Статті в журналах: "Detection of road surface conditions"

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Choi, Wansik, Jun Heo, and Changsun Ahn. "Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset." Sensors 21, no. 22 (November 22, 2021): 7769. http://dx.doi.org/10.3390/s21227769.

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Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.

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Piccardi, Armando, and Lorenzo Colace. "Optical Detection of Dangerous Road Conditions." Sensors 19, no. 6 (March 19, 2019): 1360. http://dx.doi.org/10.3390/s19061360.

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We demonstrated an optical method to evaluate the state of asphalt due to the presence of atmospheric agents using the measurement of the polarization/depolarization state of near infrared radiation. Different sensing geometries were studied to determine the most efficient ones in terms of performance, reliability and compactness. Our results showed that we could distinguish between a safe surface and three different dangerous surfaces, demonstrating the reliability and selectivity of the proposed approach and its suitability for implementing a sensor.

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Lee, Taehee, Yeohwan Yoon, Chanjun Chun, and Seungki Ryu. "CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes." Electronics 10, no. 12 (June 10, 2021): 1402. http://dx.doi.org/10.3390/electronics10121402.

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Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

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Songsong Xu, Songsong Xu, Chi Ruan Chi Ruan, and Lili Feng Lili Feng. "Road surface condition sensor based on scanning detection of backward power." Chinese Optics Letters 12, no. 5 (2014): 050801–50804. http://dx.doi.org/10.3788/col201412.050801.

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Bouilloud, L., E. Martin, F. Habets, A. Boone, P. Le Moigne, J. Livet, M. Marchetti, et al. "Road Surface Condition Forecasting in France." Journal of Applied Meteorology and Climatology 48, no. 12 (December 1, 2009): 2513–27. http://dx.doi.org/10.1175/2009jamc1900.1.

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Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

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Kumari Dara, Anitha, and Dr A. Govardhan. "Detection of Coordinate Based Accident-Prone Areas on Road Surface using Machine Learning Methods." International Journal of Computer Engineering and Information Technology 12, no. 3 (March 31, 2020): 19–25. http://dx.doi.org/10.47277/ijceit/12(3)1.

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The growth in the road networks in India and other developing countries have influenced the growth in transport industry and other industries, which depends on the road network for operations. The industries such as postal services or mover services have influenced the similar growths in these industries as well. However, the dependency of these industries is high on the road surface conditions and any deviation on the road surface conditions can also influence the performance of the services provided by the mentioned services. Nonetheless, the conditions of the road surface are one of the prime factors for road safety and number of evidences are found, which are discussed in subsequent sections of this work, that the bad road surface conditions are increasing the road accidents. Several parallel research attempts are deployed in order to find out, the regions where the road surface conditions are not proper, and the traffic density is higher. Nevertheless, outcomes of these parallel works are highly criticised due to the lack of accuracy in detection of the road surface defects, detection of accurate location of the defects and detection of the traffic density data from various sources. Thus, this work proposes a novel framework for detection of the road defect and further mapping to the spatial data coordinates resulting into the detection of the accident-prone zones or accident affinities of the roads. This work deploys a self-adjusting parametric coefficient-based regression model for detection of the risk factors of the road defects and in the other hand, extracts the traffic density of the road regions and further maps the accident affinities. This work outcomes into 97.69% accurate detection of the road accident affinity and demonstrates less complexity compared with the other parallel research outcomes

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7

Kumar, P., and E. Angelats. "AN AUTOMATED ROAD ROUGHNESS DETECTION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 91–96. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-91-2017.

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Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

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8

Dong, Dapeng, and Zili Li. "Smartphone Sensing of Road Surface Condition and Defect Detection." Sensors 21, no. 16 (August 12, 2021): 5433. http://dx.doi.org/10.3390/s21165433.

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Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.

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Takeuchi, Kazuya, Keiji Shibata, and Yuukou Horita. "Detection of Road Surface Conditions in Winter using CCTV Camera for Road Monitoring." IEEJ Transactions on Electronics, Information and Systems 135, no. 7 (2015): 901–7. http://dx.doi.org/10.1541/ieejeiss.135.901.

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Sharma, Sunil Kumar, Haidang Phan, and Jaesun Lee. "An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors." Applied Sciences 10, no. 13 (June 29, 2020): 4490. http://dx.doi.org/10.3390/app10134490.

