3D Human Keypoint Fusion for Pedestrian Crossing Prediction
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to accurately predict the behavior of pedestrians and other agents in dynamic and interactive scenarios, such as urban intersections, due to insufficient consideration of fine-grained human pose and motion information.
Innovation Solution
A behavior prediction system that incorporates 3D human keypoints into a multi-task learning framework, using neural networks to process context data and keypoint data for accurate crossing action and trajectory predictions, enhanced by auxiliary tasks like keypoint contrastive learning and jigsaw puzzles to improve keypoint representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional autonomous vehicle systems use basic sensor detection without fine-grained human pose information, then the system complexity is lower, but the pedestrian behavior prediction accuracy deteriorates
Solution Approach 1:
The patent transitions from 2D image data to 3D human keypoint representations, adding a spatial dimension to the input data. This dimensional enhancement allows the system to capture fine-grained pose and motion information that is critical for accurate behavior prediction, directly resolving the contradiction between maintaining low system complexity and achieving high prediction accuracy.
Solution Approach 2:
The system segments the pedestrian detection task into multiple components: detecting human keypoints, estimating 3D pose, extracting motion dynamics, and predicting behavior. This segmentation allows each component to be optimized independently while collectively improving overall prediction accuracy without proportionally increasing system complexity.
2Reliability
If the system incorporates 3D human keypoints and multi-task learning framework, then the behavior prediction accuracy is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent implements a multi-task learning framework where a single neural network performs multiple functions: detecting human keypoints, estimating 3D pose, extracting motion dynamics, and predicting behavior. This multi-functionality reduces the need for separate specialized models, thereby improving prediction reliability while managing computational energy consumption more efficiently than multiple independent systems would require.
Solution Approach 2:
The system performs preliminary extraction of 3D human keypoints and pose estimation before behavior prediction. By pre-processing the data into meaningful 3D representations, the system reduces the computational burden during the actual prediction phase, allowing for higher reliability in behavior prediction while controlling overall energy consumption.
3Measurement precision
If the system uses only context information without fine-grained human pose information, then the processing speed is faster, but the ability to predict crossing actions and trajectories in dynamic scenarios deteriorates
Solution Approach 1:
The patent extracts specific fine-grained features (3D human keypoints and pose information) from the overall sensor data, separating these critical behavioral indicators from general context information. This extraction allows the system to focus computational resources on the most predictive features for crossing actions and trajectories, improving prediction accuracy while maintaining reasonable processing speed by not processing all data at full detail.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent behavior prediction using keypoint data. One of the methods includes obtaining data characterizing a scene in an environment, the data comprising: (i) context data comprising data characterizing historical trajectories of a plurality of agents up to the current time point; and (ii) keypoint data for a target agent; processing the context data using a context data encoder neural network to generate a context embedding for the target agent; processing the keypoint data using a keypoint encoder neural network to generate a keypoint embedding for the target agent; generating a combined embedding for the target agent from the context embedding and the keypoint embedding; and processing the combined embedding using a decoder neural network to generate a behavior prediction output for the target agent that characterizes predicted behavior of the target agent after the current time point.