Road Works Zone Detection with Lane Positions and Traffic Density
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Solution Overview
Problem
Existing navigation systems fail to accurately detect road works zones during high-density traffic due to blocked line of sight, leading to inaccurate navigation and potential safety issues for autonomous vehicles.
Innovation Solution
A system and method for detecting road works zones using sensors to determine traffic density and lane positions, filtering observations based on predefined thresholds, and assigning weights to observations to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicles in outer lanes observe visual indicators of road works, then road works detection accuracy improves, but during high density traffic this observation is blocked by vehicles in other lanes
Solution Approach 1:
The patent introduces an intermediary mechanism by using vehicles in adjacent lanes as mediators to detect road works objects that are blocked from direct observation. When a vehicle in an outer lane cannot directly observe road works indicators due to blocking, the system uses observations from vehicles in adjacent lanes (particularly those in lanes closer to the road works) to infer and detect the road works zone. This intermediary approach allows indirect detection through neighboring vehicles' sensor data.
2Device complexity
If navigation systems rely on direct visual observation of road works indicators, then detection simplicity is maintained, but detection reliability fails in high density traffic conditions
Solution Approach 1:
The patent merges multiple data sources and observation methods to improve reliability. It combines direct visual observations from the vehicle's own sensors with observations from neighboring vehicles' sensors. The system integrates data from multiple lanes and multiple vehicles to create a more reliable detection system that overcomes the limitations of single-vehicle direct observation in high density traffic conditions.
Solution Approach 2:
The system implements feedback mechanisms by continuously receiving and processing observations from multiple vehicles, comparing detected road works objects across different lanes and time periods. This feedback loop allows the system to validate detections, reduce false positives, and improve overall detection reliability through collective intelligence from the vehicle fleet.
3Measurement precision
If road works detection uses data from multiple lanes and vehicles, then detection accuracy in high density traffic improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the detection problem into manageable components: processing observations from individual vehicles, grouping them by lane, and then aggregating lane-level data. This segmented approach allows the system to handle complex multi-vehicle data through a structured hierarchy, reducing computational complexity while maintaining detection accuracy.
Data Source
AI summary
The disclosure provides a system, a method, and a computer program product for updating map data. The system, for example, receives, from one or more user equipment, at least one road works observation. The at least one road works observation is associated with a road works object. Further, the system, determines a first lane and a second lane. Further, the first lane is associated with the at least one road works observation and the second lane is associated with the road works object. Further, the system determines traffic density associated with a region in vicinity of the road works object. Further, the system detects the road works zone based on the first lane, the second lane and the traffic density.