Long-Distance Vehicle Perception Using ROI Cropping and 3D Localization
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Solution Overview
Problem
Autonomous vehicles, particularly semi-trailer trucks, face limitations in perception range, often unable to detect objects at distances beyond 300 meters, which can lead to unsafe maneuvers or collisions due to insufficient image processing capabilities.
Innovation Solution
The implementation of a long-distance perception system that includes cameras, a picture-in-picture module, detection module, and depth estimation module, which crop regions of interest from images, detect objects using machine learning, and determine 3D positions of objects using terrain maps and camera pose information, enabling detection of objects up to 1000 meters away.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is performed on full-resolution images to detect objects, then detection accuracy is improved, but processing time and computational load increase significantly
Solution Approach 1:
The image processing system divides the full image into multiple regions of interest (ROIs) based on detected features or expected object locations. Only these segmented regions are processed at full resolution for object detection, while other areas are processed at lower resolution or skipped entirely. This segmentation approach maintains detection accuracy for relevant areas while reducing overall processing time and computational load.
Solution Approach 2:
The system applies different processing qualities to different regions of the image. Regions containing or near detected objects are processed at high resolution with full detection algorithms, while other regions use reduced resolution or simplified processing. This local quality differentiation ensures accurate detection where needed while minimizing unnecessary computational expenditure in areas less likely to contain targets.
2Reliability
If the perception range is extended to detect objects at 1000 meters, then safety is improved, but the complexity of the detection system increases
Solution Approach 1:
The system transitions from two-dimensional image plane analysis to three-dimensional spatial understanding by incorporating depth estimation modules and terrain map data. This dimensional enhancement allows the system to detect objects at extended distances (up to 1000 meters) by utilizing elevation, slope, and geographic context information, thereby improving safety without requiring proportionally complex additional hardware.
Solution Approach 2:
The system introduces intermediary processing modules including depth estimation components and terrain map integration layers that bridge the gap between raw image data and object detection. These intermediaries process image data through multiple stages, using terrain information and depth cues to enhance distant object detection capability while managing system complexity through modular architecture.
3Measurement precision
If multiple reference points are used to determine object location, then positioning accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-identifying and marking multiple reference points in the image before conducting full object detection and localization. These reference points are established in advance based on terrain features, road markings, or other stable environmental elements. By having reference points ready beforehand, the system reduces real-time computational requirements while maintaining high positioning accuracy through triangulation or multi-point geometric calculations.
Data Source
AI summary
Image processing techniques are described to obtain an image from a camera located on a vehicle while the vehicle is being driven, cropping a portion of the obtained image corresponding to a region of interest, detecting an object in the cropped portion, adding a bounding box around the detected object, determining position(s) of reference point(s) on the bounding box, and determining a location of the detected object in a spatial region where the vehicle is being driven based on the determined one or more positions of the second set of one or more reference points on the bounding box.