Autonomous Vehicle Speed Bump Detection Training Data Generation
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Solution Overview
Problem
Collecting and labeling training data for speed bump detection in autonomous vehicles is resource-intensive, costly, and slow due to the variability of speed bumps in different countries, making it challenging for convolutional neural networks to accurately detect them.
Innovation Solution
An apparatus comprising a capture device and a processor that generates pixel data, performs computer vision operations to detect objects, analyzes changes in vehicle orientation, and automatically annotates video frames to provide training data for speed bump detection, utilizing a gyroscope to detect orientation changes and correlate with camera images, enabling automatic labeling and object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection and labeling of training data is used, then labeling accuracy can be ensured, but the process becomes resource-intensive, costly and slow
Solution Approach 1:
The system performs self-labeling by automatically detecting objects in video frames and generating training data annotations without human intervention. The autonomous vehicle's own sensors and processors are used to create labeled datasets, eliminating the need for external manual labeling teams while maintaining high accuracy through the vehicle's perception capabilities.
Solution Approach 2:
The patent replaces the mechanical process of manual data collection and labeling with an automated computational system. Computer vision algorithms and neural networks automatically detect and label objects in video frames, substituting human labor with algorithmic processing to achieve both high productivity and maintained labeling quality.
2Adaptability or versatility
If diverse speed bump variations are captured for training data, then detection accuracy across different countries improves, but data collection complexity increases
Solution Approach 1:
The system uses a universal data collection approach where the autonomous vehicle's existing camera and sensor systems serve multiple purposes: both for navigation and for collecting diverse training data. The same hardware captures various speed bump types across different countries, eliminating the need for specialized collection equipment while achieving global adaptability.
Solution Approach 2:
The system dynamically adapts to different speed bump variations encountered in various countries by continuously collecting and processing real-world data. The training data generation process is flexible and responsive to the diversity of speed bump designs, automatically adjusting to capture relevant variations without requiring pre-planned collection strategies for each country.
3Reliability
If more training data is collected to improve neural network performance, then detection accuracy improves, but resource consumption and time requirements increase
Solution Approach 1:
The system continuously collects training data during normal autonomous vehicle operation without interrupting its primary function. Every time the vehicle captures video frames and processes them, it simultaneously generates training data. This continuous parallel operation eliminates the need for separate dedicated data collection missions, achieving both high detection accuracy and time efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution facilitates the generation of annotated video frames for speed bump detection, improving the accuracy and efficiency of training data collection, which enhances the performance of convolutional neural networks in detecting speed bumps and other obstacles, thereby improving autonomous vehicle navigation.
Implementation Method 1
detect a change in orientation of the vehicle
Data Source
AI summary
An apparatus including a capture device and a processor. The capture device may be configured to generate pixel data corresponding to an exterior view from a vehicle. The processor may be configured to generate video frames from the pixel data, perform computer vision operations on the video frames to detect objects in the video frames and determine characteristics of the objects, detect a change in orientation of the vehicle at a first time, analyze the characteristics of the objects at a second time to determine a cause of the change in orientation of the vehicle and generate annotations for the video frames that comprise the objects determined to have caused the change in orientation of the vehicle. The second time may be earlier than the first time.