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Road surface monitoring is an essential problem in providing smooth road infrastructure to commuters. This paper proposed an efficient road surface monitoring using an ultrasonic sensor and image processing technique. A novel cost-effective system, which includes ultrasonic sensors sensing with GPS for the detection of the road surface conditions, was designed and proposed. Dynamic time warping (DTW) technique was incorporated with ultrasonic sensors to improve the classification and accuracy of road surface detecting conditions. A new algorithm, HANUMAN, was proposed for automatic recognition and calculation of pothole and speed bumps. Manual inspection was performed and comparison was undertaken to validate the results. The proposed system showed better efficiency than the previous systems with a 95.50% detection rate for various road surface irregularities. The novel framework will not only identify the road irregularities, but also help in decreasing the number of accidents by alerting drivers.

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Kongrattanaprasert, Wuttiwat, Hideyuki Nomura, Tomoo Kamakura, and Koji Ueda. "Detection of Road Surface Conditions Using Tire Noise from Vehicles." IEEJ Transactions on Industry Applications 129, no. 7 (2009): 761–67. http://dx.doi.org/10.1541/ieejias.129.761.

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12

Ciamberlini, C., G. Innocenti, and G. Longobardi. "An optoelectronic prototype for the detection of road surface conditions." Review of Scientific Instruments 66, no. 3 (March 1995): 2684–89. http://dx.doi.org/10.1063/1.1145610.

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13

Al-refai, Ghaith, Hisham Elmoaqet, and Mutaz Ryalat. "In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning." Applied Sciences 12, no. 18 (September 6, 2022): 8928. http://dx.doi.org/10.3390/app12188928.

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Many network protocols such as Controller Area Network (CAN) and Ethernet are used in the automotive industry to allow vehicle modules to communicate efficiently. These networks carry rich data from the different vehicle systems, such as the engine, transmission, brake, etc. This in-vehicle data can be used with machine learning algorithms to predict valuable information about the vehicle and roads. In this work, a low-cost machine learning system that uses in-vehicle data is proposed to solve three categorization problems; road surface conditions, road traffic conditions and driving style. Random forests, decision trees and support vector machine algorithms were evaluated to predict road conditions and driving style from labeled CAN data. These algorithms were used to classify road surface condition as smooth, even or full of holes. They were also used to classify road traffic conditions as low, normal or high, and the driving style was classified as normal or aggressive. Detection results were presented and analyzed. The random forests algorithm showed the highest detection accuracy results with an overall accuracy score between 92% and 95%.

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Dong, Xue Peng, Lei Han, Gang Yi Yin, and Lei Cai. "Study of a Temperature Detection Sensor in Road Surface Sensors System." Key Engineering Materials 609-610 (April 2014): 932–36. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.932.

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Road surface sensors system, which monitors the road conditions and raises an alarm when necessary, plays a very important role in the safety of transportation. The temperature detection sensor is used to measure the temperature on and beneath the road surface. It synthetically estimates the road conditions in cooperation with other sensors, such as water film sensor, salinity sensor and so on. In this paper, the theory, circuit structure and testing method of the temperature detection sensor have been introduced, and the data processing pattern and accuracy of results have been improved.

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Döring, Jakob, Lakshan Tharmakularajah, Jakob Happel, and Karl-Ludwig Krieger. "A novel approach for road surface wetness detection with planar capacitive sensors." Journal of Sensors and Sensor Systems 8, no. 1 (January 21, 2019): 57–66. http://dx.doi.org/10.5194/jsss-8-57-2019.

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Abstract. This paper presents a novel approach for detecting road surface wetness with planar capacitive sensors on the wheel arch liner of a motor vehicle. For this purpose, various design parameters of interdigital electrodes are studied by means of the finite element method (FEM). A suitable design for the detection of whirled-up water is proposed, which is manufactured on a flexible printed circuit board (PCB) and investigated in an experimental study. A test bench is built for that purpose, which includes a motor vehicle's front wheel arch liner and can simulate realistic road surface wetness conditions. Experimental results show the possibility of distinguishing between different road wetness conditions and confirm that a static wetting of the wheel arch liner can be detected. Finally, an application-specific sensor system is proposed, which is validated by experiments on a test bench and is integrated into a vehicle. Field test results show the feasibility of detecting different road wetness levels and demonstrate the potential of the presented approach.

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16

Jang, Jinhwan. "Wheel Slip-based Road Surface Slipperiness Detection." Open Transportation Journal 14, no. 1 (September 8, 2020): 186–93. http://dx.doi.org/10.2174/1874447802014010186.

