Autonomous Vehicle Navigation With Driver Override Buffering
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating safely and efficiently while adhering to liability constraints and ensuring scalability and safety assurance, lacking an interpretable, mathematical model for navigation that can be verified and applied across millions of vehicles.
Innovation Solution
The system utilizes cameras and processing devices to analyze environmental images, incorporating GPS data and sensor information to determine navigational actions, including braking and acceleration capabilities, and implement driving policies to ensure safe navigation while considering potential interactions and liability rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use complex navigation systems to ensure safety and adhere to liability constraints, then safety assurance is improved, but device complexity increases
Solution Approach 1:
The navigation system is divided into separate functional modules: hazard detection module, liability constraint module, and navigation decision module. Each module independently processes specific aspects of safe navigation, making the overall complex system manageable and verifiable through modular design.
Solution Approach 2:
A processing device acts as an intermediary between sensor inputs and navigation outputs, applying liability constraints as intermediate computational steps. This intermediary layer ensures that safety rules are systematically applied before final navigation decisions are made.
2Reliability
If autonomous vehicles implement comprehensive safety verification systems, then reliability is improved, but ease of manufacture deteriorates
Solution Approach 1:
The navigation system implements universal safety verification procedures that can be applied across different vehicle models and manufacturing contexts. The same liability constraint algorithms and hazard detection methods are used universally, simplifying manufacturing standardization while maintaining safety verification.
3Measurement precision
If autonomous vehicles use detailed environmental analysis to identify target vehicles and calculate distances, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary identification of target vehicles and pre-calculation of distance metrics as part of the hazard detection process. By preparing this information in advance before navigation decisions are required, the system achieves high measurement precision without adding critical processing delays.
Data Source
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.