Vehicle Trajectory Deadlock Classification in Constrained Site Traffic
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Solution Overview
Problem
Heavy-duty vehicles face challenges in navigating traffic deadlocks, especially in areas with traffic constraints, which can lead to inefficiencies and operational complexities when planning trajectories for autonomous or semi-autonomous fleets.
Innovation Solution
A computer-implemented method and traffic planner that assesses vehicle trajectories to predict and prevent deadlocks by iteratively updating vehicle states and adjusting trajectories to avoid traffic-constrained areas, using classification memories to identify and plan around potential deadlocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple heavy-duty vehicles operate autonomously in the same area following planned trajectories, then vehicle fleet productivity is improved, but the risk of traffic deadlocks increases due to constrained movement paths and limited maneuverability
Solution Approach 1:
The system performs preliminary classification of trajectory states to identify potential deadlock situations before they occur. By assessing vehicle trajectories in advance and classifying them as deadlock-prone or safe, the system can proactively adjust paths or schedules to prevent deadlocks, thereby maintaining both high productivity and reliability.
Solution Approach 2:
The system continuously monitors vehicle positions and trajectory执行情况, comparing actual states against classified deadlock scenarios. When potential deadlock conditions are detected, the system provides feedback to adjust vehicle paths or speeds, creating a closed-loop control system that maintains fleet productivity while preventing deadlocks through real-time adaptation.
2Productivity
If vehicle trajectories are planned to maximize throughput in traffic-constrained areas, then site productivity is improved, but the complexity of trajectory planning and coordination increases
Solution Approach 1:
The system segments the trajectory planning problem by classifying different trajectory states independently as deadlock-prone or safe. This segmentation allows the complex coordination problem to be broken down into manageable classification tasks for individual vehicle paths, reducing overall planning complexity while maintaining high throughput in constrained areas.
Solution Approach 2:
The system changes the approach to trajectory planning by introducing classification parameters that identify deadlock risks. Instead of solving the full coordination problem directly, the system uses classification states as additional parameters to simplify trajectory selection and adjustment, thereby reducing planning complexity while maximizing productivity.
3Manufacturing precision
If heavy-duty vehicles are equipped with controllable motion support devices for precise trajectory following, then trajectory accuracy is improved, but the complexity of vehicle control systems increases
Solution Approach 1:
The system enables vehicles to self-adjust their trajectory following by classifying their current state and autonomously selecting appropriate control actions. The motion support devices are controlled based on the classified trajectory state, allowing vehicles to self-correct without complex external coordination, thereby maintaining high trajectory accuracy while managing control system complexity.
Data Source
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AI summary
The disclosure relates to a computer-implemented method of assessing vehicle trajectories for deadlock scenarios in a site where multiple vehicles operate by following planned vehicle trajectories (A-G). The site includes at least one traffic-constrained area (SL) via which at least two of the multiple vehicles passes when following their planned trajectories. The disclosure also relates to a traffic planner for planning a plurality of planned vehicle trajectories for vehicles within a site having at least one traffic-constrained location.