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Background: Faced with the high rate of traffic accidents under slippery road conditions, agencies attempt to quickly identify slippery spots on the road and drivers want to receive information on the impending dangerous slippery spot, also known as “black ice.” Methods: In this study, wheel slip, defined as the difference between both speeds of vehicular transition and wheel rotation, was used to detect road slipperiness. Three types of experiment cars were repeatedly driven on snowy and dry surfaces to obtain wheel slip data. Three approaches, including regression analysis, support vector machine (SVM), and deep learning, were explored to categorize into two states-slippery or non-slippery. Results: Results indicated that a deep learning model resulted in the best performance with accuracy of 0.972, only where sufficient data were obtained. SVM models universally showed good performance, with average accuracy of 0.965, regardless of sample size. Conclusion: The proposed models can be applied to any connected devices including digital tachographs and on-board units for cooperative ITS projects that gather wheel and transition speeds of a moving vehicle to enhance road safety in winter season though collecting followed by providing dangerous slippery spots on the road.

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van der Horst, B. B., R. C. Lindenbergh, and S. W. J. Puister. "MOBILE LASER SCAN DATA FOR ROAD SURFACE DAMAGE DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1141–48. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1141-2019.

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<p><strong>Abstract.</strong> Road surface anomalies affect driving conditions, such as driving comfort and safety. Examples for such anomalies are potholes, cracks and ravelling. Automatic detection and localisation of these anomalies can be used for targeted road maintenance. Currently road damage is detected by road inspectors who drive slowly on the road to look out for surface anomalies, which can be dangerous. For improving the safety road inspectors can evaluate road images. However, results may be different as this evaluation is subjective. In this research a method is created for detecting road damage by using mobile profile laser scan data. First features are created, based on a sliding window. Then K-means clustering is used to create training data for a Random Forest algorithm. Finally, mathematical morphological operations are used to clean the data and connect the damage points. The result is an objective and detailed damage classification. The method is tested on a 120 meters long road data set that includes different types of damage. Validation is done by comparing the results to a classification of a human road inspector. However, the damage classification of the proposed method contains more details which makes validation difficult. Nevertheless does this method result in 79% overlap with the validation data. Although the results are already promising, developments such as pre-processing the data could lead to improvements.</p>

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Zhou, Kai, Ri Sha Na, and Xu Dong Wang. "Research on Vehicle ABS Detection Technology Based on Flywheel Inertia Simulation." Advanced Materials Research 765-767 (September 2013): 2117–22. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2117.

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Anti-lock Braking System (ABS) has widespread used depending on its mature technology and superior performance. We design a test rig which can simulate the running condition of wheels for ABS to detect the braking performance. The kinetic energy of vehicle is replaced by the kinetic energy of rotating flywheel, and the tire-road friction coefficient is replaced by magnetic powder clutch. The amplitude of exciting current to the clutch has linear relationship with the friction coefficient, so as to provide a datum for detecting the working status of ABS under various road conditions. The system can realize simulating test of single road surface, bisectional road surface and joint road surface. The validity of the road simulation method can be verified by the real-time data from the user interface.

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Knyaz, V. A., and A. G. Chibunichev. "PHOTOGRAMMETRIC TECHNIQUES FOR ROAD SURFACE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 515–20. http://dx.doi.org/10.5194/isprsarchives-xli-b5-515-2016.

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The quality and condition of a road surface is of great importance for convenience and safety of driving. So the investigations of the behaviour of road materials in laboratory conditions and monitoring of existing roads are widely fulfilled for controlling a geometric parameters and detecting defects in the road surface. Photogrammetry as accurate non-contact measuring method provides powerful means for solving different tasks in road surface reconstruction and analysis. The range of dimensions concerned in road surface analysis can have great variation from tenths of millimetre to hundreds meters and more. So a set of techniques is needed to meet all requirements of road parameters estimation. Two photogrammetric techniques for road surface analysis are presented: for accurate measuring of road pavement and for road surface reconstruction based on imagery obtained from unmanned aerial vehicle. The first technique uses photogrammetric system based on structured light for fast and accurate surface 3D reconstruction and it allows analysing the characteristics of road texture and monitoring the pavement behaviour. The second technique provides dense 3D model road suitable for road macro parameters estimation.

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Knyaz, V. A., and A. G. Chibunichev. "PHOTOGRAMMETRIC TECHNIQUES FOR ROAD SURFACE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 515–20. http://dx.doi.org/10.5194/isprs-archives-xli-b5-515-2016.

