Vehicle Anti-Collision Warning Using Lane-Aware Sensor Fusion
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Solution Overview
Problem
Existing collision prevention systems fail to accurately identify obstacles when a vehicle is driving on a curve, leading to potential safety hazards due to misjudgment.
Innovation Solution
An early warning method for anti-collision using a vehicle-mounted device that combines image and radar sensors to determine the main lane line, fuse image and radar information, and calculate motion parameters to predict potential collisions, thereby improving obstacle identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a collision prevention system uses traditional obstacle identification methods, then the system structure remains simple, but the obstacle identification accuracy deteriorates when the vehicle is driving on a curve
Solution Approach 1:
The patent combines image information from cameras with radar information from radar sensors to perform fused detection of obstacles. This multi-sensor fusion approach improves obstacle identification accuracy, especially on curved roads, by cross-validating detections from different sensor types and resolving ambiguities that single sensors cannot address alone.
Solution Approach 2:
The system performs multiple functions using the same sensor suite: it detects obstacles, identifies lane lines, determines vehicle motion parameters, and calculates collision risks. By making the detection system multi-functional, the patent improves obstacle identification accuracy without proportionally increasing device complexity.
2Measurement precision
If the system fuses image and radar information for obstacle detection, then obstacle identification accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the obstacle detection process into distinct stages: image preprocessing, radar data processing, feature extraction from both sensors, data association and fusion, and final obstacle identification. This segmentation allows each stage to be optimized independently and facilitates parallel processing, reducing overall computational complexity while maintaining high detection accuracy.
Solution Approach 2:
The system performs preliminary processing of image and radar data before fusion, including noise filtering, feature extraction, and candidate obstacle identification. By preparing data in advance, the actual fusion process becomes computationally lighter and faster, reducing real-time processing complexity while preserving detection accuracy.
3Measurement precision
If the system calculates motion parameters and predicts collision time, then collision prediction accuracy improves, but the processing time increases
Solution Approach 1:
The system continuously tracks and updates motion parameters (velocity, acceleration, trajectory) of detected obstacles in advance, so that when collision risk assessment is needed, the calculations are already based on prepared data. This preliminary tracking reduces the time required for collision prediction while maintaining high accuracy.
Solution Approach 2:
The system calculates full collision prediction parameters (collision time, collision point, impact velocity) only for obstacles that pose a potential collision risk, rather than for all detected objects. This selective calculation reduces processing time while maintaining accurate collision prediction for relevant targets.
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 obstacle detection and collision prediction, reducing the likelihood of misjudgment and enhancing safety by providing timely warnings.
Implementation Method 1
obtain image information in front of the vehicle
Implementation Method 2
obtain radar information in front of the vehicle
Data Source
AI summary
An early warning method for anti-collision is provided. In the method, a vehicle-mounted device obtains image information and radar information and determines a main lane line of a road in a driving direction of a vehicle according to the image information. The vehicle-mounted device determines an obstacle located on the main lane line and obtains motion parameters of the vehicle and the obstacle by fusing of the image information and the radar information, and calculates an estimated collision time period from now to the vehicle collides with the obstacle based on the motion parameters, and calculates a braking time period of the vehicle and outputting an early warning in response that the braking time period is less than the estimated collision time period.