LiDAR Camera Sensor Fusion for ADAS Object Detection
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Solution Overview
Problem
Current advanced driver assistance systems (ADAS) face challenges in improving object detection rates and reliability, particularly when using LiDAR and camera sensors, as they have limitations in distance accuracy and detection rates, leading to potential erroneous operations in autonomous emergency braking systems.
Innovation Solution
An object detecting apparatus that divides the common detection area of LiDAR and camera sensors into sub-areas, processes data accordingly, and adjusts object detection thresholds to enhance detection accuracy, using a sensor unit with LiDAR and camera sensors, a data analyzing unit, and a sensor signal converging unit to determine dangerous objects based on coordinated data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR sensor is used for object detection, then distance accuracy is improved, but detection reliability deteriorates
Solution Approach 1:
The patent combines LiDAR sensor data and camera sensor data through data fusion techniques. The LiDAR provides accurate distance measurements while the camera provides reliable object classification and detection, compensating for each other's weaknesses to achieve both high distance accuracy and high detection reliability
Solution Approach 2:
The system creates a composite detection approach by integrating multiple sensor types (LiDAR and camera) with different detection characteristics. This composite sensing system leverages the complementary strengths of each sensor to overcome the limitations of individual sensors
2Reliability
If camera sensor is used for object detection, then detection reliability is improved, but distance accuracy deteriorates
Solution Approach 1:
The patent merges camera sensor data with LiDAR sensor data, where the camera provides reliable object detection and classification while the LiDAR supplies precise distance measurements, achieving both high reliability and high distance accuracy simultaneously
3Reliability
If both LiDAR and camera sensors are used for complementary detection, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The patent integrates LiDAR and camera sensors into a unified detection system with centralized data processing and fusion algorithms. This merging approach coordinates multiple sensors to work together efficiently, improving detection reliability while managing system complexity through integrated architecture
Solution Approach 2:
The system design creates a multi-functional sensor platform that performs both distance measurement (LiDAR function) and object recognition (camera function) within a single integrated system, reducing overall complexity compared to separate independent systems
4Ease of operation
If common detection area is processed uniformly, then processing simplicity is maintained, but object detection rate deteriorates
Solution Approach 1:
The patent divides the common detection area into multiple sub-areas and applies different processing strategies to each sub-area based on its characteristics. This segmentation enables targeted processing that improves object detection rate by focusing computational resources on critical regions while maintaining overall system manageability
5Productivity
If detection area is divided into sub-areas with different processing, then object detection rate is improved, but processing complexity increases
Solution Approach 1:
The patent segments the detection area into sub-areas with distinct processing rules, improving detection rate by applying specialized algorithms to specific regions. The segmentation is implemented through software logic that divides the field of view into manageable zones, each handled by dedicated processing routines
6Productivity
If object detection threshold is lowered to increase detection rate, then object detection rate is improved, but erroneous operations increase
Solution Approach 1:
The patent combines detection results from both LiDAR and camera sensors with different threshold criteria. By merging the detection outputs, the system achieves high detection sensitivity while maintaining low false positive rates through cross-validation between sensor types
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously evaluated and thresholds are dynamically adjusted based on detection confidence levels and environmental conditions, reducing erroneous operations while maintaining high detection rates
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 increases object detection rates and reliability, reduces erroneous operations, and improves accuracy in detecting stopped objects by extracting lane information and adjusting sensitivity conditions, thereby enhancing the safety of autonomous emergency braking systems.
Implementation Method 1
a LiDAR sensor for collecting LiDAR data
Implementation Method 2
a camera sensor for collecting image data
Data Source
AI summary
An object detecting apparatus and an operating method thereof are disclosed. An apparatus for detecting an object includes: a sensor unit including a light distance and ranging (LiDAR) sensor for collecting LiDAR data and a camera sensor for collecting image data; an area dividing unit configured to divide a common detection area of the LiDAR sensor and the camera sensor into a plurality of areas; a data analyzing unit configured to analyze the LiDAR data to extract a first object information and/or analyze the image data to extract a second object information; and a sensor signal converging unit configured to determine whether a dangerous object exists for each of the divided areas based on the first object information and/or the second object information.