Agent Trajectory Prediction Using Target Locations and Trajectory Scoring
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Solution Overview
Problem
Predicting the future trajectories of agents in complex environments, such as autonomous vehicles, is challenging due to the multimodal nature of agent behaviors, which existing technologies struggle to accurately model and interpret.
Innovation Solution
The system employs a method that decomposes the trajectory prediction problem into three stages: target prediction, motion estimation, and trajectory scoring, using neural networks to generate and select likely future trajectories based on scene context data, incorporating road topography and traffic elements, and processing this information to provide accurate and diverse predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing trajectory prediction technologies are used, then the system can process scene context data, but the accuracy of predicting multimodal agent behaviors is insufficient
Solution Approach 1:
The trajectory prediction problem is segmented into three distinct stages: target prediction (identifying potential final locations), motion estimation (generating trajectories to each target), and trajectory scoring (ranking likelihoods). This segmentation allows each stage to specialize in handling specific aspects of multimodal behavior prediction, improving overall accuracy while maintaining adaptability to diverse agent behaviors
Solution Approach 2:
Target locations serve as an intermediary representation between scene context and final trajectories. By introducing quantized target states as intermediate variables, the system can condition motion estimation on specific intended destinations, enabling better interpretation and control over multiple possible future behaviors
2Adaptability or versatility
If the system generates multiple predicted future trajectories for each target location, then the diversity of predictions increases, but the computational complexity increases
Solution Approach 1:
The system performs preliminary target prediction to identify a discrete set of candidate target locations before generating trajectories. By pre-determining potential final destinations based on scene context, the system reduces the search space for trajectory generation, maintaining prediction diversity while controlling computational complexity through staged processing
Solution Approach 2:
Different neural network components are specialized for different stages: target prediction networks identify likely destinations, motion estimation networks generate trajectories to each target, and trajectory scoring networks rank them. This local specialization allows each component to be optimized for its specific task, balancing diversity and complexity
3Loss of information
If the system uses quantized target states to condition trajectory estimation, then interpretability improves, but the loss of continuous trajectory information increases
Solution Approach 1:
Quantized target locations serve as an intermediary that provides interpretability without completely losing continuous information. The discrete target states act as interpretable waypoints that condition the continuous trajectory generation process, allowing the system to maintain both interpretability through discrete targets and precision through continuous motion estimation between targets
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.