Multi-Agent Trajectory Prediction Using Probability Maps and Lattices
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting the movement of surrounding objects, such as vehicles and pedestrians, to improve trajectory planning and safety, as existing systems lack accurate and efficient methods for multi-agent trajectory prediction.
Innovation Solution
The development of techniques that generate probability maps and trajectory lattices using neural networks, combining location data, past trajectory data, and motion data to predict the movement of agents, allowing for safer and more efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trajectory prediction methods are used, then the system is simpler to implement, but the prediction accuracy and safety are insufficient
Solution Approach 1:
The system pre-computes and stores a bank of trajectories for different object types (pedestrians, vehicles, cyclists) with various motion characteristics. This preliminary action allows the prediction system to quickly retrieve and combine relevant trajectories without performing complex real-time calculations, thereby improving prediction accuracy while managing system complexity
Solution Approach 2:
The prediction system segments the trajectory bank into different categories based on object type and motion patterns. By dividing the large trajectory database into manageable segments, the system can efficiently select and combine appropriate trajectories for prediction, balancing accuracy requirements with computational complexity
2Measurement precision
If a large bank of trajectories is stored for accurate predictions, then the prediction quality improves, but the memory requirements and data processing complexity increase
Solution Approach 1:
The system applies local quality by storing different densities and levels of detail for different trajectory segments. Frequently used or critical trajectory patterns are stored with higher precision and more detailed information, while less critical trajectories use compressed representations, optimizing the balance between prediction precision and data volume
Solution Approach 2:
The system stores a comprehensive bank of trajectories that exceeds the minimum required for basic functionality. This excessive action ensures that sufficient trajectory options are available for accurate predictions in diverse scenarios, while the selection and combination processes efficiently manage the large data volume
3Reliability
If real-time trajectory prediction is performed for multiple agents, then the navigation safety improves, but the computational time and processing load increase
Solution Approach 1:
The system performs preliminary computation by pre-generating and storing trajectories for multiple agents in advance. During real-time operation, the system retrieves these pre-computed trajectories and combines them based on current conditions, significantly reducing the computational time required for multi-agent predictions while maintaining navigation safety
Solution Approach 2:
The system merges trajectory predictions for multiple agents by combining their individual trajectory banks and using joint probability calculations. This merging approach allows efficient computation of multi-agent interactions by leveraging pre-computed individual trajectories rather than calculating all possible interactions from scratch
Data Source
AI summary
Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.