Vehicle Scenario Encoding for Reliable Environmental Classification
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Solution Overview
Problem
Conventional approaches to interpreting and applying environmental information for vehicles are limited by their unstructured nature, leading to inaccurate scenario classification and unreliable decision-making, especially in complex environments with high dimensionality of feature data.
Innovation Solution
The use of parameter-based encodings that structure environmental information using a scenario schema, including agent type, location, motion, distance, and time information, to provide a consistent and accurate representation of the environment, enabling more reliable scenario classification and vehicular responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unstructured environmental information is used for scenario classification, then the system can process data quickly, but the classification accuracy and reliability deteriorate in complex environments
Solution Approach 1:
The patent segments environmental information into distinct parameter-based encodings including agent type, location, motion, distance, and time information. Each parameter is encoded separately according to a scenario schema, allowing the system to process complex environments through modular, structured data representation rather than unstructured blobs, thereby improving classification accuracy without overwhelming system complexity
Solution Approach 2:
The patent transforms environmental information from unstructured form into structured parameter-based encodings by changing the data representation parameters. Each aspect of the environment (agent type, location, motion, distance, time) is encoded with specific parameters defined in a scenario schema, enabling reliable scenario classification while maintaining manageable data structure complexity through standardized parameter definitions
2Measurement precision
If detailed environmental features are captured to improve scenario identification, then the representation becomes more accurate, but the dimensionality and processing complexity increase
Solution Approach 1:
The patent segments detailed environmental features into discrete parameter categories (agent type, location, motion, distance, time) rather than processing high-dimensional unstructured data. Each parameter is encoded independently according to scenario schema definitions, achieving precise environmental representation while avoiding the complexity of handling all features simultaneously in a high-dimensional space
Solution Approach 2:
The patent extracts only the relevant parameters needed for scenario classification from the full environmental data stream. By selecting and encoding specific parameters (agent type, location, motion, distance, time) according to the scenario schema, the system achieves precise environmental feature representation without processing unnecessary high-dimensional data, thereby reducing overall processing complexity
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine sensor data captured by at least one sensor of a vehicle while navigating an environment over a period of time. Information describing one or more agents associated with the environment during the period of time can be determined based at least in part on the captured sensor data. A parameter-based encoding describing the one or more agents associated with the environment during the period of time can be generated based at least in part on the determined information and a scenario schema, wherein the parameter-based encoding provides a structured representation of the information describing the one or more agents associated with the environment. A scenario represented by the parameter-based encoding can be determined based at least in part on a cluster of parameter-based encodings to which the parameter-based encoding is assigned.