Traffic Entity Prediction Visualization for Autonomous Driving
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Solution Overview
Problem
Current autonomous vehicle systems are inadequate in predicting the behavior of traffic entities, such as pedestrians and bicyclists, as they rely solely on motion vectors and do not consider other observations, leading to inferior prediction results.
Innovation Solution
A system that collects and processes image and video data from a vehicle's perspective, generates stimulus data, and uses user input to train a supervised learning algorithm to predict user behavior, incorporating parameters like central tendency and variance, and adjusts model weights based on error signals to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicles rely solely on motion vectors for predicting traffic entity behavior, then the prediction method is simple and computationally efficient, but the prediction accuracy is inferior
Solution Approach 1:
The patent combines multiple data sources including motion vectors, detected features (such as gaze direction, body posture, environmental context), and statistical models into a unified prediction framework. This merging of diverse information sources enables more accurate prediction of traffic entity behavior while maintaining computational feasibility through integrated processing.
Solution Approach 2:
The system introduces an intermediary statistical model that processes and synthesizes information from multiple sources before generating predictions. This intermediary layer transforms raw detected features and motion data into probabilistic predictions, improving accuracy while managing complexity through structured intermediate representations.
2Measurement precision
If the system collects and processes multiple types of observations beyond motion vectors, then prediction accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex prediction task into distinct processing stages: collecting observations from multiple sources, detecting relevant features, forming hypotheses about future behavior, and generating predictions. This segmentation allows the system to handle complex multi-source data by processing each component separately and integrating results systematically.
Solution Approach 2:
The system changes parameters by incorporating multiple observation types with different weights and relevance levels. By adjusting which features are detected and how they are processed, the system can adapt to different computational resources while maintaining improved prediction accuracy through multi-faceted data collection.
3Reliability
If the system uses supervised learning algorithms with multiple parameters, then prediction reliability improves, but training data requirements and model complexity increase
Solution Approach 1:
The patent implements feedback mechanisms where prediction outcomes are compared with actual traffic entity behavior, and the statistical models are refined based on these comparisons. This feedback loop improves prediction reliability over time by continuously adjusting model parameters based on accumulated experience, reducing the need for excessively large initial training datasets.
Data Source
AI summary
A computing device accesses video data displaying one or more traffic entities and generates a plurality of sequences from the video data. For each sequence, the computing device identifies a plurality of stimuli in the sequence and applies a machine learning model to generate an output describing the traffic entity. The computing device generates a data structure for storing, for each sequence, information describing the sequence and linking frame indexes of stimuli from the sequence to outputs of the machine learning model. The computing device stores the data structure in association with the video data. Responsive to receiving a selection of a sequence, the computing device loads video data for the sequence. Responsive to receiving a selection of a traffic entity within the video data, the computing device generates a graphical display element including the machine learning model output for the selected traffic entity.