Tesla, inc. (20240112051). MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES simplified abstract
MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES
Organization Name
Inventor(s)
Anting Shen of Mountain View CA (US)
MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112051 titled 'MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES
Simplified Explanation
The patent application describes a method that involves using machine learning models operating at different frequencies to analyze images obtained from image sensors positioned around a vehicle. The location information of classified objects in the images is determined based on the analysis, with a subset of the images being analyzed by a second machine learning model at a frequency lower than the threshold frequency.
- Explanation of the patent:
- Obtaining images at a threshold frequency from image sensors around a vehicle - Determining location information of objects in the images - Analyzing images using a first machine learning model at the threshold frequency - Using output information from a second machine learning model for a subset of images at a lower frequency
Potential applications of this technology: - Autonomous driving systems - Object detection and tracking in real-time - Enhanced safety features for vehicles
Problems solved by this technology: - Improving accuracy and efficiency of object detection - Enhancing situational awareness for drivers - Optimizing decision-making processes for autonomous vehicles
Benefits of this technology: - Increased safety on the road - Improved navigation systems - Enhanced overall driving experience
Potential commercial applications of this technology: - Automotive industry for self-driving cars - Surveillance systems for security purposes - Traffic management and control systems
Possible prior art: - Similar systems using machine learning models at different frequencies for image analysis - Previous patents related to object detection and tracking in vehicles
Unanswered questions: 1. How does the integration of machine learning models at different frequencies improve the accuracy of object classification in images? 2. What are the potential limitations or challenges of implementing this technology in real-world scenarios, such as varying weather conditions or complex traffic environments?
Original Abstract Submitted
systems and methods include machine learning models operating at different frequencies. an example method includes obtaining images at a threshold frequency from one or more image sensors positioned about a vehicle. location information associated with objects classified in the images is determined based on the images. the images are analyzed via a first machine learning model at the threshold frequency. for a subset of the images, the first machine learning model uses output information from a second machine learning model, the second machine learning model being performed at less than the threshold frequency.
