Vehicle Image Segmentation for Off-Road Action Suggestions
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Solution Overview
Problem
Individuals lacking experience in off-road and adventurous driving face challenges in decision-making when confronted with specific conditions or terrains, necessitating improved vehicle action suggestions.
Innovation Solution
A machine learning-based image segmentation system deployed on a vehicle's electronic control unit (ECU) provides dynamic presentation of vehicle action suggestions through terrain detection and alert systems, utilizing cameras and sensors to identify obstructions and recommend throttle, steering, speed adjustments, and other actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning-based image segmentation is implemented for terrain detection, then driver awareness and safety are enhanced, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct functional components: image acquisition from cameras, semantic feature extraction through machine learning models, terrain classification, and suggestion generation. This modular segmentation allows the complex system to be managed through independent modules that can be developed, tested, and maintained separately, thus enhancing reliability without being overwhelmed by overall system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw image data and driver recommendations. The machine learning model acts as an intermediary that transforms complex image data into simplified semantic features and terrain classifications, which then feed into the suggestion generation system. This intermediary layer abstracts the complexity from the final decision-making process while maintaining high reliability in terrain assessment.
2Ease of operation
If real-time terrain detection and multiple action suggestions are provided, then maneuverability in off-road conditions is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images to extract semantic features before full terrain analysis is required. The machine learning model continuously processes image data in the background, maintaining ready-to-use terrain classifications and feature extractions that can be quickly converted into action suggestions when needed, thus reducing real-time processing delays while improving ease of operation.
Solution Approach 2:
The patent implements partial action by providing a prioritized list of action suggestions rather than analyzing every possible terrain parameter simultaneously. The system focuses on the most critical terrain features and generates high-priority recommendations first, allowing the vehicle to respond quickly to immediate hazards while continuing to process additional terrain data in the background for future recommendations.
Data Source
AI summary
Aspects of the subject disclosure relate to dynamic presentation of vehicle action suggestions using machine learning-based image segmentation on a vehicle. A device implementing the subject technology may include a processor configured to obtain an image from a camera of the vehicle. The processor is also configured to determine one or more semantic features in the image by performing image segmentation on the image using a trained machine learning model. The processor is also configured to detect, based on the one or more semantic features, a vehicle path condition in the image. The processor is also configured to display, on a user interface, a notification indicating a plurality of suggestions that can be a respective action to be executed by the vehicle based on the detected vehicle path condition.