Autonomous Vehicle Impact Detection With Remote Damage Triage
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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 time-consuming inspections, with existing sensor systems often resulting in false positives or negatives regarding the need for servicing.
Innovation Solution
A method and system that utilize a remote computing system to analyze sensor data from autonomous vehicles, creating algorithms to classify the severity of impact events, determining whether maintenance is required, and providing routing instructions for inspections or continued operation, leveraging machine learning models and sensor data from a fleet of vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is analyzed to detect impact incidents, then detection capability is improved, but false positives and negatives occur
Solution Approach 1:
The sensor data analysis is segmented into multiple independent evaluation dimensions including acceleration thresholds, duration metrics, frequency analysis, and pattern recognition. Each dimension independently evaluates aspects of the impact event, and only when multiple dimensions confirm the same classification does the system trigger a service alert. This segmentation reduces false positives by requiring corroboration across multiple measurement categories rather than relying on a single threshold.
Solution Approach 2:
The system implements feedback loops where service data from actual vehicle inspections is fed back into the machine learning models to continuously refine impact classification accuracy. The system learns from confirmed impact events and non-events, adjusting sensitivity thresholds and classification parameters based on real-world outcomes. This feedback mechanism progressively reduces both false positives and false negatives by adapting to actual vehicle conditions and impact patterns.
2Measurement precision
If comprehensive sensor analysis is performed to assess damage severity, then assessment accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary triage by immediately evaluating basic impact parameters such as acceleration magnitude and duration as soon as sensor data becomes available. This preliminary assessment quickly categorizes events into obvious non-events, clear impacts requiring service, and ambiguous cases needing further analysis. By performing this initial classification immediately, the system reduces processing time for the majority of events while reserving comprehensive analysis only for cases where it is truly necessary.
Solution Approach 2:
The system applies partial analysis to most events and excessive (comprehensive) analysis only when needed. For typical impact events, the system evaluates a subset of sensor parameters and classification criteria sufficient for confident decision-making. For ambiguous or severe events, the system then applies full comprehensive analysis including all sensor modalities and detailed pattern matching. This selective approach optimizes processing time while maintaining assessment accuracy for critical cases.
3Extent of automation
If machine learning algorithms are deployed in autonomous vehicles, then self-assessment capability is improved, but computational resources are consumed
Solution Approach 1:
The machine learning workload is segmented between edge computing in the autonomous vehicle and cloud-based processing. The vehicle's onboard system performs lightweight real-time classification using pre-trained models for immediate impact detection. More computationally intensive tasks such as model retraining, large-scale pattern analysis, and fleet-wide learning are performed in the cloud using aggregated data from multiple vehicles. This segmentation enables self-assessment capability while distributing computational energy consumption across appropriate platforms.
Solution Approach 2:
The system uses simplified copy versions of comprehensive impact assessment models for real-time onboard processing. Instead of running full-scale complex machine learning algorithms in the vehicle, the system deploys streamlined model copies that capture essential classification logic with reduced computational requirements. These simplified models are periodically updated with improved versions from the cloud, enabling energy-efficient self-assessment while maintaining access to advanced analytical capabilities when needed.
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.