| dc.description.abstract | In recent years, there were lots of deaths caused by traffic accidents of rear-end collision. To prevent these fatalities, forward collision warning (FCW) systems have been proposed to protect drivers from the danger due to paying no attention to forward traffic situations. A standard FCW system should include two major parts: preceding vehicle detection and verification. The preceding vehicle detection extracts vehicle candidates, and then the verification procedure measures the likelihood of vehicles to reduce the wrong detection. Not only FCW systems but also many other advanced driving assistance systems (ADASs) such as lane departure warning (LDW), blind spot detection (BSD), around view monitoring (AVM), pedestrian collision warning (PCW), parking guiding assistance (PGA) systems, etc. have been enthusiastically developed to assist drivers.
In this dissertation, we present a weather-adaptive forward collision warning system applying local features for vehicle detection and global features for vehicle verification, which would help drivers to avoid unintended collisions to the preceding vehicles or obstacles. On the other hand, an image-based parking guiding system is also proposed to help drivers’ parking.
In the proposed FCW system, the local features such as horizontal and vertical edges are first calculated. Then edge maps are bi-leveled using a learning thresholding method to adapt the intensity variation of captured images, so that the extraction of edge points is less influenced by bad weather conditions. Third, the preserved edge points are used to generate possible objects . Fourth, the objects are selected based on edge response, location, and symmetry of object candidates to generate vehicle candidates. Three candidate generation schemes are hierarchically designed to extract vehicle candidates in various weather conditions. At last, a method based on principal component analysis (PCA) is proposed to verify the vehicle candidates. PCA is a technique used to extract the important features of a set of vehicle images. Each extracted feature describes a characteristic of vehicle appearance which is defined as a global feature. Depending on the extracted features, a candidate region can be decomposed and reconstructed. The similarity between the original regions and the reconstructed regions are measured to verify the vehicle candidates. Theoretically, PCA method is used to remove the non-vehicle candidates to reduce the false alarm. The proposed FCW system combined the merits of local and global features to reduce the false alarm rate but keep the detection rate.
The proposed FCW system has been test and evaluated on various weather conditions. The average accuracies of the proposed FCW system in clear and bad weather conditions are 96.2% and 79.1%, respectively. The accuracy on heavily rainy day is poor than other weather conditions due to the severely blurred edges and appearance. The blurred images is a huge challenge of all related FCW systems, but the proposed system can recognize vehicles under heavily rainy days as long as they can be observed.
The proposed FCW system has the following properties: (i) the edge extraction is adaptive to various lighting condition, (ii) the local features are mutually processed to improve the reliability of vehicle detection, (iii) the hierarchical generation schemes of vehicle detection enhances the adaptability to various weather conditions, (iv) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.
Parking is an essential skill for most drivers. However, it is difficult for many drivers to park their vehicles into a small parking area. Thus parking guiding assistant systems have been developed to help the drivers. In general, a steering sensor can provide the vehicle moving direction for drivers; nevertheless, a steering sensor is complicated to install and expensive. In this study, an image-based parking guidance (IPG) system is proposed to help drivers parking their cars into parking space. The proposed system only relies on an embedded hardware and a wide-angle camera to capture images for analysis without steering sensor. This is a money-saved technique; moreover, it is suitable for used cars and after-market usage.
In the proposed IPG system, input images are first transformed into top-view images by a homography transformation. Then corner points on two consecutive images are extracted to match each other. The feature-point pairs are further pruned by a least-square error metrics. The remained pairs are then used to estimate vehicle motion parameters, where an isometric transformation model based on the Ackermann steering geometry is proposed to describe the vehicle motion. At last, the vehicle trajectory is estimated based on the vehicle motion parameters and the parking guidance lines are drawn according to the vehicle trajectory.
The estimated parking guidance lines are compared with the actual trajectories based on several specific angles of turning steering wheel. The average errors of the estimated vehicle trajectories with different turning angles are about from 2 to 8 cm on images; the maximum errors are about from 6 to 30 cm on images. The key characteristics of a parking guidance system are stability and precision; the proposed system almost has similar accuracy to the steering-sensor-based system, but is a cheaper and accessible system. The image-based parking guidance system is worth developing to achieve a commerical product.
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