Spatial-Temporal Grid Mapping for Traffic Stream Congestion
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Solution Overview
Problem
Current navigation systems for autonomous vehicles are unable to assess large-scale traffic conditions and determine the risk level of roadways, leading to potential instability and hindered vehicle movement due to congestion.
Innovation Solution
A system that utilizes telemetric data from multiple vehicles to identify and quantify congestion by mapping normalized road segments onto a two-dimensional spatial-temporal grid, calculating congestion metrics, and providing output signals for navigation adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If autonomous vehicles monitor only their immediate surroundings, then the vehicle's immediate safety is maintained, but the vehicle cannot assess large-scale traffic conditions and predict congestion risks
Solution Approach 1:
A server acts as an intermediary between vehicles and traffic condition data. The server aggregates telemetric data from multiple vehicles, processes it to identify congested traffic streams, and provides processed information back to vehicles. This mediator approach allows vehicles to access large-scale traffic information without each vehicle needing complex processing capabilities.
Solution Approach 2:
The traffic stream is divided into discrete road segments that are individually analyzed for congestion conditions. Each road segment's telemetric data is processed separately to determine congestion metrics, allowing the system to manage large-scale traffic analysis through manageable segments rather than treating the entire traffic network as a single complex system.
2Measurement precision
If the system processes telemetric data from multiple vehicles to identify congestion, then traffic condition assessment accuracy improves, but computational requirements and system complexity increase
Solution Approach 1:
The system focuses on processing only the essential telemetric parameters needed for congestion detection (such as vehicle speed, spacing, and density) rather than processing all possible vehicle data. This partial action approach achieves sufficient congestion detection accuracy without requiring excessive computational resources.
Solution Approach 2:
The system uses telemetric data that vehicles already generate and transmit for other purposes (such as navigation and safety monitoring). By utilizing this existing data stream for congestion analysis, the system avoids the need for dedicated sensors or data collection infrastructure, reducing overall system complexity while maintaining detection accuracy.
3Productivity
If the system provides detailed congestion information to vehicles, then navigation decision quality improves, but information processing time and communication bandwidth increase
Solution Approach 1:
The server pre-processes telemetric data and identifies congested traffic streams before vehicles need this information for navigation decisions. By having congestion information ready in advance and maintaining updated congestion maps, the system provides timely navigation guidance without requiring vehicles to perform time-consuming real-time analysis when making decisions.
Solution Approach 2:
The system transforms raw telemetric parameters into simplified congestion metrics and binary congestion status indicators (congested vs. not congested). This parameter transformation reduces the complexity of information that needs to be communicated to vehicles while retaining the essential decision-making parameters needed for navigation.
Data Source
AI summary
A system and method of identifying and quantifying congestion within a traffic stream including obtaining telemetric data from a plurality of vehicles traveling within a plurality of normalized road segments, determining a property of each normalized road segment based on the telemetric data and a road profile for each road segment, determining a disruption score indicative of a level of disruption in a traffic flow within each road segment, mapping the road segments within a two-dimensional spatial-temporal grid of cells, wherein each cell represents a normalized road segment at a specified time, for each cell, determining if the traffic flow is congested, identifying a congested traffic stream including a plurality of contiguous cells that have congested traffic flow, quantifying the congested traffic stream, and providing an output signal.