External Agent Behavior Prediction With Modular Hypothesis Pruning
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Solution Overview
Problem
Existing systems for predicting external agent behavior in autonomous vehicles lack transparency, modularity, and efficiency, leading to increased computational resources and latency, and are prone to errors.
Innovation Solution
A method and system that utilizes a modular intent estimation model with tree-like structures, evaluating decisions based on real-world parameters and physical dimensions, enabling independent optimization and reduced interdependencies, and pruning unlikely hypotheses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems for predicting external agent behavior are used, then behavior prediction is achieved, but the systems lack transparency and require increased computational resources
Solution Approach 1:
The patent segments the behavior prediction system into modular components: hypothesis generation module, hypothesis evaluation module, and decision module. Each module handles specific aspects of prediction independently, reducing overall system complexity while maintaining prediction accuracy through structured decomposition of the prediction task.
Solution Approach 2:
The system dynamically adjusts computational effort by generating and evaluating multiple hypotheses with varying levels of detail based on situational context. The hypothesis evaluation process adaptively prunes unlikely scenarios, optimizing computational resource allocation while preserving prediction reliability across different operational conditions.
2Reliability
If existing systems for predicting external agent behavior are used, then behavior prediction is achieved, but latency increases
Solution Approach 1:
The system performs preliminary hypothesis generation based on pre-established decision models and historical data patterns. By pre-computing likely behavior scenarios and organizing them in a structured hypothesis tree, the system reduces real-time computational latency while maintaining prediction accuracy through rapid evaluation of pre-prepared hypotheses.
3Loss of information
If modular intent estimation model with tree-like structures is used, then model transparency is enhanced, but computational overhead increases
Solution Approach 1:
The patent extracts and evaluates only the most critical hypotheses from the complete hypothesis tree, separating detailed transparent reasoning from final decision output. This allows the system to maintain full model transparency for analysis while reducing computational overhead by focusing intensive processing on high-probability scenarios rather than evaluating every possible hypothesis in detail.
4Productivity
If hypotheses are pruned based on evaluation, then computational resources are reduced, but prediction completeness may be compromised
Solution Approach 1:
The system changes the evaluation parameters dynamically, adjusting hypothesis pruning thresholds based on contextual factors such as environmental uncertainty, agent type, and situational criticality. This allows computational efficiency to be optimized without compromising prediction completeness in high-stakes scenarios, as the pruning criteria adapt to maintain adequate coverage of relevant hypotheses.
Data Source
AI summary
A method for predicting external agent behavior can include: receiving decisioning data, identifying a set of environmental agents (e.g., external agents), and evaluating a set of hypotheses for each environmental agent. A system for predicting external agent behavior can include a computing system and a sensor suite (e.g., onboard an autonomous vehicle), which can function to implement any or all of the processes of the method.