Autonomous Driving Model Updates for Reduced-Sensor Vehicle Control
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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 slowed 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 or 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 is used to enable autonomous driving, then the navigation and control capabilities are improved, but the cost and device complexity increase
Solution Approach 1:
The patent extracts and removes unnecessary sensors from the autonomous vehicle system. Specifically, it eliminates the requirement for radar, LIDAR, and ultrasonic sensors by using a minimal set of optical sensors (cameras) combined with advanced image processing algorithms. This extraction principle directly reduces device complexity while maintaining autonomous driving functionality through software-based solutions that replace hardware-dependent approaches.
Solution Approach 2:
The patent replaces the mechanical/sensor-based detection system with a software-based image processing system. Instead of relying on multiple physical sensors (radar, LIDAR, ultrasonic), the system uses optical sensors combined with machine learning algorithms and computer vision techniques to perform detection, navigation, and control functions. This substitution reduces hardware complexity while achieving the same autonomous driving objectives through intelligent software processing.
2Reliability
If a substantial amount of hardware and sensors is used to enable autonomous driving, then the navigation and control capabilities are improved, but the cost increases
Solution Approach 1:
The patent adopts inexpensive optical sensors (standard cameras) instead of expensive specialized sensors like radar, LIDAR, or ultrasonic sensors. These cheaper camera-based systems can be mass-produced and integrated into vehicles at lower cost, making autonomous driving technology more economically viable and easier to manufacture while still achieving reliable navigation and control through advanced software processing.
3Device complexity
If advanced software configurations and machine learning models are implemented, then the autonomous driving operations are enabled with reduced hardware, but the processing requirements and computational complexity increase
Solution Approach 1:
The patent replaces the sensor-heavy mechanical detection system with a software-based intelligent processing system. Machine learning models and computer vision algorithms process images from simple optical sensors to achieve autonomous driving functions. This substitution shifts complexity from hardware to software, reducing physical device requirements while enabling sophisticated autonomous operations through intelligent algorithms that can learn and adapt to various driving scenarios.
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.