Probabilistic Heat Maps for Entity Behavior Prediction
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Solution Overview
Problem
Current prediction techniques for entity behavior in environments, such as physics-based modeling and rules-of-the-road simulations, are inefficient in terms of storage requirements and do not effectively summarize sensor data for predictive purposes.
Innovation Solution
The use of probabilistic heat maps to summarize sensor data, where each cell in the heat map represents a pattern of behavior associated with specific entity types and environmental characteristics, allowing for efficient storage and retrieval of predictive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physics-based modeling or rules-of-the-road simulations are used to predict future states of entities, then prediction accuracy is improved, but storage requirements increase and data processing efficiency decreases
Solution Approach 1:
The patent creates simplified probabilistic heat map representations that copy only the essential behavioral patterns from extensive sensor data, rather than storing all raw data. Each heat map cell stores probability values representing likely entity behaviors at specific locations and times, enabling accurate predictions with minimal storage requirements.
Solution Approach 2:
The patent transforms raw sensor data into probabilistic parameters organized in heat map structures. By changing the data representation from detailed physical measurements to aggregated probability distributions across spatial and temporal dimensions, the system achieves both prediction accuracy and storage efficiency.
2Reliability
If physics-based modeling or rules-of-the-road simulations are used to predict future states of entities, then prediction accuracy is improved, but computational efficiency decreases
Solution Approach 1:
The patent performs computational work in advance by collecting sensor data and pre-computing probabilistic heat maps that capture entity behavior patterns. Once the heat maps are generated, predictions can be made quickly by simply querying the pre-computed probability values, eliminating the need for repeated complex simulations during actual prediction operations.
Solution Approach 2:
The patent creates simplified probabilistic heat map representations that copy only the essential behavioral patterns from extensive sensor data, rather than storing all raw data. Each heat map cell stores probability values representing likely entity behaviors at specific locations and times, enabling accurate predictions with minimal storage requirements.
3Loss of information
If extensive sensor data is stored for prediction purposes, then prediction completeness is improved, but storage requirements increase
Solution Approach 1:
The patent creates simplified probabilistic heat map representations that copy only the essential behavioral patterns from extensive sensor data, rather than storing all raw data. Each heat map cell stores probability values representing likely entity behaviors at specific locations and times, enabling accurate predictions with minimal storage requirements.
Solution Approach 2:
The patent merges multiple sensor observations into aggregated probabilistic distributions within heat map cells. By combining information from numerous data points into consolidated probability values organized by location, entity type, and time, the system preserves prediction completeness while dramatically reducing storage requirements.
Data Source
AI summary
The generation and/or use of probabilistic heat maps for use in predicting the behavior of entities in an environment is described. In an example, a computing device(s) can receive sensor data from sensors of a vehicle in an environment. The computing device(s) can determine, based at least in partly on the sensor data, a location of an entity in the environment, and a first characteristic associated with the entity or the environment (type, velocity, orientation, etc.). The computing device(s) can access, from a database, a heat map generated from previously collected sensor data associated with the environment. The computing device(s) can perform a look-up using the heat map based at least partly on the location of the entity and the first characteristic, and can determine a predicted behavior of the entity at a predetermined future time based on a pattern of behavior associated with a cell in the heat map.