Agent Trajectory Prediction Using Target-Conditioned Destinations
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Solution Overview
Problem
Predicting the future trajectories of agents in a real-world environment is challenging due to the highly multimodal nature of their behaviors, which are influenced by unknown intents and complex interactions with other scene elements, making it difficult for autonomous vehicles to make effective navigation and collision avoidance decisions.
Innovation Solution
A system that decomposes the trajectory prediction problem into three stages: target prediction, target conditioned motion estimation, and trajectory scoring and selection, using neural networks to predict discrete target locations and their likelihoods, and then generate probabilistic future trajectories based on these targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trajectory prediction methods are used, then the system is simpler to implement, but the prediction accuracy deteriorates in highly multimodal environments
Solution Approach 1:
The trajectory prediction problem is segmented into three distinct stages: target prediction (identifying likely final locations), target conditioned motion estimation (generating trajectories to each target), and trajectory scoring/selection (ranking and selecting final predictions). This segmentation allows each stage to be optimized independently, improving overall accuracy while managing complexity through modular neural network components.
Solution Approach 2:
The patent introduces target locations as an intermediary concept between the input scene context and the final trajectory predictions. Instead of directly predicting trajectories from raw data, the system first predicts discrete target locations that serve as intermediate goals, then conditions motion estimation on these targets. This intermediary layer captures the multimodal nature of agent behavior more effectively.
2Reliability
If the system considers all possible trajectories, then the prediction comprehensiveness improves, but the computational time increases
Solution Approach 1:
The system generates a limited set of trajectories conditioned on predicted target locations rather than exhaustively considering all possible trajectories. By focusing computational resources on trajectories leading to high-probability targets, the system achieves sufficient comprehensiveness for safety-critical decisions while dramatically reducing computation time compared to full trajectory space exploration.
Solution Approach 2:
The target prediction stage performs preliminary action by identifying likely final locations before generating full trajectories. This preliminary identification of target locations allows the system to pre-filter the trajectory space, generating only those trajectories that lead to plausible destinations, thereby reducing the overall computational burden while maintaining prediction reliability.
3Measurement precision
If the system uses detailed scene context data, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The data processing pipeline is segmented into distinct neural network modules: a context encoder that processes scene context data into latent representations, a target predictor that generates likely final locations, and a motion estimator that produces trajectories. This segmentation allows each module to specialize in processing specific aspects of the data, improving accuracy while managing complexity through functional decomposition.
Solution Approach 2:
The system extracts key features from detailed scene context data through the context encoder, transforming complex raw data into condensed latent representations. This extraction process retains the essential information needed for accurate trajectory prediction while removing redundant details, thereby reducing the complexity of subsequent processing stages without sacrificing prediction accuracy.
Data Source
AI summary
A system obtains scene context data characterizing the environment. The scene context data includes data that characterizes a trajectory of an agent in a vicinity of a vehicle up to a current time point. The system identifies a plurality of initial target locations, and generates, for each of a plurality of target locations that each corresponds to one of the initial target locations, a respective predicted likelihood score that represents a likelihood that the target location will be an intended final location for a future trajectory of the agent. For each target location in a first subset of the target locations, the system generates a predicted future trajectory for the agent given that the target location is the intended final location for the future trajectory. The system further selects, as likely future trajectories of the agent, one or more of the predicted future trajectories.