Autonomous Vehicle Traffic Planning with Gap-Balancing Control
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Solution Overview
Problem
Centralized vehicle control systems often lead to vehicles blocking each other, resulting in queue formation and deadlock states, which reduce system productivity and require external intervention.
Innovation Solution
A traffic planning method that optimizes a state-action value function with a command-independent term penalizing small inter-vehicle gaps, using a gap-balancing indicator to promote even gap distribution, reducing the likelihood of blocking states and improving system efficiency with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized control is used to maximize productivity, then system output increases, but vehicles may block each other forming queues and deadlocks
Solution Approach 1:
The patent applies preliminary action by introducing a command-independent term in the objective function that penalizes small inter-vehicle gaps before deadlocks occur. This predictive mechanism proactively prevents blocking states by maintaining adequate spacing between vehicles, rather than reacting after conflicts arise. The gap-balancing indicator calculates and adjusts commands to ensure minimum safe distances are maintained throughout the planning horizon.
2Productivity
If traditional optimization methods are used to determine motion commands, then productivity is maximized, but computational resources are heavily consumed
Solution Approach 1:
The patent segments the objective function into two distinct parts: a command-independent term that penalizes small inter-vehicle gaps, and a command-dependent term that optimizes productivity. This segmentation allows the system to handle gap balancing separately from productivity optimization, reducing the computational complexity of the overall optimization problem while maintaining both safety and efficiency goals.
Solution Approach 2:
The patent changes the parameter structure by introducing a gap-balancing indicator that transforms the optimization landscape. By reformulating the objective function to include this indicator, the system achieves better convergence properties and reduces computational resource consumption while still maximizing productivity through the command-dependent term.
3Speed
If vehicles are allowed to move freely to maximize speed, then individual vehicle efficiency increases, but queue formation and deadlocks occur
Solution Approach 1:
The patent implements feedback by continuously monitoring inter-vehicle gaps and using the gap-balancing indicator to adjust motion commands. The objective function provides real-time feedback on spacing conditions, allowing the system to dynamically modify vehicle speeds and trajectories to prevent queue formation while maintaining overall system efficiency and avoiding deadlocks.
Data Source
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AI summary
A traffic planning method for controlling a plurality of vehicles (v1, v2, v3, v4), wherein each vehicle occupies one node in a shared set of planning nodes (wp1, wp2, wp3, wp4, wp5, wp6, wp7, wp8) and is movable to other nodes along predefined edges between pairs of the nodes in accordance with a finite set of motion commands. In the method, initial node occupancies of the vehicles are obtained, and a sequence of motion commands is determined by optimizing a state-action value function Q(s, a) = Qs (s, a) + QL (s) which depends on node occupancies s and the motion commands a to be given. The objective function includes a command-dependent term Qs (s, a), which may be updated in each iteration based on a reward function, and a command-independent term QL (s), which penalizes node occupancies with too small inter-vehicle gaps.