Autonomous Vehicle Risk Modeling for Mixed Traffic and Security Threats
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Solution Overview
Problem
Current autonomous and semi-autonomous vehicle systems lack adaptive risk modeling capabilities to effectively account for human, environmental, and non-human factors, leading to inadequate responses in real-world driving conditions, which may compromise safety.
Innovation Solution
A method and system for adaptive risk modeling that evaluates behavioral, mixed traffic, geography-dependent, sensor-surrounding, and security risk parameters using comparative autonomous, mix, geography-dependent, sensor-surrounding, and security risk models, transforming these into a risk analysis to inform real-time response actions for improved vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current algorithms and systems are used for autonomous vehicle risk assessment, then system simplicity is maintained, but the ability to adequately account for human factors, environmental factors, and non-human factors is insufficient
Solution Approach 1:
The risk assessment system is segmented into five distinct specialized models: comparative autonomous model (evaluates behavioral risk features), mix model (evaluates mixed traffic features), geography-dependent model (determines geography-dependent behavioral features), sensor-surrounding model (determines surrounding risk parameter), and security risk model (determines security threat parameter). Each model focuses on specific risk dimensions, enabling comprehensive coverage of human factors, environmental factors, and non-human factors while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The risk assessment system serves multiple functions simultaneously: it evaluates behavioral risks, mixed traffic conditions, geography-dependent behaviors, sensor-surrounding conditions, and security threats. The transformed outputs from all five models are integrated into a unified risk analysis that informs real-time response actions, making the system universally applicable to diverse driving scenarios and risk types.
2Reliability
If comprehensive risk modeling is implemented to cover all driving factors, then safety and adaptability are improved, but computational complexity and processing requirements increase
Solution Approach 1:
By dividing the comprehensive risk assessment into five specialized models, each handling specific risk dimensions, the computational complexity is distributed and managed more efficiently. Each model processes specific input features and produces focused outputs that are then transformed and integrated, reducing the computational burden compared to a single monolithic model while maintaining comprehensive safety coverage.
Solution Approach 2:
The system implements feedback through the transformation of outputs from all five models into a unified risk analysis using exposure parameters. This feedback mechanism allows the system to continuously adjust and refine risk assessments based on integrated information from behavioral, traffic, geographical, sensor, and security models, improving safety through iterative optimization while managing computational complexity through structured information flow.
Data Source
AI summary
A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.