Traffic Analysis Server Anomaly Detection via Historical Pattern Comparison
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Solution Overview
Problem
Current vehicle systems rely on sensor data for operation and safety, but there is a lack of effective monitoring and analysis of vehicle traffic data to detect anomalies such as malicious hacks or malfunctions, which can compromise vehicle safety and traffic flow.
Innovation Solution
A system comprising vehicles, infrastructure sensors, and a traffic analysis server that collects and analyzes vehicle and infrastructure data to identify anomalies by comparing present traffic patterns with historical data, using machine learning algorithms to determine anomalies and potentially take action such as notifying authorities or disabling vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle systems rely on sensor data for operation and safety, then vehicle operation and safety are enabled, but there is a lack of effective monitoring and analysis to detect anomalies such as malicious hacks or malfunctions
Solution Approach 1:
The patent implements a feedback mechanism where traffic data from multiple vehicles is continuously collected, analyzed, and used to generate anomaly detections that feed back into the system. The server compares present traffic patterns with historical data and provides feedback about detected anomalies to relevant vehicles and authorities, creating a closed-loop system that improves safety through continuous monitoring and response.
Solution Approach 2:
The patent introduces a traffic analysis server as an intermediary between vehicles and authorities. This intermediary collects data from multiple sources, performs centralized analysis using machine learning algorithms, and coordinates responses to anomalies. The server acts as a mediator that aggregates information from individual vehicles and translates it into actionable intelligence for safety responses.
2Measurement precision
If a traffic analysis server collects and analyzes vehicle and infrastructure data to identify anomalies, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The traffic analysis server performs multiple functions within a single system: collecting data from vehicles and infrastructure sensors, storing historical traffic patterns, analyzing present traffic behavior, detecting anomalies, and coordinating responses. This multi-functional approach consolidates what could be separate complex systems into a unified platform, improving measurement precision while managing overall system complexity.
Solution Approach 2:
The system employs machine learning algorithms that automatically learn from historical traffic data and improve anomaly detection capabilities without requiring manual programming for each scenario. The algorithms self-adjust and refine their detection precision over time by processing accumulated data, reducing the need for continuous manual system configuration and maintenance.
3Speed
If the system analyzes present traffic behavior compared to historical patterns in real-time, then anomaly detection speed is improved, but computational resources required increase
Solution Approach 1:
The system pre-processes and stores historical traffic patterns in organized structures before they are needed for comparison. By preparing reference data in advance and organizing it for efficient retrieval, the system enables faster real-time comparisons without requiring all computational resources to be available during the actual anomaly detection moment, thus balancing speed with resource consumption.
Data Source
AI summary
Technologies for monitoring vehicle traffic include a traffic analysis server that receives infrastructure data from infrastructure sensors positioned along a road segment of a road and vehicle data from one or more vehicles travelling along the road segment. The traffic analysis server determines whether anomalies are present in the traffic data through the road segment based on an expected traffic behavior for the road segment. The traffic analysis server determines the expected traffic behavior for the road segment in a particular time window based on a historical traffic pattern associated with the road segment, based on historical vehicle data and historical infrastructure data captured during a prior time window corresponding to the particular time window for that road segment. Other embodiments are described and claimed.