Vehicle Trajectory Deadlock Classification in Constrained Traffic Areas
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Solution Overview
Problem
Planning trajectories for heavy-duty vehicles to avoid traffic deadlocks in constrained areas is challenging, especially for autonomous or semi-autonomous vehicles, as they require complex motion support devices and can be difficult to extricate from deadlock situations, necessitating special skills and equipment.
Innovation Solution
A computer-implemented method assesses vehicle trajectories using a connected directed graph to identify potential deadlocks by updating vehicle states iteratively, classifying explicit and implicit deadlocks, and storing them in memory to inform traffic planning and site design, allowing for proactive avoidance of deadlocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If multiple heavy-duty vehicles operate autonomously or semi-autonomously in a site with traffic-constrained areas, then vehicle automation and operational flexibility are improved, but the risk of traffic deadlocks increases and trajectory planning becomes more complex
Solution Approach 1:
The patent applies preliminary action by predicting potential deadlock states before vehicles actually enter them. The system iteratively updates vehicle states and classifies explicit and implicit deadlock states in advance, allowing the trajectory planner to avoid these states proactively rather than reacting after deadlocks occur. This resolves the contradiction by enabling automated operation while simplifying planning through preemptive deadlock avoidance.
Solution Approach 2:
The patent implements feedback by using the classified deadlock states to continuously improve trajectory planning. The system stores explicit and implicit deadlock states and uses this information to adjust and optimize vehicle trajectories, creating a closed-loop system where planning decisions are refined based on predicted deadlock scenarios. This feedback mechanism enables high-level automation while managing planning complexity through learned patterns.
2Productivity
If vehicles follow planned trajectories in traffic-constrained areas, then operational efficiency is improved, but the likelihood of encountering deadlocks increases
Solution Approach 1:
The patent applies preliminary anti-action by taking measures to prevent deadlocks before they occur. The system identifies explicit and implicit deadlock states and uses this information to modify trajectories in advance, counteracting the tendency of fixed trajectory following to cause deadlocks. This allows vehicles to maintain high operational efficiency while proactively preventing reliability issues related to deadlocks.
Solution Approach 2:
The patent implements dynamics by making trajectory planning adaptive rather than static. The system dynamically adjusts trajectories based on predicted deadlock states, allowing vehicles to deviate from planned paths when necessary to avoid deadlocks. This dynamic approach maintains productivity by minimizing disruptions while improving reliability through flexible deadlock avoidance.
3Measurement precision
If explicit and implicit deadlock states are classified and stored, then deadlock prediction accuracy is improved, but computational requirements and memory usage increase
Solution Approach 1:
The patent applies taking out by separating explicit and implicit deadlock states into distinct classifications. The system extracts and stores only the essential deadlock state information needed for prediction, rather than storing all possible trajectory combinations. This extraction approach improves prediction accuracy by focusing on critical deadlock patterns while reducing memory requirements by storing only necessary state classifications.
4Reliability
If vehicle trajectories are adapted to avoid deadlocks, then operational reliability is improved, but trajectory optimization and site design complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-classifying deadlock states before trajectory optimization. The system identifies explicit and implicit deadlock states in advance and uses this pre-computed information to guide trajectory adjustments, rather than performing complex optimization during real-time operation. This approach improves reliability through proactive deadlock avoidance while reducing the computational complexity of real-time trajectory optimization.
Data Source
AI summary
A computer-implemented method of assessing vehicle trajectories for deadlock scenarios in a site where multiple vehicles operate by following planned vehicle trajectories is described. The site includes at least one traffic-constrained area via which at least two of the multiple vehicles passes when following their planned trajectories.