LIDAR Lane Marker Interpolation for Vehicle Blind Spots
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Solution Overview
Problem
Existing vehicle vision systems struggle to accurately determine lane markers, especially when the camera's view is obstructed by the vehicle's hood or front bumper, leading to inaccurate lane position estimation due to blind spots.
Innovation Solution
A vehicular driving assistance system that combines a CMOS imaging array camera with LIDAR sensors to capture image and sensor data, interpolating lane marker positions using data from both the camera and LIDAR sensors to estimate the location of the vehicle's front wheel relative to the lane markers, even in obstructed views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a camera is used to capture image data for lane marker determination, then the system can identify lane markers in the field of view, but the vehicle hood or front bumper creates blind spots that obstruct the view of lane markers immediately in front of or along the side of the vehicle
Solution Approach 1:
The patent combines camera image data with LIDAR sensor data to create a comprehensive lane marker detection system. The camera captures lane markers in visible areas while the LIDAR sensor detects lane markers in blind spot areas, and the ECU merges both data sources to determine complete lane marker positions, eliminating detection gaps caused by hood obstruction.
Solution Approach 2:
The ECU acts as an intermediary that processes and integrates data from both the camera and LIDAR sensor. It correlates the image data from the camera with the sensor data from the LIDAR to interpolate and determine lane marker positions that would otherwise be invisible to the camera due to hood obstruction.
2Ease of operation
If the camera is positioned to view forward of the vehicle, then it can capture traffic lane information, but the vehicle hood obstructs the view of lane markers immediately in front of the vehicle
Solution Approach 1:
The system merges the camera's wide field of view capability with the LIDAR's ability to detect objects in obscured areas. The camera provides lane marker information where visible, while the LIDAR supplements this with data from areas blocked by the hood, creating a complete and accurate lane marker position determination.
3Device complexity
If only camera-based vision systems are used, then the system structure remains simple, but lane marker determination accuracy deteriorates in obstructed views
Solution Approach 1:
The patent merges optical sensing (camera) with active sensing (LIDAR) to achieve accurate lane marker detection. This multi-sensor approach improves measurement precision in obstructed views while maintaining reasonable system complexity through integrated processing in the ECU.
Solution Approach 2:
The ECU serves as an intermediary that intelligently processes and correlates data from multiple sensor types, enabling accurate lane marker determination despite the increased device complexity of using both camera and LIDAR sensors together.
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
Enhances the accuracy of lane marker detection and estimation, reducing blind spots and improving driving assistance systems such as lane departure warning, adaptive cruise control, and collision avoidance by providing precise location data of lane markers relative to the vehicle.
Implementation Method 1
The system includes a LIDAR sensor disposed at the equipped vehicle that captures sensor data
Implementation Method 2
the LIDAR sensor emits laser pulses and receives reflected laser light back
Implementation Method 3
The camera includes a CMOS imaging array with at least one million photosensors arranged in rows and columns
Data Source
AI summary
A vehicular driving assistance system includes a camera and a LIDAR sensor disposed at a vehicle. An electronic control unit (ECU) includes at least one data processor. The system, responsive to processing of image data captured by the camera, determines a lane marker ahead of the vehicle. The system, responsive to processing of sensor data captured by the LIDAR sensor, determines a first portion of the lane marker forward of the vehicle and determines a second portion of the lane marker rearward of the vehicle. The system interpolates the lane marker between the first portion and the second portion. The system, responsive to the determined lane marker and the interpolated lane marker, estimates location of a front wheel of the vehicle relative to the lane marker.