Sensor Fusion Track Prioritization for False Track Reduction
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Solution Overview
Problem
The increasing number of sensors in autonomous vehicles leads to higher false track detection, which affects the performance of sensor fusion systems by increasing computational load and network overload, making it challenging to manage track life cycles effectively and prioritize tracks accurately.
Innovation Solution
A method and apparatus that determine a management index for tracks based on maintenance time, accuracy of sensor information, and collision risk, using weights to combine these factors into a single index for prioritizing and managing tracks, thereby improving the efficiency of sensor fusion systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of sensors is increased to improve detection accuracy, then the reliability of object detection is improved, but the number of false tracks increases and computational load increases
Solution Approach 1:
The patent applies parameter changes by introducing a management index that dynamically evaluates tracks based on multiple parameters including maintenance time, sensor accuracy, and collision risk. This allows the system to adaptively prioritize and manage tracks by changing the evaluation parameters rather than using fixed criteria, thereby handling the increased complexity from multiple sensors effectively
2Quantity of substance
If the number of tracks is increased to improve comprehensive monitoring, then the coverage of monitored objects is improved, but the computational load and network overload increase
Solution Approach 1:
The patent extracts and prioritizes only the most critical tracks by evaluating them against a management index that considers maintenance time, sensor accuracy, and collision risk. This allows the system to focus computational resources on high-priority tracks while reducing or eliminating low-priority false tracks, thereby reducing overall computational load while maintaining comprehensive monitoring coverage
Solution Approach 2:
The patent applies local quality by assigning different management priorities to different tracks based on their specific characteristics. Instead of uniform processing, each track is evaluated individually using the management index, allowing high-priority tracks (those with high collision risk or low maintenance time) to receive more computational attention while low-priority tracks are processed more efficiently or eliminated
3Duration of action of stationary object
If track maintenance time is extended to improve tracking stability, then the stability of track continuity is improved, but false tracks are continuously maintained
Solution Approach 1:
The patent applies dynamics by making the track management criteria adaptive rather than static. The management index dynamically evaluates each track based on its maintenance time, sensor accuracy, and collision risk, allowing the system to automatically adjust which tracks are maintained and which are eliminated. This dynamic approach prevents false tracks from being continuously maintained while preserving stable true tracks
Data Source
AI summary
A method for operating a vehicle is introduced. The method may comprise detecting, based on sensing information from one or more sensors of the vehicle, track information associated with an object, and determining a management index of the track information based on at least one of a maintenance time of the track information associated with an amount of time the object has been tracked, an accuracy of information from the one or more sensors, or a risk of collision between the vehicle and the object.