Parking Path Search Using Intermediate Goal-Region Nodes
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Solution Overview
Problem
Autonomous and automated parking systems face computational inefficiencies due to large search spaces and non-holonomic constraints, leading to prolonged computational times in finding optimal parking paths, especially in corner cases like parallel parking.
Innovation Solution
A modified parking path algorithm that determines initial and intermediate waypoints by selecting an intermediate source node closest to the goal node, balancing global optimality and computational time, using a variant of the Dijkstra algorithm to efficiently navigate vehicles into parking spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard parking path algorithm is used to search for the optimal parking path, then the path optimality is improved, but the computational time increases significantly
Solution Approach 1:
The patent divides the parking path search into two segments: first finding an intermediate node that reaches the goal region, then finding the final path from the intermediate node to the goal pose. This segmentation reduces the computational burden of searching the entire path space while maintaining path quality.
Solution Approach 2:
The patent extracts the goal region definition from the complete path search problem. By defining a goal region and finding an intermediate node that reaches it, the system separates the complex optimal path search into a simpler two-stage process that reduces computational time.
2Productivity
If the search space is reduced to decrease computational time, then the computational efficiency is improved, but the path optimality deteriorates
Solution Approach 1:
The search space is segmented into two parts: first searching for an intermediate node that reaches the goal region (reducing search complexity), then searching for the final path segment from the intermediate node to the goal pose (ensuring optimality for the remaining portion). This maintains path quality while improving efficiency.
Solution Approach 2:
The patent performs a partial search by finding an intermediate node that reaches the goal region rather than searching for the complete optimal path in one step. This partial action reduces computational time while the second search stage ensures the final path segment is optimal.
Data Source
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AI summary
This document describes techniques and systems for performing a parking path search using a modified parking path algorithm. An example parking system can obtain an initial pose and a goal pose of a host vehicle and an obstacle map for a parking environment. The parking system can then use a parking path algorithm to determine first waypoints for a parking path. In response to a number of run-time iterations being greater than a threshold, the parking system can select an intermediate source node closest to the goal pose. The parking path algorithm can then be used to determine second waypoints from the intermediate source node to the goal pose. The host vehicle can then be controlled to park using the first waypoints and the second waypoints. In this way, a parking path search is performed more efficiently, while still generating a path close to the globally-optimal solution.