Vector Data Structure for Autonomous Vehicle Distance Measurement
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Solution Overview
Problem
Existing autonomous vehicle detection and tracking systems face inaccuracies due to flawed sensor data representation, leading to potential safety issues during navigation and trajectory planning.
Innovation Solution
The use of vector data structures to determine distances and directions from reference points to nearest objects in the environment, enabling more accurate object detection and trajectory planning by discretizing the environment into cells and employing bilinear interpolation for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor data representation methods are used, then the system is simpler to implement, but the accuracy of object detection and distance measurement deteriorates
Solution Approach 1:
The environment is discretized into a grid of cells, with each cell containing vector data representing the nearest object. This segmentation allows for efficient storage and retrieval of distance information while improving measurement precision through structured spatial organization.
Solution Approach 2:
A vector map data structure is introduced as an intermediary between raw sensor data and trajectory planning algorithms. This intermediary layer pre-processes and organizes distance and direction information, enabling faster and more accurate queries during navigation without increasing real-time computational complexity.
2Reliability
If inaccurate sensor data representation is used, then the system is easier to implement, but the safety of autonomous vehicle operation deteriorates
Solution Approach 1:
The system pre-computes and stores vector data for each grid cell during environment scanning, including distance and direction to the nearest object. This preliminary action ensures that accurate safety-critical information is readily available during trajectory planning without requiring complex real-time calculations.
Solution Approach 2:
The patent replaces complex geometric calculations and object detection algorithms with pre-computed vector data structures. Instead of performing mechanical computations during navigation, the system uses stored vector information to rapidly determine safe trajectories, improving reliability while maintaining manageable system complexity.
3Measurement precision
If detailed vector data is stored for each cell, then the accuracy of trajectory planning improves, but the memory requirements and data processing complexity increase
Solution Approach 1:
Each grid cell stores only the locally relevant vector data representing the nearest object in that specific cell. This local quality approach ensures that trajectory planning has access to precise local distance and direction information without requiring global environmental data, reducing overall data quantity while maintaining planning accuracy.
Data Source
AI summary
Techniques for generating more accurate determinations of object proximity by using vectors in data structures based on vehicle sensor data are disclosed. Vectors reflecting a distance and direction to a nearest object edge from a reference point in a data structure are used to determine a distance and direction from a point of interest in an environment to a nearest surface. In some examples, a weighted average query point response vector is determined using the determined distance vectors of cells neighboring the cell in which the point of interest is located and nearest to the same object as the query point, providing a more accurate estimate of the distance to the nearest object from the point of interest.