Autonomous Vehicle Area Scanning With Passive Sensor Search
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Solution Overview
Problem
Autonomous or semi-autonomous vehicles face challenges in ensuring safe operation, particularly in unusual environmental conditions or when sensors or components are damaged, leading to increased risks due to impaired functionality.
Innovation Solution
The implementation of a computer-implemented method for automatic passive searching using autonomous vehicle components, which includes receiving a situation trigger, determining search parameters, identifying vehicles with sensors, generating search criteria, communicating with processors, obtaining sensor data, evaluating the data, and implementing actions such as sending messages to law enforcement agencies, along with systems for route determination, navigation, and risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles operate in unusual environmental conditions or with damaged sensors, then vehicle operation continues, but safety and reliability deteriorate
Solution Approach 1:
The system performs preliminary assessment of environmental conditions and sensor functionality before autonomous operation. The processor evaluates whether current conditions are suitable for autonomous operation, and preemptively requests manual operation or alerts the operator when conditions are unfavorable, preventing unsafe operation before it occurs.
Solution Approach 2:
The system continuously monitors environmental conditions and sensor status, providing feedback to the operator through alerts and notifications. This feedback loop enables real-time adjustments to operation mode, ensuring safety by transitioning from autonomous to manual operation when conditions deteriorate.
2Ease of operation
If advanced sensors and autonomous operation features are implemented, then vehicle control capability improves, but system complexity increases
Solution Approach 1:
The system automatically assesses its own operational suitability by monitoring environmental conditions and sensor functionality. The processor autonomously determines whether conditions favor autonomous or manual operation, reducing the burden on the operator to manually evaluate complex system states while maintaining high control capability.
Solution Approach 2:
The system dynamically adjusts operational parameters based on environmental conditions and sensor status. By changing the operational mode parameter between autonomous and manual based on assessed conditions, the system optimizes control capability while managing complexity through adaptive parameter adjustment rather than fixed complex control logic.
3Reliability
If manual operation is required due to unfavorable conditions, then safety improves, but productivity decreases
Solution Approach 1:
The system dynamically transitions between autonomous and manual operation modes based on real-time environmental assessment. This dynamic adaptability allows the vehicle to maintain high productivity through autonomous operation when conditions are favorable, while ensuring safety through manual operation when conditions deteriorate, optimizing the trade-off between productivity and safety.
Data Source
AI summary
Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.