Lane Change Detection Using LIDAR-Camera Sensor Fusion
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately detecting lane changes of objects around a vehicle, particularly in congested conditions, due to limitations in radar sensor accuracy for lateral direction object detection and reliability issues with camera sensors for three-dimensional shape and speed information.
Innovation Solution
A method and apparatus using a LIDAR sensor and camera sensor combination to detect objects, estimate lane changes by selecting candidate objects based on lane edge information and movement data, and determine if they change lanes through Kalman filtering for speed and angular speed estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a radar sensor is used to detect objects around the driving vehicle, then the detection range and speed information can be obtained, but the accuracy of lateral direction detection is low and shape information cannot be provided
Solution Approach 1:
The patent combines LIDAR sensor data with camera sensor data to create a comprehensive object detection system. The LIDAR provides accurate three-dimensional position and shape information while the camera provides visual confirmation and additional contextual data, together resolving the limitations of using either sensor alone for lateral direction detection and shape identification
2Measurement precision
If a camera sensor is used to detect objects around the driving vehicle, then two-dimensional detection is possible, but reliability in three-dimensional shape and speed information is low
Solution Approach 1:
The LIDAR sensor acts as an intermediary that bridges the gap between two-dimensional camera detection and three-dimensional object understanding. By providing accurate range, depth, and velocity data through time-of-flight measurements, the LIDAR enables reliable three-dimensional shape reconstruction and speed estimation while the camera provides visual context
3Measurement precision
If only normal traveling situation data is used for object detection, then detection accuracy is adequate, but rapid determination in congested state cannot be achieved
Solution Approach 1:
The system performs preliminary analysis by selecting candidate objects that are likely to change lanes based on their current position and trajectory before congestion occurs. During congested conditions, this pre-identified candidate list allows for rapid determination of lane changes without requiring full re-analysis of all surrounding objects, thus maintaining both accuracy and speed in urgent situations
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
Enables accurate determination of lane changes in both normal and congested driving conditions, improving the detection of potentially dangerous objects and generating effective avoidance routes for autonomous vehicles.
Implementation Method 1
a LIDAR sensor scanning information obtained repeatedly at every predetermined period of time by a sensor scanning surroundings of a driving vehicle
Data Source
AI summary
A method for determining a lane change, performed by an apparatus for determining a lane change of an object located around a driving vehicle with which is equipped a sensor, the method including, detecting a plurality of objects located around the driving vehicle using scanning information obtained repeatedly at every predetermined period of time by the sensor scanning surroundings of the driving vehicle, selecting at least one candidate object estimated to change lanes among the plurality of objects based on previously detected lane edge information and determining whether the candidate object changes lanes based on information on movement of the candidate object.