Autonomous Driving Mode Switching With Camera-Only Sensor Reduction
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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 structures 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 for autonomous driving, then the vehicle can function effectively and safely, but the cost increases and adoption slows
Solution Approach 1:
The patent extracts and removes unnecessary sensors from the autonomous driving system, retaining only the essential optical sensors (cameras) while eliminating redundant hardware such as LIDAR, RADAR, and ultrasonic sensors. This extraction principle reduces system complexity and cost while maintaining core autonomous driving functionality through sophisticated software processing of camera data.
Solution Approach 2:
The patent uses multiple cameras positioned at different locations on the vehicle to capture overlapping views of the environment. By processing these multiple optical copies of the scene through software algorithms, the system reconstructs three-dimensional spatial information and depth perception that would traditionally require expensive specialized sensors, thereby reducing hardware requirements while maintaining safety.
2Reliability
If a substantial amount of hardware and sensors is used for autonomous driving, then the vehicle can function effectively and safely, but the cost increases
Solution Approach 1:
The patent replaces expensive, complex sensors (LIDAR, RADAR, ultrasonic sensors) with relatively inexpensive optical cameras. While individual cameras are less durable in harsh environments compared to specialized sensors, the system uses multiple redundant camera units that can be replaced more easily and cheaply, reducing the overall cost barrier for autonomous vehicle manufacturing while maintaining safety through software redundancy.
3Device complexity
If advanced software structures and machine learning models are implemented, then autonomous driving operations can be enabled with reduced hardware, but processing requirements increase
Solution Approach 1:
The patent implements machine learning models that are pre-trained on vast datasets of driving scenarios before deployment in the vehicle. This preliminary training allows the software to efficiently process real-time camera data with reduced computational energy requirements during actual autonomous driving operations, as the heavy lifting of pattern recognition has already been performed during offline training phases.
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.