Traffic Monitoring Using Density Maps for Gap Entry Prediction
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Solution Overview
Problem
Conventional technologies face challenges in accurately predicting whether a target mobile object, such as a two-wheeled vehicle, will enter between other mobile objects in mixed traffic scenarios.
Innovation Solution
A traffic monitoring system that uses a hardware processor to acquire the position of mobile objects, generate a density distribution based on detection device information, and predict the likelihood of a target object entering a gap between two mobile objects by analyzing temporal changes in density values and relative speeds, with reference values and threshold lengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods are used to track mobile objects, then the system structure remains simple, but the prediction accuracy of whether a target object will enter a gap between other objects is insufficient
Solution Approach 1:
The patent introduces a density distribution dimension by superimposing index values representing mobile objects onto a spatial grid. This transforms the prediction problem from direct trajectory analysis to density-based probability assessment, enabling accurate prediction of gap entry behavior without requiring complex individual trajectory modeling.
Solution Approach 2:
The patent changes the parameter representation from discrete object positions to continuous density values on a grid. By calculating temporal changes in density values and comparing them against reference values, the system achieves accurate prediction of gap entry events while maintaining computational efficiency.
2Reliability
If the system monitors all mobile objects in detail to improve prediction accuracy, then the prediction reliability improves, but the computational load and processing time increase
Solution Approach 1:
The patent applies partial action by focusing computational resources only on relevant regions of the traffic scene. By monitoring density changes in specific areas where gap entry is likely to occur, rather than analyzing every mobile object's complete trajectory, the system achieves reliable predictions with reduced processing time.
Solution Approach 2:
The patent performs preliminary action by pre-calculating density distributions and establishing reference values for different traffic conditions. This allows the system to quickly compare current states against pre-established benchmarks, enabling fast and reliable predictions without extensive real-time computation.
Data Source
AI summary
A traffic monitoring system includes a storage device that stores a program and a hardware processor, in which the hardware processor executes a program stored in the storage device, thereby acquiring a position of a mobile object based on information from a detection device for detecting the position of the mobile object, generating information on a density distribution in which index values having a distribution according to the position of the mobile object are superimposed on one another for a plurality of mobile objects, and predicting whether a target mobile object is likely to enter a gap between two of the mobile objects based on a temporal change in density value indicated by the information on a density distribution.