Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection &#x0028 PLARD &#x0029 approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1 &#x0029 data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2 &#x0029 feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board.
Road detection is beneficial to various other autonomous tasks @cite_26 @cite_46 @cite_9 @cite_30 @cite_31 @cite_47 . Over the years, various algorithms have been developed to tackle the road detection problem @cite_3 @cite_41 @cite_36 @cite_2 . For instance, model-based methods build shape @cite_1 @cite_22 or appearance models @cite_10 to describe the road structure and then identify road areas in the input images. Learning-based methods then attempt to employ classifiers (such as SVM @cite_49 and random forest @cite_48 ) to distinguish road from non-road areas. In practice, learning-based methods usually perform better than model-based methods.
@cite_30 @cite_31 @cite_26 @cite_47 @cite_22 @cite_41 @cite_36 @cite_9 @cite_48 @cite_1 @cite_3 @cite_49 @cite_2 @cite_46 @cite_10
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Road detection is a key task for autonomous land vehicles. Monocular vision-based road-detection algorithms are mostly based on machine learning approaches and are usually cast as classification problems. However, the pixel-wise classifiers are faced with the ambiguity caused by changes in road appearance, illumination and weather. An effective way to reduce the ambiguity is to model the contextual information with structured learning and prediction. Currently, the widely used structured prediction model in road detection is the Markov random field or conditional random field. However, the random field-based methods require additional complex optimization after pixel-wise classification, making them unsuitable for real-time applications. In this paper, we present a structured random forest-based road-detection algorithm which is capable of modelling the contextual information efficiently. By mapping the structured label space to a discrete label space, the test function of each split node can be trained i... Road detection is a crucial task in autonomous navigation systems. It is responsible for delimiting the road area and hence the free and valid space for maneuvers. In this paper, we consider the visual road detection problem where, given an image, the objective is to classify every of its pixels into road or non-road. We address this task by proposing a convolutional neural network architecture. We are especially interested in a model that takes advantage of a large contextual window while maintaining a fast inference. We achieve this by using a Network-in-Network (NiN) architecture and by converting the model into a fully convolutional network after training. Experiments have been conducted to evaluate the effects of different contextual window sizes (the amount of contextual information) and also to evaluate the NiN aspect of the proposed architecture. Finally, we evaluated our approach using the KITTI road detection benchmark achieving results in line with other state-of-the-art methods while maintaining real-time inference. The benchmark results also reveal that the inference time of our approach is unique at this level of accuracy, being two orders of magnitude faster than other methods with similar performance. Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle. Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task, because they can extract high-level local features to find road regions from raw RGB data, such as convolutional neural networks and fully convolutional networks (FCNs). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose siamesed FCNs (named “s-FCN-loc”), which is able to consider RGB-channel images, semantic contours, and location priors simultaneously to segment the road region elaborately. To be specific, the s-FCN-loc has two streams to process the original RGB images and contour maps, respectively. At the same time, the location prior is directly appended to the siamesed FCN to promote the final detection performance. Our contributions are threefold: 1) An s-FCN-loc is proposed that learns more discriminative features of road boundaries than the original FCN to detect more accurate road regions. 2) Location prior is viewed as a type of feature map and directly appended to the final feature map in s-FCN-loc to promote the detection performance effectively, which is easier than other traditional methods, namely, different priors for different inputs (image patches). 3) The convergent speed of training s-FCN-loc model is 30 faster than the original FCN because of the guidance of highly structured contours. The proposed approach is evaluated on the KITTI road detection benchmark and one-class road detection data set, and achieves a competitive result with the state of the arts.