Vehicle Trajectory Prediction With Agent-Based Resource Prioritization
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Solution Overview
Problem
Current driving-control systems face inefficiencies in resource allocation for predicting agent trajectories, as they allocate equal computational resources to all agents, leading to suboptimal performance and potential errors due to the use of heuristic approaches.
Innovation Solution
A prioritization model is introduced to determine the likelihood of agents intersecting the vehicle's potential travel space, allowing for optimized resource allocation by prioritizing agents with higher probabilities of interaction, thereby allocating more resources to predict their trajectories accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If equal computational resources are allocated to all agents, then comprehensive monitoring of all agents is achieved, but resource efficiency deteriorates and prediction accuracy for critical agents is insufficient
Solution Approach 1:
The patent applies local quality by differentiating resource allocation based on agent characteristics. High-priority agents (e.g., pedestrians, cyclists) receive enhanced computational resources for more accurate trajectory prediction, while low-priority agents receive standard resources. This selective approach improves prediction accuracy for critical agents without uniformly increasing overall computational burden.
Solution Approach 2:
The patent implements partial action by focusing computational efforts only on agents that pose potential risks or have high interaction probability. Rather than applying equal computational intensity to all agents, the system selectively intensifies processing for specific agents based on priority classification, thereby improving efficiency and accuracy where it matters most.
2Reliability
If heuristic approaches are used for agent prioritization, then implementation simplicity is maintained, but prediction accuracy and reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary prioritization module that acts as a mediator between agent detection and trajectory prediction. This module classifies agents into priority levels based on multiple factors (agent type, distance, velocity, interaction probability) before passing them to the prediction system. While this adds a processing layer, it significantly improves prediction accuracy by ensuring critical agents receive appropriate computational attention.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting prediction resources based on agent priority parameters. The system modifies computational allocation, prediction frequency, and model complexity based on the classified priority level of each agent, thereby improving overall system reliability without requiring complete system redesign.
3Measurement precision
If more computational resources are allocated to trajectory prediction, then prediction accuracy improves, but processing speed and system responsiveness deteriorate
Solution Approach 1:
The patent applies partial action by allocating enhanced computational resources only to high-priority agents rather than all agents. This selective intensification maintains high processing speed for the overall system while achieving superior prediction accuracy for critical agents that require detailed trajectory analysis.
Solution Approach 2:
The patent implements local quality by applying different levels of prediction precision to different agent groups. High-priority agents receive computationally intensive predictions with higher accuracy, while low-priority agents receive standard predictions. This differentiated approach optimizes the balance between overall processing speed and local prediction accuracy where it matters most.
Data Source
AI summary
In one embodiment, a method includes accessing sensor data associated with an environment of the vehicle; identifying agents in the environment based on the sensor data; determining a probability distribution indicative of whether a preliminary trajectory of each of the agents intersects a potential travel space of the vehicle; generating a prioritization of the agents based on the probability distribution; allocating an amount of computing resources of the vehicle for analyzing each of one or more of the agents based on the prioritization and characteristics of the respective one or more agents; and predicting a trajectory of one or more of the agents according to the allocated computing resources. An accuracy of the prediction is proportional to the amount of allocated computing resources. The method also includes determining, based on the analysis of the agents, one or more operations for the vehicle to perform.