Traffic Jam End Detection Using Travel Data and Vehicle Images
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Solution Overview
Problem
Existing traffic jam detection methods struggle to accurately identify the leading position and continuity of traffic jams, especially when vehicles are traveling at low speeds or when jams are partially interrupted.
Innovation Solution
An information processing device that combines travel data and image analysis to determine traffic jam sections, using first and second detection processes to identify the end portions of traffic jams, and dynamically expands the detection range based on travel data from vehicles outside the initial section.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic jam detection is performed based only on travel data from vehicles within an initial section, then the detection process is simple and quick, but the detection accuracy is insufficient when traffic jams are partially interrupted or vehicles are traveling at low speeds
Solution Approach 1:
The detection process is divided into multiple stages: first detecting traffic jams within an initial section, then dynamically expanding to detect additional sections if needed. This segmentation allows the system to start with a simple detection and only increase complexity when necessary to improve accuracy.
Solution Approach 2:
The detection scope is made dynamic rather than fixed. The system initially detects traffic jams in a first section, then based on the detected end portion and travel data, dynamically determines whether to expand detection to a second section. This dynamic adaptation allows the system to balance simplicity and accuracy based on actual traffic conditions.
2Measurement precision
If the detection scope is limited to a fixed initial section, then the processing time is short and the system is simple, but the detection accuracy decreases when traffic jams extend beyond the initial section or are partially interrupted
Solution Approach 1:
The system performs preliminary detection within the first section before deciding whether to expand to a second section. This preliminary action allows quick initial detection while preserving the option to perform additional detection if the traffic jam appears to extend beyond the initial section, thus balancing speed and accuracy.
Solution Approach 2:
The system uses feedback from the first detection process and travel data to determine whether to perform a second detection process. If the end portion of the traffic jam in the first section indicates potential extension or interruption, the system feedback-driven expands detection to the second section, ensuring accuracy without unnecessary processing time.
3Measurement precision
If travel data from vehicles outside the initial section is acquired and analyzed, then the detection accuracy for continuous traffic jams is improved, but the data processing load and system complexity increase
Solution Approach 1:
The system dynamically adjusts the scope of data collection based on detection needs. Travel data from vehicles outside the initial section is acquired only when the first detection process indicates potential traffic jam extension or interruption. This dynamic data collection approach improves continuity detection accuracy while minimizing unnecessary data processing complexity.
4Measurement precision
If multiple detection processes are performed to accurately identify traffic jam end portions, then the detection accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The detection process is segmented into a mandatory first detection process and an optional second detection process. The first process always executes to establish baseline accuracy, while the second process executes only when conditions warrant expansion. This segmentation ensures accurate end portion detection while minimizing unnecessary processing time through conditional execution.
Data Source
AI summary
An information processing device executes the followings: determining a first section in which a traffic jam is occurring based on first travel data; performing a first detection process based on a first image captured by a vehicle positioned around the first section, the first detection process being a process of detecting an end portion of the first section; when an end portion of the first section is detected, acquiring second travel data from a plurality of vehicles positioned outside the first section, and determining based on the second travel data whether there is a second section outside the first section, the second section being a section in which a series of traffic jam is occurring together with the first section; when it is determined that there is a second section, detecting an end portion of the second section; and presenting traffic jam information to a user.