Parameter-Based Environment Encoding for Vehicle Scenario 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 by representing agents, their locations, motion, distance, and time information, allowing for clustering and scenario determination, enabling more accurate and reliable scenario classification and vehicle maneuvering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional unstructured approaches are used to interpret environmental information, then the system is simpler to implement, but scenario classification accuracy and decision-making reliability deteriorate
Solution Approach 1:
The patent segments environmental information into distinct parameter categories (location, motion, distance, time) and structures them hierarchically. Each parameter type is processed and encoded separately before being integrated into comprehensive scenario representations, improving classification accuracy while maintaining manageable complexity through modular organization.
Solution Approach 2:
The patent transforms unstructured environmental data into structured parameter-based encodings by defining specific parameter types with standardized formats. This parameterization approach converts raw sensor data into organized representations that enhance scenario classification reliability without requiring overly complex processing systems.
2Measurement precision
If high dimensionality of feature data is processed without structured encoding, then more comprehensive environmental information is captured, but measurement precision and scenario determination accuracy deteriorate
Solution Approach 1:
The patent extracts essential environmental features from high-dimensional sensor data by identifying and isolating key parameter types (location, motion, distance, time). This extraction process separates critical information from redundant data, improving measurement precision while reducing the effective data volume that requires processing.
Solution Approach 2:
The patent organizes high-dimensional feature data into structured parameter spaces with defined relationships and hierarchies. By imposing this structural dimension on raw data, the system achieves more precise environmental representation without being overwhelmed by the quantity of underlying sensor measurements.
3Reliability
If unstructured environmental data is used for scenario classification, then data processing is faster, but decision-making reliability in complex environments deteriorates
Solution Approach 1:
The patent applies preliminary structuring and encoding to environmental data before scenario classification occurs. By pre-organizing sensor inputs into standardized parameter formats with defined relationships, the system establishes a foundation for reliable decision-making that does not require excessive processing time during critical decision moments.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can access a plurality of parameter-based encodings providing a structured representation of an environment captured by one or more sensors associated with a plurality of vehicles traveling through the environment. A given parameter-based encoding of the environment identifies one or more agents that were detected by a vehicle within the environment and respective location information for the one or more agents within the environment. The plurality of parameter-based encodings can be clustered into one or more clusters of parameter-based encodings. At least one scenario associated with the environment can be determined based at least in part on the one or more clusters of parameter-based encodings.