Edge Computing Intersection Warning System for VRU Trajectory Prediction
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Solution Overview
Problem
Conventional intersection warning systems for vulnerable road users (VRUs) lack comprehensive 360° analysis and computational capacity to predict trajectories and paths of vehicles and VRUs, leading to missed warnings and false positives, which can diminish trust and safety.
Innovation Solution
An intersection infrastructure warning system utilizing an edge computing device with a GPU, sensors, and warning devices like drones or holosonic speakers, processing spatial-temporal data with AI algorithms to predict paths, trajectories, and behaviors of road users, and deploy targeted warnings to VRUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional intersection infrastructure uses basic V2I communication and SMS warnings, then the system can provide some warning coverage, but the warning accuracy and reliability are insufficient leading to false positives and missed warnings
Solution Approach 1:
The patent introduces an edge computing device as an intermediary between sensors and warning devices. This intermediary processes spatial-temporal data using AI algorithms to predict trajectories and identify conflict zones, thereby improving both the reliability and precision of warnings without requiring direct complex connections between all system components
Solution Approach 2:
The patent replaces basic V2I communication and SMS systems with an AI-based predictive analytics system. The GPU-accelerated machine learning models substitute traditional rule-based warning mechanisms, enabling more accurate trajectory prediction and conflict zone identification while maintaining system scalability
2Measurement precision
If the system implements comprehensive 360° spatial-temporal data analysis with AI algorithms, then the accuracy of trajectory prediction improves, but the computational complexity and processing requirements increase significantly
Solution Approach 1:
The patent segments the computational workload by deploying edge computing devices at strategic locations around the intersection. Each edge device processes data for specific zones, breaking down the complex 360° analysis into manageable regional computations that can be executed in parallel, thereby reducing individual device complexity while maintaining overall system precision
Solution Approach 2:
The edge computing device acts as an intermediary that handles the computational complexity of AI algorithms and trajectory predictions. This intermediary absorbs the processing burden, allowing the overall system to achieve high prediction precision without requiring every component to be computationally complex
3Loss of time
If conventional systems provide general warnings to all road users, then the system coverage is broad, but the timeliness and relevance of warnings are reduced causing delays and false alarms
Solution Approach 1:
The patent applies local quality by providing customized warnings to specific vulnerable road users based on their individual trajectories, locations, and risk levels. Instead of uniform general warnings, the system analyzes spatial-temporal data to deliver targeted alerts only to those in conflict zones, improving both timeliness and relevance while reducing information loss
4Measurement precision
If the system uses multiple sensors and AI processing to identify conflict zones, then the detection accuracy improves, but the system cost and infrastructure requirements increase
Solution Approach 1:
The patent implements multi-functionality by designing edge computing devices that perform multiple functions: collecting data from various sensors, processing spatial-temporal information, running AI trajectory predictions, identifying conflict zones, and triggering warnings. This universal approach consolidates infrastructure requirements while maintaining high detection accuracy through integrated multi-functional processing
Data Source
AI summary
A warning system for warning vulnerable road users (VRUs) includes an edge computing device having a graphics processing unit (GPU), one or more sensors that acquire spatial-temporal data of road users, and one or more warning devices that output a warning to warn targeted VRUs of danger. The GPU uses one or more artificial intelligence (AI) algorithms to process the spatial-temporal data of the road users to predict a path, trajectory, behavior, and intent for the road users. The GPU then analyzes the predictions of the road users to determine convergences between the predictions to determine threat interactions and identify targeted VRUs. In response to determining threat interactions and identifying the targeted VRUs, the edge computing device outputs targeting instructions and warning response instructions to the one or more warning devices to deploy the one or more warning devices to warn the targeted VRUs.