Diffusion Network Trajectory Generation for Controllable Agent Simulation
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Solution Overview
Problem
Existing machine learning models for simulating pedestrian movements lack realism and controllability, failing to respond effectively to user inputs or adapt to new obstacles, and struggle to implement defined criteria during simulation.
Innovation Solution
The use of diffusion networks configured with training data and guidance objectives to determine trajectories of subjects, incorporating past trajectories, local grid context, and collision avoidance criteria, allowing for more realistic and controllable trajectory generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are used to simulate pedestrian movements, then the simulation can be performed, but the movement realism and ability to respond to user inputs are insufficient
Solution Approach 1:
The system dynamically adjusts trajectory generation by incorporating real-time guidance inputs (objectives, constraints, criteria) into the diffusion model inference process. The model can adapt its predictions based on user-provided guidance while maintaining realistic movement patterns learned during training, resolving the contradiction between realism and adaptability.
Solution Approach 2:
The system changes the parameters of the diffusion model during inference by introducing guidance vectors that modify the trajectory generation based on user inputs. This allows the model to maintain its learned realistic movement characteristics while adapting to new objectives, constraints, or criteria provided by users.
2Reliability
If diffusion models with guidance are used for trajectory generation, then controllability and realism are improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary training of the diffusion model on large datasets of realistic trajectories before deployment. This pre-training captures realistic movement patterns in advance, allowing the model to generate realistic trajectories with simpler inference-time computations when guidance is applied, reducing the computational burden during actual use.
3Adaptability or versatility
If training data with diverse annotations is used to configure the neural network, then the trajectory generation becomes more adaptable, but the data processing complexity increases
Solution Approach 1:
The system segments training data into different categories (e.g., pedestrian trajectories, vehicle trajectories, cyclist trajectories, remote subject trajectories, map data) and processes each segment separately during training. This modular approach allows the model to learn diverse movement patterns while managing data processing complexity through organized, category-specific processing pipelines.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to neural networks for realistic and controllable agent simulation using guided trajectories. The neural networks can be configured using training data including trajectories and other state data associated with subjects or agents and remote or neighboring subjects or agents, as well as context data representative of an environment in which the subjects are present. The trajectories can be determining using the neural networks and using various forms of guidance for controllability, such as for waypoint navigation, obstacle avoidance, and group movement.