Autonomous Driving Override Model for Liability-Aware Obstacle Avoidance
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Solution Overview
Problem
Current autonomous vehicle navigation systems face challenges in safely and accurately navigating roadways while adhering to liability constraints and ensuring scalability for widespread adoption.
Innovation Solution
The system employs cameras to provide autonomous vehicle navigation features, combining image analysis with GPS data, sensor data, and map data to make navigational decisions, while also considering liability constraints and safety assurance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle systems implement comprehensive safety assurance models with liability constraints, then safety and accuracy of navigation improve, but system complexity and computational requirements increase
Solution Approach 1:
The safety model is segmented into distinct functional modules: hazard detection module, liability rule module, and navigational response module. Each module processes specific aspects of safety independently, allowing the system to maintain high safety standards while managing complexity through modular architecture. The hazard detection module identifies potential accidents, the liability rule module evaluates responsibility, and the navigational response module executes appropriate actions.
Solution Approach 2:
The system performs preliminary classification of hazards into categories (e.g., pedestrian hazards, vehicle hazards, infrastructure hazards) before detailed analysis. Liability rules are pre-defined and stored in a database, allowing the system to quickly reference applicable rules without complex real-time reasoning. This preliminary structuring reduces computational burden during critical navigation decisions.
2Measurement precision
If autonomous vehicle systems use detailed image analysis and multiple sensors for navigation decisions, then navigation accuracy and safety improve, but computational cost and processing time increase
Solution Approach 1:
The system applies partial action by using different levels of analysis for different hazards. For low-risk situations, basic sensor data suffices. For high-risk hazards detected by the hazard detection module, the system activates more computationally intensive image analysis and detailed liability rule evaluation. This selective approach maintains high accuracy when needed while reducing computational cost during normal operation.
3Reliability
If autonomous vehicle systems implement comprehensive liability rule evaluation, then accountability and safety assurance improve, but processing time and system response speed decrease
Solution Approach 1:
The liability rule evaluation applies local quality by focusing detailed analysis only on relevant hazards and situations. The system identifies which liability rules apply based on the specific hazard category and contextual factors, rather than evaluating all possible rules. This selective evaluation maintains accountability and legal compliance while significantly reducing processing time for navigation decisions.
4Difficulty of detecting and measuring
If autonomous vehicle systems use multiple cameras and sensors for environment monitoring, then detection capability and safety improve, but device complexity and manufacturing cost increase
Solution Approach 1:
The system employs multi-functionality by using cameras and sensors for multiple purposes simultaneously. The same camera array used for basic navigation and obstacle detection also performs hazard classification, liability rule evaluation, and post-incident analysis. This universal use of components reduces the need for dedicated specialized hardware, lowering manufacturing costs while maintaining high detection capability across all safety-critical functions.
Data Source
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AI summary
An autonomous system may selectively displace human driver control of a host vehicle. The system may receive an image representative of an environment of the host vehicle and detect an obstacle in the environment of the host vehicle based on analysis of the image. The system may monitor a driver input to a throttle, brake, and/or steering control associated with the host vehicle. The system may determine whether the driver input would result in the host vehicle navigating within a proximity buffer relative to the obstacle. If the driver input would not result in the host vehicle navigating within the proximity buffer, the system may allow the driver input to cause a corresponding change in one or more host vehicle motion control systems. If the driver input would result in the host vehicle navigating within the proximity buffer, the system may prevent the driver input from causing the corresponding change.