Trajectory Prediction Sampling for Realistic Autonomous Driving Futures
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting the future locations of objects in complex environments, particularly in congested areas with multiple moving vehicles and static obstacles, where existing methods often result in unrealistic trajectories and increased computational resources.
Innovation Solution
The implementation of a graph neural network (GNN) that processes sensor data and map information to predict object positions, velocities, and trajectories by encoding features into nodes and edges, allowing for the determination of non-overlapping future positions and efficient sampling of distribution data that adheres to environmental rules and criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prediction methods are used to determine future positions of objects, then the system is simpler to implement, but the trajectories become unrealistic and computational resources increase
Solution Approach 1:
The patent segments the prediction problem into multiple discrete future time steps, where the system determines a sequence of future positions rather than a single endpoint. This segmentation allows the model to evaluate intermediate positions and ensure realistic trajectories while maintaining computational efficiency through structured processing.
Solution Approach 2:
The system dynamically adjusts the prediction process by evaluating multiple possible future positions and selecting trajectories that adhere to environmental rules. The prediction mechanism adapts to different environmental constraints and object types, making the trajectory generation both realistic and computationally efficient.
2Reliability
If complex prediction models are used to ensure safe navigation, then collision avoidance improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary evaluation of multiple future positions before final trajectory determination. By pre-evaluating potential collision risks at intermediate time steps and filtering unrealistic trajectories early in the prediction process, the system ensures safe navigation while reducing the computational burden of final decision-making.
Solution Approach 2:
The patent introduces an intermediary evaluation layer that checks predicted positions against environmental rules and constraints. This intermediary step acts as a mediator between the prediction model and final trajectory selection, ensuring collision avoidance without requiring the entire system to be computationally intensive.
3Measurement precision
If multiple future positions are evaluated to ensure realistic trajectories, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The system evaluates future positions at periodic time steps rather than continuously. By determining positions at discrete intervals and evaluating realism at each step, the system achieves high prediction accuracy while avoiding the computational overhead of continuous evaluation, thus reducing processing time.
Data Source
AI summary
Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.