Vehicle Path Planning With Parallel Pose-Space Trajectory Evaluation
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Solution Overview
Problem
Traditional vehicle path planning methods are inefficient due to limited parallelization, leading to sparse exploration of configuration spaces, which can result in lengthy computation times and resource-intensive processes, especially in dense pose configuration spaces.
Innovation Solution
The approach involves massive parallel processing to evaluate paths in a dense pose configuration space by translating trajectories along the orientation axis, using bit vectors for reachability evaluation and parallel reduction patterns to account for dependencies between trajectory sections, allowing for independent processing of sections and leveraging modern parallel processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional graph search algorithms (e.g., A*) are used with heuristics to reduce search space, then computation time is reduced, but the density of configuration space exploration becomes sparse and paths may not be identified in all scenarios
Solution Approach 1:
The configuration space is segmented into multiple independent sections that can be processed in parallel. Each processing thread handles a specific section of the configuration space, allowing dense exploration without increasing sequential computation time. This segmentation enables the system to maintain high exploration density while keeping computation time manageable through parallel processing.
Solution Approach 2:
The patent introduces parallel processing as an additional dimension for exploring the configuration space. Instead of sequentially traversing the space, multiple processing threads simultaneously explore different sections, effectively adding a temporal parallelism dimension. This allows dense configuration space exploration to occur without proportionally increasing computation time, as multiple explorations happen concurrently rather than sequentially.
2Reliability
If the configuration space is explored densely to identify all potential paths, then path identification accuracy improves, but computation time increases and resource consumption becomes excessive
Solution Approach 1:
The configuration space is divided into multiple sections that can be independently processed in parallel. Each processing thread handles a specific section, allowing dense exploration of all sections to occur simultaneously rather than sequentially. This segmentation enables complete path identification with high accuracy while maintaining manageable computation time through parallel execution.
Solution Approach 2:
Multiple processing threads are merged into a coordinated parallel processing system that collectively explores the entire configuration space densely. The results from all thread sections are combined to provide comprehensive path identification. This merging allows the system to achieve complete and accurate path exploration without the computational burden of a single sequential process examining every detail.
3Productivity
If conventional parallelization with limited threads is used, then resource consumption is controlled, but the speed-up factor is minimal and modern processor capabilities are underutilized
Solution Approach 1:
The configuration space is segmented into multiple sections that can be processed by numerous parallel threads,充分利用 modern processors with thousands of cores. Each thread handles a specific section independently, enabling massive parallelization that achieves significant speed-up factors. This segmentation strategy allows the system to harness modern processor capabilities without requiring complex inter-thread communication or coordination mechanisms.
Solution Approach 2:
Each processing thread independently evaluates reachability of its assigned configuration space section using bit vector operations and parallel reduction patterns without requiring complex inter-thread synchronization. The threads are self-sufficient in their processing, requiring minimal coordination overhead. This self-service approach enables massive parallelization while keeping the processing architecture relatively simple and easy to implement.
Data Source
AI summary
To determine a path through a pose configuration space, trajectories of poses may be evaluated in parallel based at least on translating the trajectories along at least one axis of the pose configuration space (e.g., an orientation axis). A trajectory may include at least a portion of a turn having a fixed turn radius. Turns or turn portions that have the same turn radius and initial orientation can be translatively shifted along and processed in parallel along the orientation axis as they are translated copies of each other, but with different starting points. Trajectories may be evaluated based at least on processing variables used to evaluate reachability as bit vectors with threads effectively performing large vector operations in synchronization. A parallel reduction pattern may be used to account for dependencies that may exist between sections of a trajectory for evaluating reachability, allowing for the sections to be processed in parallel.