Agent Importance Prediction for Autonomous Driving Attention Prioritization
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to effectively predict the importance of agents in their environment, such as other vehicles or pedestrians, which hinders safe navigation and decision-making.
Innovation Solution
A system is developed to process input features of agents in the vehicle's surroundings to determine output features and predict their importance, enabling informed movement decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex training data and labeling processes are used to predict agent importance, then prediction accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system uses the autonomous vehicle's own trajectory predictions and attention mechanisms to generate importance scores for agents, eliminating the need for external ground-truth labels and complex training data collection processes
Solution Approach 2:
The patent introduces an intermediary importance prediction model that translates complex sensor data and trajectory information into simplified importance scores, which then guide the navigation system's attention and processing priorities
2Productivity
If computational resources are allocated to all agents equally, then comprehensive monitoring is maintained, but processing efficiency decreases
Solution Approach 1:
The system applies different processing quality levels to different agents based on their importance scores: high-impact agents receive detailed analysis and continuous monitoring, while low-impact agents receive coarse monitoring, optimizing resource allocation across the environment
Solution Approach 2:
The patent implements partial action by focusing computational resources on a subset of high-importance agents rather than processing all agents equally, achieving sufficient monitoring coverage with reduced computational expenditure
Data Source
AI summary
A method, a system, and a non-transitory storage media for determining agent importance prediction for autonomous driving. Input features associated with agents present in an environment surrounding a vehicle are processed. Output features associated with the agents are determined based on the input features. An importance of each agent is predicted using the output features. One or more movements of the vehicle are determined based on the predicted agent importance.