Trajectory Bank Prediction for Autonomous Agent Motion Forecasting
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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, as existing systems lack effective methods for accurately forecasting future positions and trajectories.
Innovation Solution
The development of techniques involving the generation of probability maps and trajectory lattices using neural networks, which combine location data, past trajectory data, and motion data to predict the future movements of agents, allowing for safer and more efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use traditional sensor data to predict object movements, then they can identify surrounding objects, but they fail to accurately forecast future positions and trajectories
Solution Approach 1:
The system pre-computes multiple possible trajectories for surrounding objects and stores them in a trajectory bank before they are needed for decision-making. This preliminary action allows the system to have ready-made trajectory predictions available when needed, improving both accuracy and reliability of trajectory forecasting.
Solution Approach 2:
The trajectory prediction system dynamically updates and selects trajectories from the pre-computed bank based on current sensor data and changing environmental conditions. This dynamic approach allows the system to adapt to new information while leveraging pre-computed predictions, resolving the contradiction between accuracy and reliability.
2Measurement precision
If the system pre-computes a bank of trajectories for all possible scenarios, then trajectory prediction accuracy improves, but computational complexity and memory requirements increase
Solution Approach 1:
Instead of computing all possible trajectories uniformly, the system focuses computational resources on generating trajectories for locally relevant scenarios and object types. The trajectory bank is organized with local quality, storing detailed trajectories for nearby objects while using coarser approximations for distant objects, reducing overall complexity while maintaining prediction accuracy where it matters most.
Solution Approach 2:
The trajectory prediction problem is segmented into multiple independent computations for different object types, distance ranges, and scenario categories. Each segment is pre-computed separately and stored in the trajectory bank, allowing the system to manage computational complexity through division while maintaining comprehensive coverage of possible scenarios.
3Adaptability or versatility
If the system uses a large bank of pre-computed trajectories, then prediction coverage improves, but memory usage and data processing time increase
Solution Approach 1:
The system pre-computes and stores trajectories in an organized bank structure before they are needed, so that during actual operation, the system only needs to query and retrieve relevant trajectories rather than computing them in real-time. This preliminary action significantly reduces processing time during critical decision-making moments while maintaining comprehensive trajectory coverage.
Solution Approach 2:
From the large bank of pre-computed trajectories, the system extracts only the relevant trajectories needed for the current situation based on object type, location, and environmental context. This extraction process filters out unnecessary data, reducing memory usage and processing time while maintaining comprehensive coverage of relevant scenarios.
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.