Trip Data Image Representation for Driving Behavior Prediction
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Solution Overview
Problem
Current predictive models struggle to effectively interpret and understand large volumes of trip data from vehicle sensors due to computational limitations, hindering accurate extraction of driving behaviors.
Innovation Solution
Transform trip data into an image representation using a high-depth tensor format, dividing data into segments based on time or distance, and employing a predictive model like a convolutional neural network (CNN) to extract driving behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trip data is directly input into predictive models, then the models can process the raw data, but the computational resources (processors and memory) are overwhelmed and the models cannot effectively extract driving behavior features
Solution Approach 1:
The patent divides trip data into multiple segments based on time periods or distance intervals. Each segment is transformed into a separate image representation, allowing the predictive model to process manageable portions of data sequentially rather than overwhelming computational resources with all raw data at once.
Solution Approach 2:
The patent transforms one-dimensional trip data sequences into two-dimensional image representations using depth maps. This dimensional transformation allows convolutional neural networks to effectively extract spatial and temporal features from the data, improving driving behavior recognition while maintaining computational efficiency.
2Loss of information
If all trip data from multiple sensors is collected, then comprehensive driving behavior information is available, but the data becomes difficult to interpret and understand by predictive models
Solution Approach 1:
The patent combines multiple sensor data types (accelerometer, gyroscope, magnetometer, barometer) into a composite depth map representation. Each sensor contributes to different channels of the image representation, creating a unified multi-channel image that preserves all sensor information while making it interpretable by image-based predictive models.
Solution Approach 2:
The patent introduces an intermediate transformation layer that converts raw sensor data into image representations. This intermediary step acts as a bridge between the raw multi-sensor data and the predictive model, making the data interpretable while preserving the underlying driving behavior information.
3Productivity
If trip data is divided into segments, then the data becomes more manageable for processing, but the segmentation process adds complexity to the data transformation pipeline
Solution Approach 1:
The patent employs a universal transformation process that handles both segmented and non-segmented data through the same image generation pipeline. The segmentation logic is separated from the transformation logic, allowing the same transformation function to work on data of any granularity, reducing overall system complexity.
Data Source
AI summary
Method and system for predicting driving behaviors of a driver by transforming trip data into an image representation are disclosed. For example, the method includes receiving trip data of one or more trips of a driver, dividing the trip data into a plurality of trip data segments based on a predetermined time period, wherein each trip data segment corresponds to a portion of the one or more trips, transforming the plurality of trip data segments into the image representation, and determining predicted driving behaviors of the driver based on the image representation of the one or more trips using a prediction model.