Autonomous Vehicle Damage Detection With Confidence-Based Safe Maneuvering
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Solution Overview
Problem
There is a need for systems and methods to effectively utilize the connected nature of vehicle owners, their vehicles, and insurance companies, particularly in monitoring and responding to hazards or damage to autonomous vehicles.
Innovation Solution
A system comprising sensors, a processor, and a decision engine that collects and analyzes data to determine a confidence factor for hazards or damage, triggering commands to move the vehicle to a safe location and sending alerts to operators or authorities if thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is continuously collected and analyzed using a decision engine to determine hazard confidence factors, then vehicle safety monitoring capability is improved, but system complexity and computational resource requirements increase
Solution Approach 1:
The detection system is divided into multiple independent sensors (accelerometer, gyroscope, camera, microphone) that can be selectively activated based on the situation. Each sensor handles specific aspects of hazard detection, allowing the system to achieve comprehensive monitoring without requiring all sensors to operate simultaneously, thus reducing overall system complexity while maintaining high reliability.
Solution Approach 2:
The system performs preliminary analysis of sensor data to generate confidence factors before triggering full hazard response protocols. By pre-processing sensor inputs and calculating confidence levels in advance, the decision engine can quickly determine whether further action is needed, reducing computational burden during critical moments while maintaining accurate safety monitoring.
2Speed
If the system automatically moves the autonomous vehicle to a safe location upon detecting hazards, then response time and safety are improved, but control over the vehicle and potential false movements increase
Solution Approach 1:
The system provides multiple feedback mechanisms including confidence factor calculations, alert generation to operators, and logging of detection events. This feedback loop allows operators to review system decisions, verify hazard authenticity, and maintain control over automatic maneuvers. The feedback ensures that automatic vehicle movement occurs only when hazards are confidently detected, preventing false movements while maintaining rapid response capability.
3Loss of information
If alerts are sent to operators, owners, and authorities upon hazard detection, then communication and safety response are improved, but information processing overhead and notification management complexity increase
Solution Approach 1:
The alert system uses local quality by sending different types of notifications to different recipients based on the specific hazard detected and the confidence factor. Critical hazards with high confidence trigger immediate alerts to all parties (operators, owners, authorities), while lower confidence hazards may only notify operators or log the event. This differentiated approach ensures effective communication without overwhelming any single recipient with unnecessary notifications.
Data Source
AI summary
A method disclosed comprises receiving sensor data indicating probable damage to the autonomous vehicle from a sensor aboard the autonomous vehicle. The method analyzing the sensor data using a decision engine to determine a confidence factor associated with the probable damage to the autonomous vehicle, and, based on the confidence factor meeting a threshold value, determining an occurrence of a condition causing damage. The method includes sending a command to a vehicle actuation manager to move the autonomous vehicle to a safe location in response to the condition and sending an alert. Systems implementing similar aspects are also disclosed.