspections on all vehicles. This selective approach maintains vehicle safety by identifying critical issues while allowing vehicles without detected problems to remain operational, thus preserving productivity.
4Measurement precision
If manual inspection processes are used to assess vehicle damage, then assessment accuracy is improved, but operational efficiency and cost-effectiveness worsen
Solution Approach 1:
The system replaces manual mechanical inspection processes with automated sensor-based detection and machine learning-based damage assessment. Sensors continuously collect data from vehicles, and machine learning models automatically analyze this data to assess damage accuracy, eliminating the need for manual inspection while maintaining or improving assessment accuracy and significantly increasing inspection efficiency.
Data Source
AI summary
The present technology is effective to cause at least one processor to collect sensor data from at least one sensor on an autonomous vehicle, wherein the sensor data includes a plurality of measurements from the at least one sensor, identify, from the sensor data, at least one measurement from the plurality of measurements that is outside a threshold measurement for the at least one sensor and is indicative of an impact incident, send the sensor data to a remote computing system, and receive, in response to the sending of the sensor data that is indicative of the impact incident, routing instructions from the remote computing system.