Autonomous Vehicle Navigation with Safe-State Distance Checking
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Solution Overview
Problem
Current autonomous vehicle navigation systems lack a scalable and interpretable mathematical model for safety assurance, particularly in navigating roadways while adhering to liability constraints, which is crucial for widespread adoption.
Innovation Solution
The system employs cameras to analyze images and process data from GPS, sensors, and maps to determine navigational actions, testing these actions against accident liability rules to ensure safe and liability-free navigation, using a processing device to select and implement viable actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle navigation systems implement comprehensive safety checks and liability rule testing for each navigational action, then safety assurance and liability compliance are improved, but system complexity and computational processing time increase
Solution Approach 1:
The system performs preliminary classification of navigational actions into safe and unsafe categories using a trained machine learning model before executing detailed liability rule testing. This preliminary action filters out obviously unsafe actions, reducing the computational burden of comprehensive safety checks while maintaining high safety assurance standards.
Solution Approach 2:
The safety assurance system is segmented into multiple independent modules: image capture, machine learning classification, liability rule testing, and navigational action selection. Each module operates independently with defined interfaces, improving system reliability through modular design while making the overall system more manageable and maintainable.
2Reliability
If autonomous vehicle navigation systems implement comprehensive safety checks and liability rule testing for each navigational action, then safety assurance and liability compliance are improved, but processing time and computational resources increase
Solution Approach 1:
The machine learning model performs preliminary classification of navigational actions as safe or unsafe before detailed liability rule testing. This preliminary action quickly filters out obviously unsafe actions, reducing the number of actions requiring comprehensive processing and thereby reducing overall processing time while maintaining safety assurance.
Solution Approach 2:
The machine learning model is trained to perform multiple functions simultaneously: classifying navigational action safety, predicting potential accidents, and estimating liability risks. This multi-functionality eliminates the need for separate processing steps, reducing computational overhead and processing time while maintaining comprehensive safety assurance.
3Measurement precision
If autonomous vehicle navigation systems use sophisticated image analysis and machine learning models to classify navigational actions, then navigation safety and liability assessment accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The machine learning model is designed as a universal system that performs multiple assessment functions: classifying navigational action safety, predicting accident likelihood, and estimating liability risks. This single multi-functional model achieves high measurement precision across all these dimensions while avoiding the complexity of multiple separate systems.
Solution Approach 2:
The system merges image capture, image analysis, safety classification, and liability assessment into an integrated processing pipeline. By combining these functions into a unified system with shared computational resources and data flows, the patent achieves high assessment accuracy while reducing overall device complexity compared to separate independent systems.
Data Source
AI summary
Systems and methods are provided for navigating a host vehicle. In one implementation, a system may include a processing device configured to receive an image acquired by an image capture device; determine a planned navigational action for accomplishing a navigational goal of the host vehicle; analyze the at least one image to identify a first target vehicle ahead of the host vehicle and a second target vehicle ahead of the first target vehicle; determine a next-state distance between the host vehicle and the second target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a maximum braking capability of the host vehicle and a current speed of the host vehicle; and cause the vehicle to implement the planned navigational action if the stopping distance is less than the determined next-state distance.