Multi-Vehicle Right-of-Way Planning via Slack Nonlinear Optimization
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Solution Overview
Problem
Existing methods for multi-vehicle right-of-way planning in complex scenarios are computationally intractable and often yield suboptimal results, leading to inefficiencies and resource waste, while existing graph theory-based methods are inadequate for comprehensive scenario coverage.
Innovation Solution
A driving priority assignment method that involves calculating time limits for potential collision points, constructing a slack nonlinear programming problem, and using iterative solutions to optimize vehicle driving priorities and reference velocities, reducing computational burden while maintaining optimal results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mixed-integer optimization problem is used to obtain optimal vehicle driving priorities, then optimal right-of-way allocation is achieved, but computational complexity becomes intractable and computation time becomes prohibitive
Solution Approach 1:
The patent segments the optimization problem by introducing a two-stage approach: first solving a relaxed continuous optimization problem to obtain initial priority values, then performing iterative integer rounding to achieve optimal discrete priorities. This segmentation transforms the intractable single-step mixed-integer optimization into manageable sequential steps, reducing computational time while maintaining optimality.
Solution Approach 2:
The patent changes the parameter type from discrete integer priorities directly to continuous values through relaxation, allowing the use of efficient continuous optimization algorithms. The continuous relaxation parameter enables gradient-based methods to operate, and the solution is subsequently rounded to integers, effectively changing the optimization landscape from discrete to continuous for computational efficiency.
2Loss of time
If FCFS or heuristic algorithms are used to reduce computation time, then computational overhead is reduced, but optimality of right-of-way allocation is sacrificed
Solution Approach 1:
The patent implements feedback through iterative optimization where the continuous relaxation solution is used to guide the integer optimization process. The iterative algorithm continuously refines the priority assignment by comparing continuous and discrete solutions, using the feedback from each iteration to converge toward the optimal integer solution, thereby maintaining optimality while reducing computation time.
Solution Approach 2:
The patent performs preliminary action by solving the continuous relaxation problem first to obtain initial priority values and timing information. This preliminary continuous solution provides a warm start for the subsequent integer optimization, reducing the search space and computational effort required to achieve the optimal discrete solution.
3Reliability
If graph theory-based collision relationship graphs are constructed for complex scenarios, then collision detection is improved, but device complexity and computational burden increase significantly
Solution Approach 1:
The patent extracts the essential collision detection function from the complex graph theory framework. Instead of constructing comprehensive collision relationship graphs for all possible scenarios, the method extracts only the critical time and position information needed for priority determination, eliminating unnecessary graph construction complexity while maintaining collision detection reliability.
Data Source
AI summary
The provided is a driving priority assignment and reference velocity planning method for multi-vehicle cooperation. The driving priority assignment method includes: obtaining driving parameter data of vehicles under multi-vehicle cooperation, and identifying potential collision points between vehicles and calculating a time limit for vehicles to drive to the potential collision points, wherein the time limit includes a minimum time and a maximum time; constructing and solving a slack nonlinear programming problem based on the time limit for the vehicles to drive to the potential collision points to obtain an initial solution result, wherein during the process of constructing the slack nonlinear programming problem, a vehicle driving priority p involved in the potential collision points and a time t when the vehicles reach the potential collision points are configured as optimization variables, and introducing a slack variable; and performing iterative solution to obtain an optimal vehicle driving priority.