Neural Network Environment Representation for Autonomous Vehicles
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Solution Overview
Problem
Current systems for autonomous vehicles and robots face challenges in efficiently determining environmental information, such as object locations and navigation paths, due to the need for separate processes that require significant time and resources, which is undesirable for low-latency decisions and devices with limited computing resources.
Innovation Solution
A neural network framework that uses multi-view monocular inputs to detect 3D objects and segment the environment in a unified Bird's-Eye View (BEV) space, allowing for joint 3D object detection and segmentation with minimal additional computational cost, using a scale-aware dynamic label assignment and BEV centerness weighting, and pre-training on 2D data to improve 3D task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate processes are used to determine object locations and navigation paths, then measurement precision is improved, but loss of time and device complexity increase significantly
Solution Approach 1:
The patent combines separate object detection and navigation path determination processes into a single unified neural network model. This integrated approach processes environmental data through one cohesive system that simultaneously identifies objects, their locations, and navigable paths, eliminating the need for separate sequential processing steps and reducing overall computation time while maintaining accuracy.
Solution Approach 2:
The neural network model is designed with multi-functionality to perform multiple tasks simultaneously - object detection, location identification, and navigation path determination - within a single framework. This universal approach allows the system to handle diverse environmental understanding requirements without requiring separate specialized processes for each function.
2Measurement precision
If separate processes are used to determine object locations and navigation paths, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple computational functions into a single integrated neural network architecture. By combining object detection, location determination, and path planning capabilities within one model, the system reduces the number of separate computational graphs, data flow management overheads, and processing coordination requirements that would otherwise be necessary with separate processes.
Solution Approach 2:
The neural network is designed as a universal model that handles multiple environmental analysis tasks through shared computational layers and unified processing logic. This multi-functional design eliminates the need for multiple specialized processing pipelines and reduces overall system complexity while maintaining the precision required for accurate environmental understanding.
3Measurement precision
If separate processes are used for environmental analysis, then measurement precision is improved, but productivity decreases due to sequential processing requirements
Solution Approach 1:
The patent integrates multiple detection and segmentation tasks into a single parallel processing framework within the neural network. This allows simultaneous execution of object detection, location identification, and path determination operations on different spatial regions and data features, significantly improving processing throughput while maintaining the precision required for accurate environmental analysis.
Solution Approach 2:
The neural network processes environmental data by transforming it into a unified Bird's-Eye View representation that enables parallel analysis across different spatial dimensions and task requirements. This dimensional transformation allows multiple detection and segmentation operations to proceed simultaneously in parallel, improving productivity without sacrificing measurement precision.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to determination about objects in an environment. In at least one embodiment, a neural network can be used to determine one or more positions of one or more objects within a three-dimensional (3D) environment and to generate a segmented map of the 3D environment based, at least in part, on one or more two dimensional (2D) images of the one or more objects.