Cross-Vehicle Camera Calibration for Reduced Sensor Autonomy
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Solution Overview
Problem
Current self-driving vehicles require a significant amount of hardware and sensors to function effectively, leading to increased costs and slower adoption of autonomous technology.
Innovation Solution
The use of a vehicle computing environment with a reduced sensor suite, primarily utilizing optical sensors, to lower costs and simplify the implementation of autonomous driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full sensor suite is used for autonomous driving, then reliability and safety are improved, but device complexity and cost increase
Solution Approach 1:
The patent combines data from multiple sensors (cameras, LIDAR, radar) through sensor fusion algorithms to achieve reliable autonomous driving perception. By merging complementary information from different sensor types, the system maintains high reliability while avoiding the need for redundant individual sensors, thus reducing overall system complexity.
Solution Approach 2:
The patent employs multi-functional sensors that can perform multiple detection tasks. For example, cameras are used for both object detection and lane recognition, while LIDAR serves both distance measurement and 3D mapping functions. This multi-functionality reduces the total number of sensors needed while maintaining comprehensive environmental awareness.
2Measurement precision
If a full sensor suite is used for autonomous driving, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent dynamically adjusts sensor parameters such as field of view, detection range, and sampling frequency based on driving conditions. For example, the system increases LIDAR scanning frequency only when obstacles are detected, and adjusts camera exposure settings according to lighting conditions. This optimizes measurement precision while reducing unnecessary sensor activation and associated costs.
Solution Approach 2:
The patent implements selective sensor activation where not all sensors operate at full capacity continuously. Instead, the system activates specific sensors or increases their detection intensity only when and where needed based on environmental context, achieving high measurement precision for critical measurements while reducing overall hardware utilization costs.
3Adaptability or versatility
If multiple sensors are used for autonomous driving, then autonomous driving capabilities are improved, but ease of manufacture decreases
Solution Approach 1:
The patent divides the autonomous driving system into modular functional units (perception module, decision module, control module) with standardized interfaces. Each sensor type is integrated as a separate module that can be independently manufactured, tested, and replaced. This segmentation simplifies the manufacturing process while maintaining comprehensive autonomous driving capabilities.
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.