Autonomous Vehicle Camera Navigation With Safety-Value Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicles and aircraft systems struggle to navigate unpredictably changing environments, particularly in public spaces, due to high data volume requirements for detailed maps and insufficient response to dynamic obstacles and fellow road users.
Innovation Solution
A system comprising a navigation module, control module, recognition module, safety-determining module, and communication module, utilizing camera images for navigation point recognition, safety assessment, and control value generation based on deep learning, allowing the system to learn from operator inputs and adapt to varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed high-resolution maps are used for navigation in unpredictable environments, then navigation accuracy is improved, but data volume requirements become unacceptably high
Solution Approach 1:
The system segments the navigation problem into two parts: pre-stored reference camera images capture static environmental features (landmarks, road layout) while live camera images capture dynamic current state. By comparing these segmented data sources, the system achieves accurate positioning without requiring complete detailed maps of the entire environment, thus reducing data volume while maintaining navigation precision.
2Difficulty of detecting and measuring
If sensors are used to detect obstacles and road users, then detection capability is improved, but the system cannot determine the necessary response
Solution Approach 1:
The system implements feedback by continuously comparing live camera images with pre-stored reference images and evaluating the degree of correspondence. This comparison provides feedback about the current navigation state and any deviations from the expected route, enabling the system to automatically determine appropriate responses such as route correction or obstacle avoidance without requiring complex sensor data processing.
3Loss of time
If the system is trained only on known circumstances, then training time is reduced, but the system cannot handle unseen situations safely
Solution Approach 1:
The system performs preliminary action by pre-storing multiple reference camera images of navigation points and destinations captured under various conditions (different times, weather, lighting). When navigating, the system compares live images against this pre-prepared reference library, allowing it to handle unseen situations by matching them against previously captured reference data, thus ensuring safety without requiring extensive real-time training.
Data Source
AI summary
System for controlling an autonomous vehicle on the basis of control values and acceleration values, having a safety-determining module configured to receive live images from a camera, to receive recorded stored images preprocessed for image recognition from an internal safety-determining module data storage, to receive navigation instruction(s) from a navigation module; to compare the live images with the stored images to determine a degree of correspondence; and to determine a safety value which indicates the extent to which the determined degree of correspondence suffices to execute the navigation instruction(s); wherein a control module is configured to receive the navigation instruction(s); to receive the live images; to receive the safety value; to compare the safety value to a predetermined value; and if the safety value is greater than the predetermined value, to convert the navigation instruction(s) and the camera images into control values and acceleration values for the autonomous vehicle.