Graph-Based Trajectory Prediction for Uncertain Traffic Scenes
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Solution Overview
Problem
Predicting the future trajectories of surrounding agents in complex traffic scenes is challenging due to inherent uncertainty, which hinders the ability of autonomous vehicles to safely and efficiently navigate.
Innovation Solution
The system uses a graph-based representation to encode high-definition maps and agent trajectories, allowing for the prediction of future trajectories by conditioning on selectively aggregated context based on lane-graph traversals and sampling latent variables to account for longitudinal variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trajectory prediction methods are used, then the prediction process is simple, but the prediction accuracy and reliability are low due to inherent uncertainty in complex traffic scenes
Solution Approach 1:
The prediction system is segmented into multiple specialized modules: graph encoder for map representation, policy header for route selection, trajectory decoder for path generation, and reward model for evaluation. Each module handles a specific aspect of the prediction task, improving overall reliability through modular specialization while managing complexity through clear separation of concerns.
Solution Approach 2:
A graph-based representation serves as an intermediary structure between the input traffic scene and the predicted trajectories. The graph encoder transforms complex scene data into structured graph representations, which then guide the policy header and trajectory decoder. This intermediary representation simplifies the prediction process while maintaining high reliability through structured information flow.
2Measurement precision
If comprehensive context aggregation is performed for all possible trajectories, then the prediction accuracy improves, but the computational cost and time consumption increase significantly
Solution Approach 1:
The policy header performs preliminary action by selecting the most probable route through the graph structure before the trajectory decoder generates detailed paths. This preliminary route selection filters out unlikely trajectories early in the process, allowing comprehensive context aggregation to be focused only on relevant paths, thereby maintaining precision while reducing computational time.
Solution Approach 2:
Instead of performing exhaustive context aggregation for all possible trajectories, the system performs partial aggregation focused on the most probable routes identified by the policy header. The reward model then provides targeted feedback on these selected trajectories, achieving sufficient prediction precision without the computational burden of evaluating every possible path.
3Adaptability or versatility
If multiple latent variables are sampled to account for longitudinal variability, then the diversity and robustness of predictions improve, but the sample efficiency decreases
Solution Approach 1:
The system dynamically adjusts the sampling process by using the reward model to evaluate and filter sampled trajectories. Instead of uniformly sampling a large number of trajectories, the reward model provides adaptive feedback that guides the sampling process toward more promising regions of the trajectory space, maintaining adaptability while improving sample efficiency through dynamic evaluation and selection.
Data Source
AI summary
Provided are methods for predicting agent trajectories, which can include generating a graph corresponding to a map of a scene by encoding map features and agent features as node encodings of the graph and determining a policy for application to outgoing edges of the nodes of the graph. Some methods described also include sampling paths for a target vehicle in the scene according to the policy and predicting a set of trajectories based on the sampled paths traversed by the policy and a sampled latent variable. Systems and computer program products are also provided.