Object Position Prediction Clustering for Autonomous Vehicle Planning
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Solution Overview
Problem
Existing robotic systems struggle to accurately predict future object locations in an environment, which hinders safe operation of autonomous vehicles.
Innovation Solution
Implementing machine learned models to predict multiple candidate positions for objects based on historical data and vehicle trajectories, clustering these positions into groups that trigger similar vehicle behaviors, and using these predictions to enhance vehicle planning and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learned models predict multiple candidate positions for objects, then prediction accuracy improves, but computational resources increase
Solution Approach 1:
The prediction process is segmented into two stages: first predicting multiple candidate positions, then clustering them into groups. This segmentation allows the system to handle complex predictions in manageable steps, maintaining accuracy while optimizing computational resource usage at each stage.
Solution Approach 2:
The system predicts more candidate positions than strictly necessary (excessive action), then uses clustering to filter and organize them. This approach ensures comprehensive coverage of potential object locations while the clustering step efficiently reduces the computational burden by grouping similar positions.
2Reliability
If multiple candidate positions are predicted and clustered, then vehicle planning safety improves, but processing time increases
Solution Approach 1:
The clustering of candidate positions is performed in advance as a preliminary action before final vehicle planning decisions are made. By pre-organizing potential object positions into clusters, the system reduces the computational workload during time-critical vehicle control decisions, thereby maintaining safety while reducing processing time.
3Productivity
If clustering is applied to organize object positions, then decision-making efficiency improves, but model complexity increases
Solution Approach 1:
Clustering acts as an intermediary step between raw position predictions and final vehicle planning decisions. This intermediary organization of data into clusters simplifies the decision-making process by presenting processed, grouped information rather than raw individual positions, thereby improving efficiency while the modular nature of clustering keeps model complexity manageable.
Data Source
AI summary
Techniques for predicting positions of objects in an environment are described herein. For example, the techniques may include clustering predicted object positions and using a representative target for one or more clusters to determine a predicted position of an autonomous vehicle. The clusters may vary in size, number of positions, etc. Object positions can be selected from one or more clusters for consideration during vehicle planning, which may include simulation.