Multi-Stage Vehicle Detection Using Sliding Window Filters
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and responding to nearby vehicles within their field of view, particularly in complex environments, due to limitations in existing image processing and vehicle detection systems.
Innovation Solution
The implementation of a vehicle detection system that uses a sliding-window filter to identify region-pairs in images, followed by a multi-stage classification process to determine the presence of nearby vehicles, allowing for real-time control adjustments such as speed reduction, safe distance maintenance, and lane changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multi-stage classification process is used to improve vehicle detection accuracy, then detection precision increases, but processing time and computational complexity increase
Solution Approach 1:
The vehicle detection process is divided into multiple stages: initial region-pair identification using sliding-window filter, followed by selective multi-stage classification only for potential vehicle regions. This segmentation allows the system to apply computationally intensive classification only where needed, improving accuracy while controlling processing time.
Solution Approach 2:
The system performs partial classification by applying the multi-stage classification process only to region-pairs that meet initial detection criteria, rather than processing the entire image uniformly. This selective approach reduces overall processing time while maintaining high detection accuracy for potential vehicles.
2Measurement precision
If advanced vehicle detection algorithms are implemented to improve detection accuracy, then detection precision increases, but processor usage and computational resources increase
Solution Approach 1:
The detection system segments processing into two phases: a lightweight initial screening using sliding-window color-based region identification, and a more intensive multi-stage classification phase applied only to candidate regions. This reduces overall processor usage by avoiding full classification on entire images.
Solution Approach 2:
The sliding-window filter serves as an intermediary step that pre-processes the image to identify potential vehicle regions before applying the computationally intensive multi-stage classification. This intermediary filtering reduces the data volume requiring advanced processing, lowering overall computational resource consumption.
3Speed
If real-time vehicle detection and response is implemented to improve safety, then response time decreases, but system complexity increases
Solution Approach 1:
The control system is segmented into multiple functional modules: image capture, region-pair identification, potential vehicle detection, multi-stage classification, and control execution. This modular segmentation enables real-time processing by allowing parallel operations and optimized data flow between stages, improving response time while managing complexity through structured organization.
Data Source
AI summary
The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes a vehicle detection system. The vehicle detection system includes receiving an image of a field of view of the vehicle and identifying a region-pair in the image with a sliding-window filter. The region-pair is made up of a first region and a second region. Each region is determined based on a color of pixels within the sliding-window filter. The vehicle detection system also determines a potential second vehicle in the image based on the region-pair. In response to determining the potential second vehicle in the image, the vehicle detection system performs a multi-stage classification of the image to determine whether the second vehicle is present in the image. Additionally, the vehicle detection system provides instructions to control the first vehicle based at least on the determined second vehicle.