Road Performance Measurement via Vehicle Headway Dynamics
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Solution Overview
Problem
Current navigation systems for autonomous vehicles are unable to assess large-scale traffic conditions and determine risk levels of roadways, leading to potential instability and safety hazards due to their limited awareness of external traffic issues.
Innovation Solution
A method and system that utilize telemetric data from vehicles to determine a disruption score for traffic flow, allowing for adjustments such as route changes, lane changes, speed modifications, and headway dynamics to mitigate risks, by accumulating and analyzing data from vehicles traversing a road segment and comparing it to a stable traffic profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If autonomous vehicles rely only on immediate surroundings monitoring, then the vehicle's local navigation capability is maintained, but the vehicle cannot assess large-scale traffic conditions and risk levels
Solution Approach 1:
The patent introduces a remote server as an intermediary that collects, processes, and analyzes telemetric data from multiple vehicles. This mediator handles the complex task of assessing large-scale traffic conditions and computing disruption scores, allowing individual vehicles to benefit from comprehensive traffic awareness without bearing the full computational burden themselves.
Solution Approach 2:
The system merges data from multiple vehicle sources to create a collective understanding of traffic conditions. By combining telemetric data from numerous vehicles traversing the same road segments, the system builds a comprehensive view of traffic flow patterns and disruptions that no single vehicle could obtain alone.
2Measurement precision
If the system collects and analyzes telemetric data from multiple vehicles, then traffic flow disruption detection accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing telemetric data in databases before disruption events occur. Road segment profiles and historical traffic patterns are pre-computed and stored, enabling rapid disruption detection when needed without requiring intensive real-time processing of all raw data.
Solution Approach 2:
The system extracts only the essential features and metrics needed for disruption detection from the full telemetric data set. By identifying and extracting key parameters such as headway dynamics and traffic flow characteristics, the system achieves accurate disruption detection while minimizing processing time and computational resources.
3Reliability
If the system provides real-time traffic disruption information to vehicles, then vehicle safety and mobility are improved, but the system requires continuous data collection and processing
Solution Approach 1:
Vehicles contribute their own telemetric data to the collective system, performing part of the data collection effort themselves. Each vehicle's sensors and onboard systems generate the raw data that feeds into the disruption detection system, reducing the need for dedicated infrastructure-based data collection and distributing the energy burden across all participating vehicles.
Data Source
AI summary
A system, method and server for a controlling a relation between a vehicle and traffic flow over a road segment. The system includes a telemetric device of the vehicle and a processor of the server. The telemetric device obtains telemetric data related to the road segment being traversed by the vehicle. The processor determines a property of the road segment based on the telemetric data and a road profile for the road segment, determines a disruption score indicative of a level of disruption in the traffic flow for the road segment based on the property, and outputs a notification signal when the disruption score is above a selected disruption threshold. The notification signal is usable for controlling the relation between the vehicle and the traffic flow.