Obstacle State Detection via Multi-Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unmanned vehicle systems face challenges in accurately and reliably determining the static state of obstacles due to limitations in single sensors, leading to potential misjudgments in obstacle state assessment.
Innovation Solution
The method involves obtaining real-time obstacle velocities from multiple sensors, calculating belief function assignment values for status parameters using a D-S evidence combination technology, and fusing these values to determine the obstacle's state, ensuring algorithm independence and system integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to detect obstacle velocity, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent segments the detection task by assigning different sensors (laser radar, millimeter wave radar, ultrasonic sensor) to detect obstacle velocity from different physical principles and characteristics. Each sensor targets specific aspects of obstacle detection, dividing the complex detection problem into manageable segments that can be processed independently and then fused to achieve high precision obstacle state determination
Solution Approach 2:
The patent merges data from multiple independent sensors using belief function theory and D-S evidence combination technology. By combining velocity information from laser radar, millimeter wave radar, and ultrasonic sensors, the system achieves more reliable and accurate obstacle state judgment than any single sensor could provide alone, resolving the contradiction between using multiple sensors and system complexity
2Reliability
If belief function theory with D-S evidence combination is used to fuse sensor data, then obstacle state judgment reliability improves, but algorithm complexity increases
Solution Approach 1:
The patent introduces belief function theory and D-S evidence combination technology as intermediary mathematical frameworks to fuse sensor data. These intermediary theories provide a systematic method to combine velocity information from multiple sensors with different reliability levels, transforming complex multi-sensor fusion into a structured computational process that improves judgment reliability while managing algorithmic complexity through established mathematical principles
3Speed
If real-time obstacle velocity detection is performed continuously, then responsiveness and decision-making speed improve, but energy consumption increases
Solution Approach 1:
The patent implements dynamic obstacle detection by continuously monitoring real-time obstacle velocity and adjusting detection based on actual environmental conditions. The system dynamically processes sensor data only when obstacles are detected or conditions change, rather than performing static continuous processing, thereby maintaining high responsiveness while reducing unnecessary energy consumption during stable conditions
Data Source
AI summary
Embodiments of the present disclosure disclose a method and an apparatus for determining a static state of an obstacle, a device and a storage medium. The method includes the following. Real-time obstacle velocities are obtained by detecting an obstacle via at least two sensors. A belief function assignment value of each sensor is calculated for at least two status parameters respectively according to the real-time obstacle velocity corresponding to each sensor. The belief function assignment value of each sensor is fused for each status parameter with a D-S evidence combination technology to obtain a fused belief function assignment value corresponding to each status parameter. A static state of the obstacle is judged according to the fused belief function assignment value corresponding to each status parameter.