Roadway Sensor Nodes for AV Path Adjustment in Construction Zones
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Solution Overview
Problem
Autonomous vehicles struggle to adjust their travel patterns efficiently in construction zones due to the heavy computational load required for processing conventional traffic markers, which complicates the determination of road accessibility.
Innovation Solution
A system of sensor nodes positioned on the roadway, coupled with an autonomous vehicle computing device, detects and processes positional data from these nodes to generate and adjust travel patterns without complex map comparisons, allowing for efficient navigation around construction zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional traffic markers are used to indicate construction zones, then drivers can easily identify and adjust their travel patterns, but autonomous vehicles face heavy computational loads in processing these markers
Solution Approach 1:
The patent introduces sensor nodes as intermediary devices deployed along the roadway that actively communicate travel pattern adjustment information to autonomous vehicles. These sensor nodes serve as mediators between the construction zone infrastructure and the autonomous vehicle's navigation system, translating physical road conditions into structured digital signals that the vehicle can process efficiently without heavy computational burden
Solution Approach 2:
The patent replaces the passive mechanical/visual traffic marker system with an active electronic sensing and communication system. Instead of relying on autonomous vehicles to detect and interpret physical traffic markers through complex image processing and spatial reasoning, the system uses sensor nodes with transponders that electronically transmit navigation information directly to the vehicle's computing device, substituting mechanical detection with electronic signal processing
2Measurement precision
If autonomous vehicles process conventional traffic markers to determine road accessibility, then accurate navigation around construction zones is achieved, but processing time and computational resources are significantly increased
Solution Approach 1:
The sensor nodes are pre-deployed along the roadway at known locations with predetermined spacing, establishing a ready-made detection framework before autonomous vehicles arrive. The sensor nodes continuously monitor and maintain their operational status, so when an autonomous vehicle encounters a sensor node, the information processing infrastructure is already in place, eliminating the need for the vehicle to perform complex real-time reconstruction of traffic marker patterns
Solution Approach 2:
The sensor nodes create simplified digital representations of the physical road conditions and travel pattern restrictions. Instead of processing complex visual data from multiple traffic markers to reconstruct the road layout, the autonomous vehicle receives pre-processed information from sensor nodes that directly encodes the accessibility status and recommended travel patterns, effectively copying the essential navigation information in a computationally efficient format
Data Source
AI summary
Systems for adjusting travel patterns for autonomous vehicles are disclosed. The system includes a plurality of sensor nodes positioned on a roadway., where each sensor node is operably coupled to at least one distinct sensor node. The system also includes an autonomous vehicle (AV) computing device(s) in electronic communication with the plurality of sensor nodes. The AV computing device(s) adjusts travel patterns for an autonomous vehicle on the roadway by detecting a first sensor node of the plurality of sensor nodes and obtaining positional data for the first sensor node and each subsequent sensor node. The computing device(s) then generate a modified travel pattern for the autonomous vehicle based on positional data for each sensor node, and/or accessibility status for each sensor node. Additionally, the computing device adjusts an initial travel pattern of the autonomous vehicle on the roadway to the modified travel pattern.