Lane Marking Detection Algorithm Based on High-Precision Map

Lane marking detection is challenging under adverse weather, especially in rain. Rain affects the visibility by changing the amount of light reflected from the road and splashing of rain periodically blocks vision through the car windshield. Colored (yellow, blue etc.) and eroded lane markings have lower contrast between lane and road surface. Rain affects the visibility of these colored and eroded lanes severely. In this paper, a robust lane detection and tracking method is proposed to detect lane under heavy rainfall condition. Here, an automatic adjustable threshold (AAT) is proposed, which improves the performance of canny edge detector to detect colored, eroded and blurred lane markings. Probabilistic Hough Transform (PHT) is used to detect candidate lane lines. A lane verification technique slope and angle based geometric constraints (SAGC) is used to detect and verify lane markings. Finally, a lane tracking technique by angle range validation (ARV) is introduced, which can keep track of the lane position when either left or right or both lane markings are partially or fully invisible due to erosion or occlusion or when lanes are not even painted on the road. Experimental results show that the average detection rate is 94.12%, and the average detection time is 30.24ms per frame which outperformed state-of-the-art method.

Lane width evaluation is one of the crucial aspects in road safety inspection, especially in work zones where a narrow lane width can result in a reduced roadway capacity and also, increase the probability of severe accidents. Using mobile mapping systems (MMS) equipped with laser scanners is a safe and cost-effective method for rapidly collecting detailed information along road surface. This paper presents an approach to derive lane width estimates using point clouds acquired from a geometrically-calibrated mobile mapping system. Starting from an accurate LiDAR point cloud, the road surface is extracted with the assistance of trajectory elevation data. Lane markings are identified based on the intensity data. Next, the lane marking centerline is derived and clustered to identify areas with ambiguous or missing lane markings and finally, use the normal (or, unambiguous) lane markings to estimate the lane width. The derived lane width estimates are used to develop a reporting mechanism for areas with narrow lanes, ambiguous lane markings, missing lane markings, and/or wide lanes.

Detection of road lane markings is attractive for practical applications such as advanced driver assistance systems and road maintenance. This paper proposes a system to detect and recognize road lane markings in panoramic images. The system can be divided into four stages. First, an inverse perspective mapping is applied to the original panoramic image to generate a top-view road view, in which the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, a feature vector of each potential road marking segment is extracted by calculating the Euclidean distance between the center and the boundary at regular angular steps. Third, the shape of each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous falsely detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is promising and is capable of recognizing 93%, 95% and 91% of striped line segments, blocks and arrows respectively, as well as 94% of the lane markings.

In the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly, a lane marking clustering method is introduced. This is done by combining the edge and color information of the lane marking. Finally, a fitting model is implemented. A line fitting model is used to extract the lane marking parameters. However for those regions in which lane can not described as a line, the algorithm computed the curve parameters using Lagrange interpolating polynomial.

Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization.

Computer detection intelligent mining algorithm for software defined networks

Lane marking detection is a basic task of Driver Assistance Systems (DAS) and Autonomous Land Vehicle (ALV). In order to improve the accuracy of lane marking detection, we design a rough lane marking locating method based on predecessors’ work. Considering the characteristic of lane markings, we extract Haar-like features of lane marking regions and train a strong cascade classifier by Adaboost Algorithm. The classifier is simple in principle and can fast locate the possible areas of lane markings accurately. Experimental results show that our method performs well.KeywordsLane marking locatingAdaboost algorithmHaar-like features

Since lane information is necessary for road security improvement in unmanned vehicle systems, the detection of the lane is an important task. Most existing approaches are particularly designed for specific road scenario (such as the highway, urban roads). However, the detection precision may be deteriorated if the lane markings are blurred and vestigial. Similarly, the reflection and smudges on the road surface also influence the detection result. In this paper, we introduce a novel robust lane detection algorithm based on the differential excitation. Firstly, we extract the region of interest (ROI) by considering human visual attention. Then we enhance salient texture information and remove the noise effectively through differential excitation. The binary image is obtained using Weber's law. Furthermore, we select the points that satisfy the proposed rules as voting points. Finally, the lane markings are detected and extracted by Hough transform accordingly. Experimental results on an open database indicate that the proposed method outperforms the classical Sebdani's approach and the Low's approach.

