Waymo llc (20240286626). Methods and Systems for Automatic Introspective Perception simplified abstract
Contents
- 1 Methods and Systems for Automatic Introspective Perception
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Methods and Systems for Automatic Introspective Perception - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Object Detection and Automatic Response Systems
- 1.13 Original Abstract Submitted
Methods and Systems for Automatic Introspective Perception
Organization Name
Inventor(s)
Mark Calleija of Los Altos CA (US)
Clayton Kunz of Mill Valley CA (US)
Matthew Langford of Mountain View CA (US)
Ramadev Hukkeri of Pittsburgh PA (US)
Daniel Rothenberg of Frederick CO (US)
Methods and Systems for Automatic Introspective Perception - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240286626 titled 'Methods and Systems for Automatic Introspective Perception
Simplified Explanation
The patent application describes techniques and systems for a computing system to use sensor data from an autonomous vehicle to detect objects in the vehicle's environment and adjust the vehicle's control strategy based on the detected objects.
- The computing system uses sensor data to detect objects in the vehicle's environment.
- It determines the distance between the object and the sensor.
- It compares this distance to a baseline distance based on prior detections of similar objects.
- The control strategy of the vehicle is adjusted based on this comparison.
Key Features and Innovation
- Utilizes sensor data from an autonomous vehicle to detect objects in the environment.
- Determines detection distance and compares it to a baseline distance.
- Adjusts the vehicle's control strategy based on the comparison.
Potential Applications
This technology can be applied in autonomous vehicles, robotics, and other systems that require object detection and automatic response capabilities.
Problems Solved
- Enhances object detection capabilities in autonomous vehicles.
- Improves automatic response systems for vehicles and other applications.
Benefits
- Increased safety through improved object detection.
- Enhanced efficiency in vehicle control strategies.
- Potential for reduced accidents and improved navigation.
Commercial Applications
The technology can be utilized in the automotive industry for autonomous vehicles, as well as in industries that require automated object detection and response systems.
Prior Art
Readers can explore prior patents related to object detection in autonomous vehicles and automatic response systems to gain a deeper understanding of the existing technology in this field.
Frequently Updated Research
Stay informed about the latest advancements in object detection technology for autonomous vehicles and automatic response systems to ensure you are up to date with the latest developments.
Questions about Object Detection and Automatic Response Systems
1. How does this technology improve safety in autonomous vehicles?
- This technology enhances safety by improving object detection capabilities and adjusting control strategies based on detected objects.
2. What are the potential applications of this technology beyond autonomous vehicles?
- This technology can be applied in robotics and other systems that require object detection and automatic response capabilities.
Original Abstract Submitted
example embodiments relate to self-supervisory and automatic response techniques and systems. a computing system may use sensor data from an autonomous vehicle sensor to detect an object in the environment of the vehicle as the vehicle navigates a path. the computing system may then determine a detection distance between the object and the sensor responsive to detecting the object. the computing system may then perform a comparison between the detection distance and a baseline detection distance that depends on one or more prior detections of given objects that are in the same classification group as the object. the computing system may then adjust a control strategy for the vehicle based on the comparison.