Vehicle Marshaling Wireless KPI Monitoring for Disruption Response
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Solution Overview
Problem
Wireless communication systems in autonomous vehicles face challenges such as network congestion, packet delays, interference, and signal degradation, making it difficult to diagnose and address communication-related issues proactively during vehicle marshaling.
Innovation Solution
A system and method for calculating key performance indicators (KPIs) such as packet error rate, latency, and data rate, detecting communication disruptions, and initiating remedial actions like marshaling commands or generating a radio-frequency coverage heat map to enhance communication reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless communication is used for vehicle marshaling, then system complexity is reduced and ease of operation is improved, but communication reliability deteriorates due to network congestion, packet delays, interference, and signal degradation
Solution Approach 1:
The system continuously monitors communication quality metrics (packet error rate, latency, signal strength) and uses this feedback to dynamically adjust transmission parameters, select optimal communication channels, and trigger remedial actions when disruptions are detected, thereby maintaining reliable communication despite wireless interference and congestion
Solution Approach 2:
The system performs preliminary assessments of communication quality before critical failures occur by continuously measuring KPIs and detecting early signs of disruption. Remedial actions are prepared and can be initiated proactively before complete communication failure, preventing marshaling operations from being compromised
2Reliability
If communication disruptions are detected and remedial actions are initiated, then communication reliability is improved, but device complexity increases due to additional monitoring and control mechanisms
Solution Approach 1:
The vehicle and infrastructure system autonomously monitor their own communication quality and automatically initiate remedial actions without external intervention. The system self-diagnoses communication disruptions and self-corrects by adjusting transmission parameters or switching channels, reducing the need for complex external monitoring infrastructure
Solution Approach 2:
The communication monitoring and control functions are segmented into distinct modular components: KPI measurement modules, disruption detection modules, and remedial action initiation modules. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive communication reliability
3Measurement precision
If real-time communication monitoring is implemented, then measurement precision of communication quality is improved, but loss of time increases due to additional processing requirements
Solution Approach 1:
Communication quality metrics are monitored at optimized periodic intervals rather than continuously, balancing measurement precision with processing efficiency. The system adjusts the monitoring frequency based on communication conditions, increasing precision when disruptions are detected while maintaining lower overhead during stable periods
Solution Approach 2:
The system monitors only the most critical communication KPIs (packet error rate, latency, signal strength) with high precision while using less intensive monitoring for secondary parameters. This selective partial monitoring achieves sufficient measurement precision for reliable disruption detection without the time cost of comprehensive continuous monitoring of all possible parameters
Data Source
AI summary
A method includes the calculation of at least one metric corresponding to one or more key performance indicators associated with one or more messages exchanged between a vehicle and an infrastructure system, the detection of one or more communication-based disruptions associated with the one or more messages based on an analysis of the at least one metric, and the initiation of a remedial action based on the one or more communication-based disruptions and an adjustment to one or more marshaling commands.