Pedestrian Motion Prediction From Appearance and Pose Cues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and predicting the motion of objects, particularly pedestrians, due to incomplete or inaccurate information from perception systems, which can compromise safety by delaying reaction times and route planning.
Innovation Solution
The use of image data to determine the pose and trajectory of objects, such as pedestrians, through machine learning models like neural networks, which can predict motion based on appearance and geometric pose, enabling faster and more accurate updates than traditional sensor systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sensor systems are used for detecting and predicting object motion, then the system structure is simpler and more reliable, but the detection speed and prediction accuracy are insufficient, especially for rapidly changing pedestrian trajectories
Solution Approach 1:
The patent replaces traditional mechanical sensor systems (LIDAR, radar) with a vision-based system using cameras and deep learning neural networks. This substitution enables faster detection speeds (10-50ms) by processing image data through trained models that predict pedestrian trajectories based on appearance and pose features, rather than relying on slower traditional sensor processing pipelines.
Solution Approach 2:
The patent uses image data as a copy or representation of the physical scene, creating a digital visual model that can be processed rapidly. By working with image copies rather than direct sensor measurements, the system achieves faster processing while maintaining the ability to predict motion trajectories through learned patterns in the visual data.
2Measurement precision
If traditional perception systems are used, then the system is more reliable and easier to operate, but the information completeness and accuracy for predicting object motion is insufficient
Solution Approach 1:
The patent changes the parameters used for motion prediction from traditional sensor measurements to appearance-based features extracted from images. The neural network learns to predict trajectories based on visual parameters such as pedestrian pose, gait, and appearance characteristics, achieving higher prediction accuracy by utilizing richer feature representations available in image data.
Solution Approach 2:
The patent employs pre-trained neural network models that have been trained offline on large datasets. This preliminary training action allows the system to have motion prediction capabilities built-in before deployment, enabling fast and accurate predictions during real-time operation without requiring complex runtime processing or additional sensors.
3Loss of time
If faster detection methods are implemented, then the reaction time of autonomous vehicle is reduced, but the system complexity and computational requirements increase
Solution Approach 1:
The patent performs computationally intensive training of neural network models in advance, before the autonomous vehicle operates. This preliminary action transfers the heavy computational burden to an offline setting, allowing the vehicle to use pre-trained models that require minimal computational energy during real-time detection, achieving fast reaction times (10-50ms) with low runtime energy consumption.
Solution Approach 2:
The patent uses pre-computed feature representations and trained model parameters as copies of complex processing results. Instead of performing full neural network inference from scratch during operation, the system utilizes pre-extracted features and learned parameters that can be rapidly applied to new images, reducing real-time computational energy requirements while maintaining fast detection speeds.
Data Source
AI summary
Techniques for determining and/or predicting a trajectory of an object by using the appearance of the object, as captured in an image, are discussed herein. Image data, sensor data, and/or a predicted trajectory of the object (e.g., a pedestrian, animal, and the like) may be used to train a machine learning model that can subsequently be provided to, and used by, an autonomous vehicle for operation and navigation. In some implementations, predicted trajectories may be compared to actual trajectories and such comparisons are used as training data for machine learning.