Driving Agent Training With Dashcam, GPS, and IMU Data
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Solution Overview
Problem
Current self-driving vehicle technologies face challenges in obtaining realistic driving data, particularly in dangerous situations, due to limited and non-naturalistic data collection methods, leading to inefficient resource consumption and potential dangerous driving maneuvers.
Innovation Solution
A neural network model trained on real-world driving data from dashcam video, GPS, and IMU data to predict driving behavior, decoupling from physical vehicle inputs and utilizing auxiliary tasks for semantic understanding, thereby conserving computing resources and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world driving data is collected using traditional methods (hired drivers with sensors), then driving behavior data can be obtained, but the data is limited, non-naturalistic, and redundant, leading to inefficient training resources
Solution Approach 1:
The patent uses video generation models to create synthetic driving scenario data that copies and replicates real-world driving situations. Instead of relying on limited physical data collection, the system generates virtual representations of driving scenarios including dangerous situations, thereby increasing data quantity while maintaining reliability through realistic simulation.
Solution Approach 2:
The system employs self-supervised learning where the model learns from the data itself without requiring extensive manual labeling. The video generation model and training framework enable the system to automatically learn driving patterns and semantics from raw video data, reducing the need for human-annotated datasets and improving both data quantity and quality efficiency.
2Measurement precision
If more driving data is collected to improve model training, then prediction accuracy may improve, but resource consumption increases and dangerous situations remain underrepresented
Solution Approach 1:
The patent applies partial action by focusing training on specific critical aspects of driving scenarios rather than processing all possible data uniformly. The auxiliary tasks and video generation model concentrate computational resources on learning essential driving semantics and dangerous situation recognition, achieving high prediction accuracy without exhaustive resource consumption.
Solution Approach 2:
The system performs preliminary data generation and preprocessing through the video generation model before main training occurs. By pre-generating diverse driving scenarios and pre-processing video data into appropriate formats, the system reduces the computational burden during actual model training, thereby improving prediction accuracy while controlling resource usage.
3Adaptability or versatility
If traditional data collection methods are used, then some driving data is obtained, but dangerous situations are underrepresented and data lacks naturalistic diversity
Solution Approach 1:
The patent implements dynamic scenario generation where the video generation model can adaptively create diverse driving scenarios based on learned patterns. The system dynamically adjusts generated scenarios to include various weather conditions, traffic situations, and dangerous events, ensuring both diversity and realism through continuous learning from real driving data distributions.
Solution Approach 2:
The system changes key parameters of driving scenarios through the video generation model, manipulating elements such as weather conditions, traffic density, vehicle speeds, and road conditions. By systematically varying these parameters while maintaining realistic physics and visual appearance, the system achieves high adaptability across different driving conditions while preserving data realism.
Data Source
AI summary
A device may receive video data and corresponding GPS data and IMU data associated with a vehicle, and may remove video frames from the video data to generate modified video data. The device may select objects and image regions of video frames of the modified video data, and may determine a current speed and a current turn angle of the vehicle based on the GPS data, the IMU data, and the modified video data. The device may mask the objects of the video frames of the modified video data to learn first features, and may mask the image regions of the video frames of the modified video data to learn second features. The device may generate a trained neural network model based on the current speed, the current turn angle, the first features, and the second features, and may implement the trained neural network model in the vehicle.