Autonomous Vehicle Gesture Prediction for Human Intent-Aware Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle systems fail to consider human gestures and intentions, leading to poor decision-making during navigation, as they do not adequately comprehend or predict human interactions, which are crucial for timely and appropriate actions.
Innovation Solution
A method and system using deep learning techniques to analyze sensor data from autonomous vehicles, estimate human poses and gestures, and predict future actions based on context and pre-existing data to control vehicle navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing autonomous vehicle systems navigate without considering human gestures, then the system complexity is reduced, but the decision-making accuracy and safety deteriorate
Solution Approach 1:
The system segments human gesture recognition into distinct processing stages: gesture detection from sensor data, pose estimation using deep learning models, gesture classification into predefined categories, and intention prediction. This segmentation allows the complex task of understanding human gestures to be broken down into manageable modules that can be integrated into the autonomous vehicle's navigation system without overwhelming system complexity
Solution Approach 2:
The system performs preliminary gesture classification and intention prediction before making navigation decisions. By pre-processing sensor data to identify potential gestures and predict human intentions in advance, the system prepares decision-making information proactively, improving response time and accuracy while maintaining systematic organization through pre-defined gesture categories and prediction models
2Measurement precision
If the system predicts human gestures using deep learning techniques, then the gesture recognition accuracy is improved, but the computational time and processing complexity increase
Solution Approach 1:
The system applies partial action by focusing deep learning processing only on detected human subjects and their relevant body parts for gesture recognition, rather than processing all sensor data uniformly. This selective application of computational resources maintains high gesture recognition accuracy while reducing overall processing time by concentrating computational effort where it is most needed
Solution Approach 2:
The system changes processing parameters dynamically based on context information, adjusting the level of deep learning analysis applied to different scenarios. For example, the system can adjust pose estimation complexity, gesture classification thresholds, and prediction model depth based on factors such as distance to human subjects, environmental conditions, and navigation urgency, thereby optimizing the balance between accuracy and processing speed
3Reliability
If the system collects and processes extensive sensor data for gesture prediction, then the gesture prediction accuracy is improved, but the data processing load and energy consumption increase
Solution Approach 1:
The system extracts only the essential features and data elements from extensive sensor inputs that are relevant for gesture prediction, such as human pose keypoints, gesture trajectory points, and contextual environmental factors. By filtering and extracting only the necessary information rather than processing all raw sensor data, the system maintains high prediction accuracy while significantly reducing computational load and energy consumption
Solution Approach 2:
The system performs preliminary data filtering and feature extraction from sensor inputs before applying energy-intensive deep learning models for gesture prediction. By pre-processing sensor data to identify and isolate relevant gesture-related information in advance, the system reduces the volume of data requiring intensive processing, thereby lowering energy consumption while preserving the accuracy needed for reliable gesture prediction
4Measurement precision
If the system infers gesture categories from predefined categories, then the gesture classification accuracy is improved, but the system's adaptability to new gestures deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where predicted gestures and classification results are continuously monitored and used to refine the predefined gesture categories and prediction models. This feedback loop allows the system to learn from actual human gestures encountered during navigation, gradually adapting the predefined categories to accommodate new gesture types while maintaining the accuracy benefits of structured classification through iterative model updates and category refinements
Data Source
AI summary
Embodiments of present disclosure relates to method and gesture prediction system for predicting gesture of subjects for controlling AV during navigation. The gesture prediction system obtains data from sensors and generates context information of environment of AV. The gesture prediction system generates parameters for sampling frames for determining gesture of subjects. The gesture prediction system estimates current pose, and subsequent poses by extrapolating current pose and subsequent poses using deep learning techniques. Further, the gesture prediction system infers current behaviour of subjects by classifying current pose and subsequent poses into one of predefined gesture categories. The gesture prediction system predicts gesture of subjects based on current behaviour, context information and location information of subjects for controlling AV during navigation. Thus, the present disclosure forecast gestures of subjects at a faster rate and controls the AV during the navigation.