Traffic Deadlock Prediction for Guided Heavy-Duty Vehicle Fleets
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Solution Overview
Problem
Planning guided trajectories for a fleet of heavy-duty vehicles is challenging due to the risk of traffic deadlocks, especially in areas with traffic constraints, where multiple vehicles may attempt to access the same location simultaneously, leading to a situation where vehicles are unable to advance.
Innovation Solution
A traffic planner system that utilizes a deadlock prediction pre-processor and a deadlock classifier model to predict and classify traffic situations, allowing for the planning of actions to avoid deadlocks along guided vehicle trajectories within a defined region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple heavy-duty vehicles follow guided trajectories through traffic constrained locations simultaneously, then vehicle productivity and operational efficiency are improved, but the risk of traffic deadlocks increases
Solution Approach 1:
The system performs preliminary deadlock prediction and classification before vehicles enter traffic constrained locations. The deadlock prediction pre-processor analyzes future traffic situations and the deadlock classifier model predicts potential deadlocks, allowing the traffic planner to adjust trajectories in advance to avoid deadlocks while maintaining productivity
Solution Approach 2:
The system implements continuous feedback by monitoring current traffic situations and using the deadlock classifier to predict future deadlock risks. This feedback loop allows dynamic adjustment of vehicle trajectories to prevent deadlocks while optimizing fleet productivity through coordinated motion planning
2Reliability
If vehicle trajectories are adjusted to avoid deadlocks in real-time, then the reliability of traffic flow is improved, but the complexity of the traffic planning system increases
Solution Approach 1:
The traffic planning system is segmented into distinct functional modules: deadlock prediction pre-processor, deadlock classifier model, and traffic planner. This segmentation allows each component to specialize in specific tasks, improving overall reliability while managing complexity through modular architecture that can be developed and maintained independently
Solution Approach 2:
The deadlock classifier model serves as an intermediary between the traffic situation data and the traffic planner. It translates complex traffic situation representations into simplified deadlock probability classifications, reducing the complexity burden on the traffic planner while maintaining high reliability in deadlock avoidance
3Productivity
If the fleet size of heavy-duty vehicles is increased to improve productivity, then operational efficiency is improved, but the difficulty of detecting and predicting deadlock situations increases
Solution Approach 1:
The system creates a virtual copy or simulation of the traffic situation through the deadlock prediction pre-processor, which models future traffic states without requiring direct observation of all possible deadlock scenarios. This allows the system to handle large fleet sizes by working with simplified representations rather than complex real-time data from every vehicle
Solution Approach 2:
The system replaces manual or simple rule-based deadlock detection with an automated deadlock classifier model that uses machine learning algorithms. This substitution enables the system to handle increased fleet sizes and complex traffic patterns that would be impossible to detect and predict using traditional mechanical or manual methods
Data Source
AI summary
A method to determine if a traffic situation comprising a plurality of heavy-duty vehicles following a plurality of guided vehicle trajectories in a defined area will lead to a vehicle deadlock is described. For example, one method includes generating a traffic situation representation for the defined area comprising, for each of the plurality of vehicles, an initial vehicle trajectory segment location and a subsequent deadlock status, inputting the traffic situation representation to a traffic situation deadlock classifier which has been trained to output a traffic situation deadlock classification based on an input representation of a traffic situation in the defined area, and, based on the input traffic situation representation, generating a predicted deadlock classification of a subsequent traffic situation for that input traffic situation representation.