Autonomous Vehicle Camera Calibration With Reduced Optical Sensors
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Solution Overview
Problem
Current self-driving vehicles require a substantial amount of hardware and sensors to function effectively, leading to increased costs and slower adoption due to the complexity and expense of these systems.
Innovation Solution
The implementation of a vehicle computing environment that utilizes a reduced set of optical sensors, combined with advanced software configurations and machine learning models, to enable autonomous driving operations, particularly in semi-truck and freight vehicle applications, by processing sensor data to generate autonomous vehicle models for navigation and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a substantial amount of hardware and sensors are used for autonomous driving, then the navigation and control effectiveness is improved, but the cost and device complexity increase
Solution Approach 1:
The patent extracts and removes unnecessary sensors from the autonomous vehicle system, reducing the sensor suite from multiple redundant sensors to a minimal set of essential optical sensors while maintaining autonomous driving functionality through advanced software processing
Solution Approach 2:
The patent makes a single optical sensor perform multiple functions by using advanced software configurations and machine learning models to extract various driving information (lane detection, obstacle detection, navigation) from limited sensor inputs, replacing the need for multiple specialized sensors
2Reliability
If a substantial amount of hardware and sensors are used for autonomous driving, then the navigation and control effectiveness is improved, but the cost increases
Solution Approach 1:
The patent replaces expensive, complex sensor hardware with more affordable optical sensors combined with software-based processing, reducing the overall system cost while maintaining autonomous driving capabilities through computational methods
Data Source
AI summary
Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.