Aiming at the problems of low detection accuracy and long detection time in the traditional multi-sensor target data detection method, a multi-sensor target data detection method based on improved deep learning is proposed. A regional nomination network model is built to locate all rectangular areas where multi-sensor targets may exist. A sample acquisition model is established by fusing multi-sensor target data in this area in combination with the storage structure of multi-sensor target data to collect multi-sensor target data and filter the collected target data. The histogram feature method is used to extract the processed multi-sensor target data features and the extracted multi-sensor target data features are input into the neural network training samples. The square error function is used to calculate the sample deviation and the deviation is corrected by the deviation gradient sinking method. Finally, the multi-sensor target data detection results are output. Simulation results show that the proposed method has high accuracy, low false detection rate and short detection time along with applicability.

The authors propose a method for detecting and following a half-faded lane mark on an asphalt road using image processing. It ensures reliable detection and high speed following in the actual outdoor environment. The method is based on a template matching between a lane mark model and a detected lane mark. The authors built a testbed robot, which aims to repaint a half-faded lane mark semi-automatically. The details of this testbed robot and experimental results of a half-faded lane mark detection and following are described. Experimental results (trajectories of this robot) for a actual half-faded lane mark show the availability of this method.

We propose a robust method for automated detection of epileptic seizures using intracranial electroencephalogram (iEEG) recordings with two electrodes. The state-of-the-art seizure detection methods suffer from high number of false detections, even when designed to be patient-specific. The solution reported here aims to achieve very low false detection rate, while providing a high sensitivity. Two adjacent iEEG recordings are subtracted from each other to make the bipolar iEEG signal. The values achieved from singular value decomposition (SVD) of the bipolar iEEG signal are used as measure. A threshold is subsequently applied on the measure. Results indicate robustness of the proposed measure for seizure detection. The method is applied on 5 invasive recordings containing 54 seizures in 780 hours of multichannel iEEG recordings. On average, the results revealed 85.2% sensitivity and a very low false detection rate of 0.02 per hour in long-term continuous iEEG recordings.

These days, robust autonomous navigation incorporates the information on the digital map including variety of road features such as position of lane markings, road boundaries and etc. In this study, a method of extracting road features are developed using two dimensional laser range finder (LRF). In particular, range measurements are employed to identify the road surface using split and merge method. In addition, the reflectivity measurements are used to identify lane markings on the road surface. Finally a digital map of road surface is constructed which is useful for robust autonomous navigation. The proposed method is verified using experimental LRF data measured on the road environments.

<div class="section abstract"><div class="htmlview paragraph">The camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image. Then, all the lane marker candidates are clustered into many lanes and as the initialization of the following particle filtering tracking process. We calculate the distance map in the binary slice image by using a modified distance algorithm and sample particles in the distance map. Finally, we can track the lane markers with particle filtering successfully. A range of experiment results indicate that the proposed algorithm can detect and track multiple lanes correctly and robustly. Comparing with other method, it has enough tolerance to variant illumination and occlusion.</div></div>

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

In this paper, a novel road modeling strategy is proposed, defining an accurate and robust system that operates in real-time. The strategy aims to find a trade-off between computational requirements of real systems and accuracy and robustness of the results. The basis of the strategy is an adaptive road segmentation technique which ensures robust detections of lane markings and vehicles. A multiple lane model of the road is obtained by asserting hypotheses of lanes geometry based on perspective analysis and stochastic filtering. This multiple lane approach significantly improves vehicle location compared to other video-based works, as detected vehicles are accurately located within lanes. Tests show the adaptability, robustness and accuracy of the system in daylight situations with severe illumination changes, non-homogeneous color of the pavement of the road, lane markings occlusions, shadows, variable traffic conditions, etc., performing in real-time in all cases.