Vehicle Contextual Risk Profiling for Unroadworthy Traffic Detection
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Solution Overview
Problem
Current driver assistance and autonomous vehicle systems assume surrounding vehicles are in good working order, lacking the ability to assess the roadworthiness of nearby vehicles, which can lead to increased risk of collisions and legal challenges.
Innovation Solution
A computing device in a vehicle evaluates contextual risk profiles by using publicly available data, such as vehicle inspection records and license plate information, to identify the roadworthiness of surrounding vehicles, allowing for risk mitigation actions like adjusting distance or providing alerts without relying on vehicle-to-vehicle communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If driver assistance and autonomous vehicle systems assume surrounding vehicles are in good working order, then the system complexity is reduced and ease of operation is improved, but the reliability and safety are worsened due to inability to assess roadworthiness of nearby vehicles
Solution Approach 1:
The system performs preliminary assessment of surrounding vehicles by retrieving and analyzing inspection records, registration status, and ownership information before the host vehicle approaches or interacts with these vehicles. This advance evaluation allows the system to identify potential risks associated with unroadworthy vehicles, enabling proactive safety measures such as increased following distance or alternative routing decisions.
2Reliability
If the system retrieves and analyzes public data about surrounding vehicles to assess roadworthiness, then the reliability and safety are improved, but the device complexity and data processing requirements are worsened
Solution Approach 1:
The system employs an intermediary data processing layer that acts as a mediator between raw public data sources (inspection records, registration databases) and the autonomous vehicle's decision-making algorithms. This intermediary layer pre-processes, validates, and structures the retrieved data into standardized formats, reducing the computational burden on the vehicle's primary processing systems while maintaining comprehensive safety assessment capabilities.
Solution Approach 2:
The system performs preliminary assessment of surrounding vehicles by retrieving and analyzing inspection records, registration status, and ownership information before the host vehicle approaches or interacts with these vehicles. This advance evaluation allows the system to identify potential risks associated with unroadworthy vehicles, enabling proactive safety measures such as increased following distance or alternative routing decisions.
3Reliability
If the system takes proactive measures around at-risk vehicles such as adjusting distance or providing alerts, then the safety is improved, but the loss of time and operational efficiency are worsened
Solution Approach 1:
The system applies partial safety measures based on the assessed risk level of surrounding vehicles. For low-risk vehicles, the system maintains normal operating parameters and minimal monitoring. For high-risk vehicles identified through data analysis, the system implements enhanced monitoring and selective safety measures such as increased following distance or speed reductions only in the vicinity of at-risk vehicles, rather than applying conservative measures universally. This differentiated approach maintains safety while minimizing unnecessary time loss.
Data Source
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AI summary
A method for evaluating contextual risk profiles at a computing device (110, 212, 312) in a vehicle (310), the method including obtaining information about a proximate vehicle (320, 330); utilizing the information to create a risk profile for the proximate vehicle (320, 330); and based on the risk profile, initiating an action at computing device (110, 212, 312).