Autonomous Vehicle Path Planning with Dynamic Length Adjustment
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Solution Overview
Problem
Autonomous driving vehicles face challenges in determining optimized path trajectories in complex scenes due to obstacles and traffic conditions, leading to unsuccessful path planning operations.
Innovation Solution
The method initializes a default path length and iteratively reduces it by incremental amounts during path planning operations until a successful path trajectory is generated or a stop condition is reached, switching to a backup path if the failure is not due to obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional path planning operations use fixed path length estimation, then the planning process is simple, but the vehicle fails to generate successful path trajectories in complex scenes
Solution Approach 1:
The patent applies dynamics by making the path length parameter adjustable rather than fixed. The path planning operation dynamically modifies the path length based on whether obstacles are detected, transitioning between different operational modes (with obstacle detection and without) to optimize trajectory generation success in varying scene complexities.
Solution Approach 2:
The patent changes the path length parameter dynamically during the planning process. When obstacles are detected blocking the initial path, the system adjusts the path length parameter to generate alternative trajectories, thereby improving the reliability of path trajectory generation in complex scenes.
2Reliability
If the vehicle repeatedly performs path planning with reduced path length, then the vehicle can navigate complex scenes, but the computation time increases
Solution Approach 1:
The patent applies partial action by selectively performing reduced path length planning only when necessary (when obstacles block the initial path). Rather than repeatedly performing full path planning operations, the system performs a simpler check first and only engages in more computationally intensive operations when required, thereby reducing overall computation time while maintaining reliability.
Solution Approach 2:
The system uses feedback from obstacle detection results to control the path planning process. When obstacles are detected blocking the initial path, the system triggers reduced path length planning; when no obstacles are present, it proceeds directly to execution. This feedback mechanism avoids unnecessary computation and reduces time loss.
Data Source
AI summary
According to some embodiments, an ADV includes a method to plan a path trajectory. The method initializes a path length to a initial path length and performs a path planning operation using the initial path length. The method determines if the path planning operation successfully generates the path trajectory. If the path trajectory fails to be successfully generated, the method determines a reason for the failure. If the path trajectory fails to be generated for reasons other than an obstacle blocking the vehicle, the method defaults the path trajectory to a backup path. If the path trajectory fails to be generated due to an obstacle blocking the vehicle, the method reduces the path length by an incremental amount to generate a reduced path length. The method performs the path planning operation using the reduced path length until a path trajectory is successfully generated or until a stop condition is reached.