Autonomous Vehicle Path Sampling With Spatial Envelope Filtering
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Solution Overview
Problem
Current autonomous vehicle motion planning systems face inefficiencies in determining optimal trajectories due to the need to evaluate a large number of possible paths, which consumes significant computing resources and time, especially when considering physical and operational constraints.
Innovation Solution
A method involving a computing system that generates a basis path, determines secondary travel paths based on vehicle configuration data, and creates a spatial envelope to filter out infeasible trajectories, focusing on viable paths within the envelope's lateral offsets, thereby reducing computational cost and identifying lower-cost trajectories more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of possible trajectories are evaluated to ensure optimal motion planning, then the quality and reliability of the motion plan is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the trajectory evaluation process into two distinct phases: (1) generating a reduced set of candidate trajectories using spatial envelope and vehicle configuration filtering, and (2) evaluating only these filtered candidates. This segmentation allows the system to maintain high reliability by thoroughly evaluating viable options while improving productivity by eliminating the need to evaluate infeasible trajectories.
Solution Approach 2:
The patent applies preliminary action by performing filtering operations before the main trajectory evaluation. The spatial envelope generation and vehicle configuration data filtering are executed in advance to identify and eliminate infeasible trajectories before they enter the costly evaluation phase. This preliminary filtering ensures that only promising candidates are subjected to detailed assessment, thereby reducing computational waste.
2Adaptability or versatility
If all possible trajectories are generated without filtering, then the completeness of path options is improved, but the computational cost and processing time increase
Solution Approach 1:
The patent applies local quality by making the trajectory generation process adaptive to local vehicle characteristics and spatial constraints. The spatial envelope and vehicle configuration data are used to locally filter trajectories based on specific vehicle capabilities (e.g., turning radius, acceleration limits) and environmental constraints at each segment of the path, rather than applying uniform filtering across all trajectories.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the set of evaluated trajectories based on vehicle configuration parameters and spatial envelope characteristics. The filtering process modifies which trajectories are considered viable by changing the effective parameter space from all possible trajectories to only those satisfying vehicle-specific and environment-specific constraints, thereby reducing computational cost while maintaining adaptability.
3Productivity
If trajectories are filtered based on vehicle configuration data and spatial envelope, then the number of trajectories to evaluate is reduced, but the complexity of the filtering process increases
Solution Approach 1:
The patent introduces intermediary structures (spatial envelope and vehicle configuration data representations) that mediate between the raw trajectory generation and the evaluation process. These intermediaries serve as filtering layers that translate complex vehicle constraints and spatial relationships into simplified criteria for trajectory selection, reducing the burden on the evaluation process while maintaining accuracy.
Solution Approach 2:
The patent uses copying by creating simplified representations of vehicle configuration data and spatial envelope characteristics that can be efficiently processed during filtering. Instead of working with full-precision, complex models during the filtering stage, the system uses copied or approximated data structures that capture essential constraints, enabling faster filtering while preserving the accuracy needed for reliable trajectory selection.
Data Source
AI summary
Systems and methods for vehicle spatial path sampling are provided. The method includes obtaining an initial travel path for an autonomous vehicle from a first location to a second location and vehicle configuration data indicative of one or more physical constraints of the autonomous vehicle. The method includes determining one or more secondary travel paths for the autonomous vehicle from the first location to the second location based on the initial travel path and the vehicle configuration data. The method includes generating a spatial envelope based on the one or more secondary travel paths that indicates a plurality of lateral offsets from the initial travel path. And, the method includes generating a plurality of trajectories for the autonomous vehicle to travel from the first location to the second location such that each of the plurality of trajectories include one or more lateral offsets identified by the spatial envelope.


