Lane-Level Congestion Detection for Vehicle Navigation Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle navigation systems lack the capability to identify lane-level road congestion, relying on general road network congestion assessments that fail to provide high-fidelity lane traffic distribution information.
Innovation Solution
A method is developed to collect vehicle data from a fleet, identify lane-level vehicle distribution, compare it against non-congested lane profiles, and predict lane-level congestion. This involves detecting lead actor vehicles, adjusting deceleration profiles, and performing lane-level load balancing to mitigate congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general road network congestion assessment is used, then system complexity is reduced, but measurement precision of lane-level traffic distribution is insufficient
Solution Approach 1:
The patent segments the road network into individual lanes and further into lane segments, collecting vehicle data at each segment level. This segmentation enables lane-level congestion identification by analyzing vehicle distribution across multiple lanes and segments, transforming the overall road congestion problem into manageable lane-specific assessments.
Solution Approach 2:
The patent introduces a new dimension of analysis by examining vehicle distribution across the lateral dimension (multiple lanes) in addition to the longitudinal dimension. By collecting vehicle position data and determining lane-level distribution, the system adds spatial granularity to congestion assessment, enabling identification of congestion in specific lanes rather than treating the entire road as a single entity.
2Measurement precision
If lane-level congestion identification is implemented, then navigation accuracy is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The patent performs preliminary actions by continuously collecting vehicle data from fleet vehicles and pre-processing this data to identify lane-level distribution patterns. By maintaining a database of vehicle positions and lane assignments, the system prepares congestion identification data in advance, reducing the time required for real-time congestion assessment when navigation decisions are needed.
Solution Approach 2:
The patent implements feedback mechanisms where congestion identification results are continuously updated and fed back into the navigation system. By monitoring lane-level vehicle distribution over time and comparing current states with historical data, the system可以快速识别 congestion patterns without requiring extensive real-time processing, thus reducing time loss while maintaining high accuracy.
3Adaptability or versatility
If multiple lane route options are created, then adaptability of navigation system is improved, but device complexity increases
Solution Approach 1:
The patent creates dynamic lane route options that adapt to real-time congestion conditions. By continuously monitoring lane-level vehicle distribution and updating route recommendations based on current congestion status, the system provides flexible navigation alternatives. This dynamic approach allows the navigation system to adjust route options on-the-fly, offering multiple lane choices when congestion is detected in the current lane while maintaining manageable complexity through algorithmic route generation.
Data Source
AI summary
A method to incorporate lane level road congestion to enhance vehicle navigation includes: collecting vehicle data from a fleet of vehicles; identifying a lane level distribution of multiple vehicles operating on a road having multiple lanes including a host vehicle; comparing the lane level distribution against a non-congested lane of the road; and identifying if a lane level congestion is occurring in at least one of the multiple lanes.