Base Station Traffic Obstacle Detection via Vehicle Behavior Learning
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Solution Overview
Problem
Existing Intelligent Transport Systems (ITS) face challenges in accurately determining the presence and position of traffic obstacles on roads, especially when vehicles perform obstacle-avoidance behaviors without actual obstacles being present, leading to increased processing loads for individual vehicles.
Innovation Solution
A traffic communication system that includes a base station equipped with a communicator and a controller. The controller identifies traveling behaviors of vehicles by receiving information via wireless communication, performs statistical learning or machine learning on these behaviors, and determines the presence or absence of traffic obstacles and their positions on the road.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual vehicles perform obstacle-avoidance behavior determination using pattern matching, then the presence of traffic obstacles can be detected, but the processing load on individual vehicles increases
Solution Approach 1:
The patent introduces a base station as an intermediary device that performs the complex pattern matching and obstacle detection processing. Instead of each vehicle independently analyzing traffic patterns, the base station receives travel route information from multiple vehicles and centrally determines obstacle presence, thereby reducing the processing burden on individual vehicles while maintaining detection accuracy
Solution Approach 2:
The patent replaces the mechanical processing approach (each vehicle independently performing pattern matching) with an information-based system where vehicles simply transmit their travel route data to the base station, which then performs the complex analysis using statistical learning or machine learning algorithms
2Reliability
If vehicles perform pattern matching to determine traffic obstacles, then obstacle presence can be identified, but false determination increases when vehicles perform obstacle-avoidance behaviors without actual obstacles
Solution Approach 1:
The base station receives travel route information from multiple vehicles over time and uses this feedback data to perform statistical learning or machine learning analysis. By analyzing patterns across multiple vehicles and time periods, the system can distinguish between legitimate obstacle avoidance behaviors and actual obstacles, reducing false determinations
Solution Approach 2:
The patent changes the approach from binary pattern matching to probabilistic statistical learning or machine learning models. This allows the system to handle ambiguity in vehicle behaviors by calculating probabilities of obstacle presence based on multiple parameters including travel routes, timing, and spatial relationships, thereby reducing false positive rates
Data Source
AI summary
A base station used in a traffic communication system includes a communicator configured to receive, via wireless communication, information transmitted from a vehicle traveling on a road, and a controller configured to identify, based on the information received by the communicator, a traveling behavior including a travel route of the vehicle on the road. The controller is configured to execute first processing for performing statistical learning or machine learning on a plurality of the traveling behaviors respectively identified for a plurality of the vehicles traveling on the road, and second processing for determining, based on a result of the first processing, whether a traffic obstacle is present on the road and a position of the traffic obstacle on the road.