Off-Road Vehicle Path Control Using Terrain Probability Costs
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately categorizing off-road terrain, particularly in variable lighting conditions, leading to suboptimal path determination for autonomous vehicles.
Innovation Solution
A control system that determines the probability of image data relating to terrain regions and allocates costs based on path or non-path criteria, using a three-tiered cost structure to optimize the vehicle's path by distinguishing between path, non-path, and uncertain regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing vehicle control systems use cameras to detect images and categorize terrain portions into different categories, then the vehicle can navigate off-road environments, but the systems struggle to categorize terrain correctly in variable lighting conditions such as when shadows are cast on the terrain
Solution Approach 1:
The patent divides the terrain image into multiple sub-regions and processes each sub-region independently to determine path probability. This segmentation allows the system to handle variable lighting conditions more effectively by analyzing local characteristics rather than treating the entire image as a single category, thereby improving terrain categorization accuracy in shadows and varying light.
Solution Approach 2:
The patent applies different cost values to different sub-regions based on their individual path probability assessments. By assigning local quality characteristics (cost values) to specific terrain portions rather than uniform treatment, the system can accurately navigate through shadowed areas while maintaining overall path optimization.
2Productivity
If the control system uses a simple binary classification (path/non-path) for terrain regions, then the processing is computationally efficient, but the system cannot adequately handle uncertain regions or variable lighting conditions
Solution Approach 1:
The patent introduces a dynamic three-tiered cost structure that adapts to different terrain uncertainty levels. Instead of static binary classification, the system dynamically assigns cost values based on path probability assessments, allowing it to respond to variable lighting and uncertain regions while maintaining computational efficiency through structured cost allocation.
Solution Approach 2:
The patent changes the parameter representation from binary (path/non-path) to a continuous probability-based cost system. By using path probability criteria and non-path probability criteria to determine cost values, the system captures uncertainty information while maintaining processing efficiency through structured parameter transformation.
3Speed
If the control system allocates costs based on simple terrain features, then the computation is fast, but the path determination is suboptimal in complex off-road environments with variable lighting
Solution Approach 1:
The patent performs preliminary probability assessment for each sub-region before final cost allocation. By pre-evaluating path probability and non-path probability criteria for each terrain portion, the system prepares optimized cost values in advance, enabling fast yet accurate path determination in complex off-road environments with variable lighting conditions.
Data Source
AI summary
Embodiments of the present invention relate to a control system for a vehicle, the control system comprising at least one controller and being configured to: obtain image data relating to terrain to be traversed by the vehicle, and for each of a plurality of sub-regions of the image data: determine probability data relating to whether the respective sub-region relates to a path region or a non-path region of the terrain; and determine a cost for the vehicle to traverse a portion of the terrain to which the sub-region relates depending on the probability data meeting one or more path probability criteria indicating that the sub-region relates to the path region, one or more non-path probability criteria indicating that the sub-region relates to the non-path region or neither the path probability criteria nor the non-path probability criteria; and determining a vehicle path in dependence on the determined costs.