Attention-Based Trajectory Prediction for Crowded Autonomous Driving
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Solution Overview
Problem
Predicting future trajectories of agents in complex and crowded environments is technically challenging and computationally expensive for autonomous vehicles, as existing ML models require quadratic scaling in data storage and computation due to fully connected networks.
Innovation Solution
Implementing a scalable, fixed-cost ML attention mechanism that determines a fixed-sized set of relevant objects for each agent, using attention scores to predict future movements, reducing data storage and computation requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully connected neural networks are used to predict trajectories of agents in crowded environments, then prediction accuracy is improved, but computational cost and data storage requirements scale quadratically
Solution Approach 1:
The patent extracts only the relevant objects from the environment that actually influence the agent's trajectory, rather than considering all objects. This is achieved through an attention mechanism that identifies and focuses on a fixed-sized set of relevant objects, reducing the computational burden from quadratic to linear scaling while maintaining prediction accuracy.
Solution Approach 2:
The patent segments the environment into relevant and irrelevant objects relative to each agent. The attention mechanism divides the computational task by processing only the relevant subset of objects for each agent, rather than computing interactions between all pairs of objects in the environment.
2Reliability
If all objects in the environment are considered for trajectory prediction, then prediction completeness is improved, but processing time increases
Solution Approach 1:
The patent extracts only the necessary information about relevant objects needed for accurate trajectory prediction. The attention mechanism identifies which objects actually matter for each agent's motion and extracts only that information, discarding irrelevant data to reduce processing time while maintaining prediction completeness.
Solution Approach 2:
The patent uses partial action by considering only a fixed-sized set of relevant objects rather than all objects in the environment. The attention mechanism determines the minimal sufficient subset of objects that influence each agent's trajectory, processing only that partial set to achieve efficient prediction.
3Adaptability or versatility
If the number of objects to track increases in crowded environments, then environmental awareness is improved, but memory requirements increase quadratically
Solution Approach 1:
The patent extracts only the essential state information about relevant objects that is necessary for trajectory prediction. Instead of storing all possible interactions between all objects, the attention mechanism extracts and stores only the relevant object states and their relationships to the agent, reducing memory requirements from quadratic to linear scaling.
Solution Approach 2:
The patent applies local quality by making the memory representation agent-specific. Each agent has its own attention weights and relevant object set, allowing the system to adapt memory usage to local environmental conditions around each agent rather than using a uniform global memory structure.
Data Source
AI summary
Techniques are discussed herein for using a machine learning attention mechanism to predict movements, states, and/or trajectories of agents in various environments. In various examples, a prediction component of an autonomous vehicle may analyze sensor data to determine, for individual agents in the environment, unique sets of additional objects that are relevant to predicting the subsequent movements of the individual agents. For a particular agent, the prediction component may determine the relative positions and/or states between the agent and the associated set of relevant objects for the agent, and may use an attention mechanism to determine an object interaction vector including weighted attention scores for each additional object relative to the agent. Object interaction vectors may be generated for any number of agents and/or any number of timesteps to determine predicted agent movements and to forecast subsequent driving scenes within the environment.