Smart Vehicle Sensor Fusion and Driver Monitoring
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Solution Overview
Problem
Current smart vehicle technologies face challenges in efficiently navigating roads, detecting road-side objects, and maintaining safety due to limitations in sensor accuracy and reliability, especially in adverse weather conditions, and in ensuring driver attentiveness and vehicle control.
Innovation Solution
The implementation of a system that uses a combination of cameras, lidar, radar, and sonar sensors for road-side object detection and 3D model creation, along with neural networks for object recognition and vehicle control, and a method to monitor driver attentiveness using camera images and heart rate detection to ensure safe navigation and control transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors (cameras, lidar, radar, sonar) are used for road-side object detection, then detection accuracy and reliability are improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (cameras, lidar, radar, sonar) into an integrated sensor system that captures data from different modalities simultaneously. This merging approach allows the system to achieve improved detection reliability through multi-sensor fusion while managing complexity through unified processing architecture.
Solution Approach 2:
The sensor system is designed to perform multiple functions using a single integrated framework: object detection, 3D model creation, lane detection, and environmental mapping. This multi-functionality reduces the need for separate specialized systems for each task, thereby improving reliability without proportionally increasing complexity.
2Measurement precision
If neural networks are used for object recognition and 3D model creation, then navigation accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary processing of sensor data to create 3D models and detect features before final navigation decisions are made. By preparing processed representations in advance, the neural networks can operate more efficiently during critical navigation moments, reducing real-time processing time while maintaining high accuracy.
Solution Approach 2:
The complex task of object recognition and 3D modeling is divided into segmented processing stages: initial sensor data acquisition, preliminary feature extraction, 3D model construction, and final recognition decisions. This segmentation allows each stage to be optimized independently, improving overall accuracy while managing computational time through parallel processing of different segments.
3Reliability
If driver attentiveness is monitored using camera images and heart rate detection, then safety is improved, but privacy concerns and system complexity increase
Solution Approach 1:
The system uses physiological parameters (heart rate) as an intermediary indicator of driver attentiveness rather than directly monitoring cognitive state or personal information. This intermediary approach improves safety by providing objective measures of driver state while reducing privacy concerns and system complexity compared to direct cognitive monitoring.
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 navigation accuracy and safety by effectively detecting road-side objects and maintaining vehicle control, even in adverse conditions, and ensures timely intervention in case of driver fatigue or inattention, improving overall vehicle performance and safety.
Implementation Method 1
capturing images of the driver
Implementation Method 2
sending a radio signal toward the driver and detecting a heart rate from the driver based on a reflected radio signal
Data Source
AI summary
Smart car method to navigate a road includes detecting road-pavement markings using a camera and a sensor; creating a 3D model based on outputs of the camera and sensor; and navigating the road with a vehicle.