Method for representing an environment of a mobile platform
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for representing the environment of mobile platforms, such as autonomous vehicles, struggle to effectively fuse and interpret sensor data from multiple sources, leading to inefficiencies in decision-making and safety in complex situations.
Innovation Solution
A modular method using a deep neural network to predict environment representations by fusing discrete time sequences of sensor data from various sensors, transforming this data into a unified moving spatial reference system, and generating a predictive output tensor that compensates for system latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to capture environment data, then the completeness and accuracy of environment perception is improved, but the complexity of data fusion and processing increases
Solution Approach 1:
The patent segments the environment representation into discrete spatial cells (e.g., 2D grid or 3D voxels), where each cell independently stores sensor data and semantic information. This segmentation allows parallel processing of multiple sensor inputs without requiring complex global fusion, as each cell can be updated independently based on its local sensor observations.
Solution Approach 2:
The patent creates a universal environment representation framework that can accommodate multiple sensor types (cameras, LIDAR, radar, etc.) and multiple semantic attributes (object classes, distances, velocities) within a unified cellular structure. This multi-functional representation eliminates the need for separate processing pipelines for different sensor types, simplifying the overall fusion complexity.
2Reliability
If discrete time sequences of sensor data are processed, then the reliability of environment representation is improved, but the computational time and processing delay increase
Solution Approach 1:
The patent pre-organizes sensor data into a structured cellular environment representation format as data arrives, rather than waiting to collect complete data sets before processing. This preliminary structuring allows the system to work with partial information in a reliable manner, reducing computational delay while maintaining reliability through the consistent cellular framework.
Solution Approach 2:
The patent replaces traditional sequential mechanical processing of sensor data with a parallel computational approach where multiple sensor inputs are simultaneously integrated into the cellular representation. This substitution of parallel computation for sequential processing significantly reduces computational delay while maintaining or improving reliability through comprehensive data fusion.
3Productivity
If a unified environment representation is created from multiple sensors, then the decision-making capability is improved, but the system complexity and interpretability challenges increase
Solution Approach 1:
The patent assigns different semantic qualities to different spatial cells based on local conditions. Each cell can represent different object classes, distances, and semantic attributes appropriate to its local environment, rather than forcing a uniform representation across the entire scene. This local quality approach maintains interpretability by making the meaning of data locally contextual while enabling comprehensive decision-making.
Solution Approach 2:
The cellular environment representation acts as an intermediary layer between raw sensor data and decision-making algorithms. This intermediate structured representation simplifies the interface between perception and control systems, making the system more interpretable by providing a clear, organized view of the environment that bridges the gap between complex sensor inputs and decision-making requirements.
4Reliability
If the environment representation is independent of object density, then the system robustness is improved, but the ability to handle varying scene complexities decreases
Solution Approach 1:
The patent implements a dynamic cellular environment representation where the semantic content and resolution of individual cells can adapt based on local object density and importance. Cells containing significant objects can be refined or expanded, while empty or less important regions maintain a coarser representation. This dynamic adaptation allows the system to maintain robustness through consistent cellular structure while handling varying scene complexities efficiently.
Data Source
AI summary
A method and system for representing an environment of a first mobile platform. The method includes: capturing features of the environment by discrete time sequences of sensor-data from at least two sensors and respective time markers; determining distances of the first mobile platform to the features of the environment; estimating semantic information of the features of the environment; transforming the semantic information of the features of the environment into a moving spatial reference system, wherein a position of the first mobile platform is at a constant site, using the respective determined distances and respective time markers; creating an input tensor using sequences of the transformed semantic information of the features of the environment, corresponding to the sequences of the sensor data of the at least two sensors; generating an output tensor that represents the environment using a deep neural network at a requested point in time and the input tensor.