Autonomous Vehicle Navigation Liability Rule Testing
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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 essential for widespread adoption.
Innovation Solution
The system employs cameras to analyze the environment, process images, and determine navigational actions based on driving policies and accident liability rules, ensuring safe navigation by assessing potential liabilities and selecting viable actions that minimize risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle navigation systems implement comprehensive safety assurance models with liability rule testing, then safety and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The navigation system is divided into distinct functional modules: image capture device for environmental sensing, processing device for liability rule testing and decision-making, and control mechanisms for executing navigational actions. This segmentation allows each component to be optimized independently while maintaining overall system reliability without excessive complexity.
Solution Approach 2:
The system performs preliminary testing of planned navigational actions against accident liability rules before actual execution. By evaluating potential actions in advance and filtering out those that would create liability issues, the system ensures safety and reliability while maintaining manageable complexity through proactive rather than reactive decision-making.
2Reliability
If the system tests multiple potential navigational actions against accident liability rules, then safety assurance is improved, but processing time and productivity are reduced
Solution Approach 1:
The system generates multiple potential navigational actions and tests them against liability rules, but implements only the first action that satisfies safety requirements. This partial action approach ensures thorough safety checking while avoiding the excessive processing that would result from evaluating and optimizing all possible actions, thus maintaining navigational efficiency.
Solution Approach 2:
When a planned navigational action passes the accident liability rule test, the system proceeds to implement it without further deliberation. This skipping of redundant evaluation steps for already-validated actions maintains safety assurance while improving navigational efficiency by avoiding unnecessary processing delays.
3Measurement precision
If the system relies heavily on image capture devices and processing for navigation decisions, then measurement precision is improved, but loss of time in processing images increases
Solution Approach 1:
The system continuously captures and pre-processes images from the environment to build an up-to-date model of the roadway context before navigation decisions are required. This preliminary environmental perception allows the system to make rapid, accurate decisions by relying on pre-analyzed data rather than processing images in real-time during critical decision moments.
Solution Approach 2:
The processing device acts as an intermediary that pre-analyzes image data from the capture device and translates it into structured environmental information. This intermediary processing layer enables the navigation system to access precise environmental perceptions without the time penalty of real-time image analysis during decision-making.
Data Source
AI summary
A method including operations to obtain a planned driving action for accomplishing a navigational goal of a host vehicle on a roadway, identify a planned trajectory for the host vehicle, corresponding to the planned driving action, identify, from sensor data representative of an environment of the host vehicle, an occluded location of a potential object that is occluded from view of the host vehicle, identify a possible trajectory of the potential object, based on possible movement of the potential object from the location into the roadway, identify an intersection of the planned trajectory for the host vehicle with the possible trajectory for the potential object, determine a safety action of the host vehicle to respond to the possible movement of the potential object, and apply the safety action to change the planned driving action of the host vehicle.