Lane Keeping Control Using LiDAR in Tunnel Entry Conditions
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Solution Overview
Problem
Existing vehicle driving assistance systems face challenges in maintaining lane accuracy due to environmental factors such as tunnel entry, camera contamination, and strong backlights, which can impair the camera's ability to recognize lanes.
Innovation Solution
The system employs a controller that uses lane information from both a camera and a lidar sensor, selectively excluding camera-derived lane information when the vehicle enters a tunnel, when camera contamination exceeds a certain threshold, or when strong backlights are present, and relies solely on lidar sensor data for lane keeping assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lane information from both camera and lidar sensor is used, then lane detection accuracy is improved under normal conditions, but reliability deteriorates when camera is affected by environmental factors such as tunnel entry, contamination, or strong backlights
Solution Approach 1:
The system dynamically adjusts the data source selection based on environmental conditions. The controller monitors camera reliability indicators (tunnel detection, contamination level, backlight intensity) and switches between using both camera and lidar data, using only lidar data, or using only camera data, thereby adapting the lane detection system to maintain reliability across varying operational conditions
Solution Approach 2:
The controller acts as an intermediary that evaluates the reliability of camera-derived lane information by analyzing environmental factors (tunnel presence, contamination degree, backlight intensity) and mediates between camera and lidar sensor inputs to determine the optimal data source for lane detection, ensuring reliable operation under diverse conditions
2Device complexity
If camera-derived lane information is always used, then system complexity is reduced, but measurement precision deteriorates in adverse environmental conditions
Solution Approach 1:
The system implements dynamic sensor fusion with conditional logic that adjusts data source composition based on environmental assessment. The controller evaluates camera reliability indicators and dynamically selects between three operational modes: using both camera and lidar data for normal conditions, using only lidar data when camera reliability is low, and using only camera data when lidar data is unavailable, thereby optimizing measurement precision without excessive complexity
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 approach enhances the accuracy and reliability of lane keeping assist and lane following assist systems by ensuring continuous lane detection and vehicle control, even under adverse conditions.
Implementation Method 1
lane information obtained by a lidar sensor
Data Source
AI summary
Disclosed is an apparatus for assisting driving of a vehicle including a camera provided in the vehicle, a lidar sensor provided in the vehicle, and a controller configured to control the vehicle to prevent to deviate from a lane based on lane information obtained by the camera and the lidar sensor, wherein the controller excludes the lane information obtained by the camera and controls the vehicle to prevent to deviate from the lane based on the lane information obtained by the lidar sensor, when the vehicle enters a tunnel.