Vehicle Behavior Prediction Using Camera-Based Class Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle sensors do not collect all types of information necessary for various applications, limiting their effectiveness in providing precise vehicle location and behavior prediction, especially for navigational and control purposes in autonomous or semi-autonomous vehicles.
Innovation Solution
A method and apparatus that utilize image data from vehicle cameras to identify the class of a vehicle through machine learning models, supplementing existing sensor data to predict vehicle behavior by analyzing physical characteristics and points of interest, and providing alerts or activating autonomous driving modes as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle sensors are used to collect data, then the system structure is simple, but the information completeness and measurement precision are insufficient for accurate vehicle behavior prediction
Solution Approach 1:
The patent introduces image data as an intermediary medium to bridge the gap between simple sensor systems and accurate vehicle behavior prediction. Instead of directly enhancing sensor systems, the patent uses cameras to capture visual information that indirectly provides insights into vehicle behavior, thereby improving prediction accuracy without significantly complicating the sensor architecture
Solution Approach 2:
The patent replaces traditional mechanical/physical sensors with optical imaging systems. By using cameras to capture images that are then processed through machine learning models, the system substitutes direct physical measurement with optical detection and computational analysis, achieving better measurement precision while maintaining relatively simple device complexity
2Loss of information
If more sensors are added to collect comprehensive information, then the information completeness improves, but the device complexity and cost increase
Solution Approach 1:
The patent makes image data serve multiple functions simultaneously. The same image data is used for both vehicle classification and behavior prediction, eliminating the need for separate sensor systems for each function. This multi-functional approach ensures information completeness while avoiding the proliferation of sensors
Solution Approach 2:
The patent transforms the nature of data collection by changing from direct physical parameter measurement (using specialized sensors) to optical parameter capture (using cameras). This parameter transformation allows a single type of sensor to provide diverse information through different processing approaches, reducing the need for multiple specialized sensors
3Measurement precision
If image data is used to identify vehicle class and predict behavior, then the measurement precision and information value improve, but the data processing complexity increases
Solution Approach 1:
The patent divides the complex task of vehicle behavior prediction into separate processing stages. First, image data is processed to determine vehicle class through one machine learning model. Then, this class information is used as a feature in a second model for behavior prediction. This segmentation of processing tasks reduces the overall complexity compared to using a single monolithic model
Data Source
AI summary
A method, system, and computer readable storage medium are provided for predicting behavior of a vehicle. More specifically, image data of the vehicle is obtained by generating class prediction data for the vehicle and then identifying one or more related points of interest. The method, system, and computer readable storage medium then predict the behavior of the vehicle based upon the one or more points of interest and the class prediction data.