Lane-Change Trajectory Planning With Multi-Weight Path Selection
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Solution Overview
Problem
Existing vehicle lane changing trajectory planning methods fail to meet the diverse needs of users, as they do not effectively provide optimal paths for different performance requirements such as comfort, time, and energy efficiency.
Innovation Solution
A vehicle lane changing trajectory planning method that determines a starting and end point on a grid lane changing map, allocates weights using a potential function, searches for optimal solutions based on different weight requirements, smooths these solutions to remove sharp corners, and selects the optimal trajectory based on a cost function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single lane changing trajectory method (e.g., constant velocity offset, arc, trapezoidal speed, or sine function) is used, then the trajectory planning is simple, but the trajectory cannot meet diverse user needs for different performance requirements
Solution Approach 1:
The patent applies dynamics by making the trajectory planning system adjustable and flexible through multiple configurable parameters (weight factors for different cost functions, grid map resolution, potential function parameters). The system can dynamically adapt to different user needs by modifying these parameters without changing the fundamental planning architecture, thus achieving versatility while maintaining manageable complexity.
Solution Approach 2:
The patent utilizes parameter changes by introducing weight factors that can be adjusted to prioritize different performance requirements (comfort, time efficiency, energy consumption). By changing these parameters, the same trajectory planning framework can generate different optimal trajectories tailored to specific user preferences, resolving the contradiction between adaptability and complexity.
2Adaptability or versatility
If multiple trajectory methods are provided to meet different user needs, then user requirements can be satisfied, but the system complexity increases
Solution Approach 1:
The patent implements universality by creating a single trajectory planning framework that can serve multiple functions and different user needs. The unified model using potential fields and cost functions can generate trajectories optimized for comfort, time, energy, or any combination thereof, eliminating the need for separate specialized methods while maintaining versatility.
Solution Approach 2:
The system achieves multi-functionality through dynamic parameter adjustment. By modifying weight factors and constraints in the cost function, the same planning algorithm can adapt to different operational requirements, providing trajectory customization without increasing structural complexity.
3Measurement precision
If optimal trajectories are searched for different weight requirements, then user-specific optimal paths are obtained, but the computation time increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the cost function structure and potential field model before actual trajectory planning. The framework establishes the evaluation criteria and mathematical models in advance, so that when specific user requirements are given, the system only needs to adjust parameters and run the search, rather than building the entire planning system from scratch each time.
Data Source
AI summary
The vehicle lane changing trajectory planning method includes: determining a starting point and an end point of a to-be-planned path on a grid lane changing map, which is a gridded local map reflecting a lane changing condition and takes a road boundary as a map boundary; performing weight allocation on all grids in the grid lane changing map by using a potential function; searching for optimal solutions corresponding to different weight requirements between the starting point and the end point; smoothing the optimal solutions corresponding to the different weight requirements to obtain lane changing trajectories corresponding to the different weight requirements; and selecting an optimal lane changing trajectory from the lane changing trajectories corresponding to the different weight requirements.