Late Lane Change Prediction for Navigation Route Mitigation
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Solution Overview
Problem
Late lane changes in traffic networks can disrupt traffic flow, cause collisions, and increase congestion, as existing navigation systems lack the capability to predict and mitigate such incidents effectively.
Innovation Solution
A method and system that utilize probe data to identify late lane changes, generate feature descriptions, and train a late lane change model to predict such events. These predictions are then used to update digital maps, provide notifications, and adjust navigation routes to reduce the occurrence and impact of late lane changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing navigation systems are used, then basic navigation functions are provided, but they lack the capability to predict and mitigate late lane changes effectively
Solution Approach 1:
The system performs preliminary actions by training a machine learning model on historical probe data to predict late lane changes before they occur. The model analyzes patterns from past lane change events and prepares predictions in advance, enabling proactive mitigation strategies rather than reactive responses.
Solution Approach 2:
A machine learning model serves as an intermediary between raw probe data and navigation decisions. The model processes and interprets complex traffic patterns, translating them into actionable predictions that the navigation system can use to mitigate late lane changes.
2Object-affected harmful factors
If late lane changes are not predicted, then traffic flow continues uninterrupted by sudden maneuvers, but collisions and congestion increase due to lack of awareness
Solution Approach 1:
The system implements feedback by continuously monitoring probe data from vehicles, analyzing lane change patterns, and using this information to improve future predictions. The model learns from actual lane change events and adjusts its predictions accordingly, creating a closed-loop system that reduces harmful traffic disruptions over time.
Solution Approach 2:
By predicting late lane changes in advance, the system allows following vehicles to take preliminary actions such as adjusting speed or changing lanes proactively, preventing collisions and reducing congestion before they occur.
3Measurement precision
If probe data is collected and analyzed to train prediction models, then accurate late lane change predictions are achieved, but data processing requirements and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features from probe data for model training, such as lane change timing, location, speed, and environmental conditions. By focusing on key predictive variables rather than processing all raw data, the system maintains high prediction accuracy while reducing computational burden.
Solution Approach 2:
The model uses a subset of available probe data that is most representative of late lane change patterns. Rather than requiring complete data from all vehicles, the system achieves sufficient prediction accuracy through selective data sampling and feature extraction.
Data Source
AI summary
A network apparatus, for example, obtains probe data corresponding to a respective vehicle making a late lane change while traversing a TME of a traversable network. Location information indicating a location of the late lane change is extracted from the probe data. Map, weather, and/or traffic data for the location is obtained. A late lane change feature description is generated based on information extracted from the probe data and the map, weather, and/or traffic data. A model is trained using a machine learning technique and training data comprising the late lane change feature description. The model is executed to generate a late lane change prediction corresponding to a TME of a digital map. The network apparatus causes at least one of (a) the digital map to be updated, (b) traffic data corresponding to the TME to be updated, or (c) a navigation-related function to be performed based on the prediction.