Towards Autonomous Driving : Road Surface Signs Recognition using Neural Networks

In recent years, the number of traffic accidents has rapidly increased for many reasons. However, the most common cause is the driver carelessness and inattentiveness to road signs. Therefore, the aim of this paper is to automatically recognize road surface markings and by availing the information to the driver, hope to reduce road accidents. In the proposed method, the captured image is transformed and edges information used to extract the target road area. The road marking candidate is then extracted and recognized using a neural network

Map-enhanced visual taxiway extraction for autonomous taxiing of UAVs

In this paper, a map-enhanced method is proposed for vision-based taxiway centreline extraction, which is a prerequisite of autonomous visual navigation systems for unmanned aerial vehicles. Comparing with other sensors, cameras are able to provide richer information. Consequently, vision based navigations have been intensively studied in the recent two decades and computer vision techniques are shown to be capable of dealing with various problems in applications. However, there are signi cant drawbacks associated with these computer vision techniques that the accuracy and robustness may not meet the required standard in some application scenarios. In this paper, a taxiway map is incorporated into the analysis as prior knowledge to improve on the vehicle localisation and vision based centreline extraction. We develop a map updating algorithm so that the traditional map is able to adapt to the dynamic environment via Bayesian learning. The developed method is illustrated using a simulation study

Unconstrained Road Marking Recognition with Generative Adversarial Networks

Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often over-dependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates high-quality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a class-conditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.Comment: Accepted at IEEE Intelligent Vehicles Symposium (IV), 201

Road markers classification using binary scanning and slope contours

Road markers guide the driver while driving on the road to control the traffic for the safety of the road users. With the booming autonomous car technology, the road markers classification is important in its vision segment to navigate the autonomous car. A new method is proposed in this paper to classify five types of road markers namely dashed, single, double, solid-dashed and dashed-solid which are commonly found on the two lane single carriageway. The classification is using unique feature acquired from the binary image by scanning on each of the images to calculate the frequency of binary transition. Another feature which is the slopes between the two centroids which allow the proposed method, to perform the classification within the same video frame period. This proposed method has been observed to achieve an accuracy value of at least 93%, which is higher than the accuracy value achieved by the existing method

Improved situation awareness for autonomous taxiing through self-learning

As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs’ autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. Indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically