Rivian ip holdings, llc (20240221389). SYSTEM AND METHOD FOR DEEP LEARNING BASED LANE CURVATURE DETECTION FROM 2D IMAGES simplified abstract
Contents
SYSTEM AND METHOD FOR DEEP LEARNING BASED LANE CURVATURE DETECTION FROM 2D IMAGES
Organization Name
Inventor(s)
Andrei Polzounov of Seattle WA (US)
Vikram Vijayanbabu Appia of San Jose CA (US)
Ravi Kumar Satzoda of Milpitas CA (US)
SYSTEM AND METHOD FOR DEEP LEARNING BASED LANE CURVATURE DETECTION FROM 2D IMAGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221389 titled 'SYSTEM AND METHOD FOR DEEP LEARNING BASED LANE CURVATURE DETECTION FROM 2D IMAGES
The abstract of this patent application describes methods and systems for detecting a lane boundary in a two-dimensional image captured by a vehicle, determining the sinuosity of the lane, and facilitating vehicle actions based on this information.
- Detection of a line in a two-dimensional image captured by a vehicle
- Determination of whether the line is a lane boundary for a lane used by the vehicle
- Curve fitting for the lane boundary based on the detected line
- Determination of the sinuosity of the lane based on the curve fit
- Facilitation of vehicle actions based on the determined sinuosity
Potential Applications: - Autonomous driving systems - Lane departure warning systems - Traffic flow optimization
Problems Solved: - Enhancing vehicle navigation accuracy - Improving lane-keeping capabilities - Increasing road safety
Benefits: - Enhanced vehicle safety - Improved navigation efficiency - Reduced risk of accidents
Commercial Applications: Title: "Advanced Lane Detection Technology for Autonomous Vehicles" This technology can be utilized in autonomous vehicles, transportation systems, and road infrastructure development. It has the potential to revolutionize the automotive industry by enhancing the safety and efficiency of vehicle navigation.
Questions about Lane Detection Technology: 1. How does this technology improve road safety for both drivers and pedestrians? This technology enhances road safety by accurately detecting lane boundaries and facilitating vehicle actions based on the sinuosity of the lane, reducing the risk of accidents caused by lane departure or erratic driving behavior.
2. What are the key differences between traditional lane detection systems and this innovative approach? Traditional lane detection systems may struggle with accurately identifying lane boundaries in complex road scenarios, while this technology utilizes advanced processing circuitry to detect lines, determine lane boundaries, and calculate sinuosity for more precise navigation.
Original Abstract Submitted
methods and systems are provided to detect an instance of a line in a two-dimensional image captured by a vehicle and to determine whether the instance of the line is a lane boundary for a lane that will be used by the vehicle to traverse a route. an instance of a line in a two-dimensional image captured by a vehicle is detected using processing circuitry. the processing circuitry is used to determine that the instance of the line is a lane boundary for a lane associated with the vehicle. a curve fit for the lane boundary based on the instance of the line is determined using the processing circuitry. the processing circuitry is also used to determine a sinuosity of the lane based on the curve fit. execution of a vehicle action is facilitated using the processing circuitry based on the determined sinuosity.