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The quality and condition of a road surface is of great importance for convenience and safety of driving. So the investigations of the behaviour of road materials in laboratory conditions and monitoring of existing roads are widely fulfilled for controlling a geometric parameters and detecting defects in the road surface. Photogrammetry as accurate non-contact measuring method provides powerful means for solving different tasks in road surface reconstruction and analysis. The range of dimensions concerned in road surface analysis can have great variation from tenths of millimetre to hundreds meters and more. So a set of techniques is needed to meet all requirements of road parameters estimation. Two photogrammetric techniques for road surface analysis are presented: for accurate measuring of road pavement and for road surface reconstruction based on imagery obtained from unmanned aerial vehicle. The first technique uses photogrammetric system based on structured light for fast and accurate surface 3D reconstruction and it allows analysing the characteristics of road texture and monitoring the pavement behaviour. The second technique provides dense 3D model road suitable for road macro parameters estimation.

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21

Martinez-Ríos, Erick Axel, Martin Rogelio Bustamante-Bello, and Luis Alejandro Arce-Sáenz. "A Review of Road Surface Anomaly Detection and Classification Systems Based on Vibration-Based Techniques." Applied Sciences 12, no. 19 (September 20, 2022): 9413. http://dx.doi.org/10.3390/app12199413.

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Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.

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Roobini, M. S., Soujanya Mulakalapally, Navyasri Mungamuri, M. Lakshmi, Anitha Ponraj, and D. Deepa. "Car Accident Detection and Notification System Using Smartphone." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3389–93. http://dx.doi.org/10.1166/jctn.2020.9192.

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This report shows the outcome by applying large scale data mining techniques on the Finnish roads. From the research study it is very difficult task to perform because the collected data have uncertainty, incomplete and error values. So the data exploration is a challenging task. The data used in the process have been collected from Finnish road administration data sets. The data used in the process have been collected from Finnish road administration data sets. The main target of our project is to look into practicability of Robust clustering, to find the associations and repeated item sets and applying apprehend methods for the analysis of road accidents. While the results display the selected mining techniques and methods were capable to the understandable patterns. To calculate the accident frequency count as a parameter /c-means algorithm is used to cluster the locations. To characterize the surface conditions association rule mining is used. data mining skills disclosed different environmental reasons associated with road accidents. Intersection on highways have been identified as a dangerous for fatal accidents.

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Zhang, Hongyi, Rabia Sehab, Sheherazade Azouigui, and Moussa Boukhnifer. "Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles." Electronics 11, no. 5 (March 3, 2022): 786. http://dx.doi.org/10.3390/electronics11050786.

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Currently, road surface conditions ahead of autonomous vehicles are not well detected by the existing sensors on those autonomous vehicles. However, driving safety should be ensured for the weather-induced road conditions for day and night. An investigation into deep learning to recognize the road surface conditions in the day is conducted using the collected data from an embedded camera on the front of the vehicles. Deep learning models have only been proven to be successful in the day, but they have not been assessed for night conditions to date. The objective of this work is to propose deep learning models to detect on-line road surface conditions caused by weather ahead of the autonomous vehicles at night with a high accuracy. For this study, different deep learning models, namely traditional CNN, SqueezeNet, VGG, ResNet, and DenseNet models, are applied with performance comparison. Considering the current limitation of existing night-time detection, reflection features of different road surfaces are investigated in this paper. According to the features, night-time databases are collected with and without ambient illumination. These databases are collected from several public videos in order to make the selected models more applicable to more scenes. In addition, selected models are trained based on a collected database. Finally, in the validation, the accuracy of these models to classify dry, wet, and snowy road surface conditions at night can be up to 94%.

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Bibi, Rozi, Yousaf Saeed, Asim Zeb, Taher M. Ghazal, Taj Rahman, Raed A. Said, Sagheer Abbas, Munir Ahmad, and Muhammad Adnan Khan. "Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–16. http://dx.doi.org/10.1155/2021/6262194.

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Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

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Brunken, Hauke, and Clemens Gühmann. "Pavement distress detection by stereo vision / Straßenzustandserkennung durch stereoskopische Bildverarbeitung." tm - Technisches Messen 86, s1 (September 1, 2019): 42–46. http://dx.doi.org/10.1515/teme-2019-0046.

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AbstractFor road maintenance up-to-date information about road conditions is important. Such information is currently expensive to obtain. Specially equipped measuring vehicles have to perform surface scans of the road, and it is unclear how to automatically Ąnd faulty sections in these scans. This research solves the problem by stereo vision with cameras mounted behind the windshield of a moving vehicle so that the system can easily be integrated into a large number of vehicles. The stereo images are processed into a depth map of the road surface. In a second step, color images from the cameras are combined with the depth map and are classified by a convolutional neural network. It is shown that the developed system is able to Ąnd defects that require knowledge about surface deformations. These defects could not have been found on monocular images. The images are taken at usual driving speed.

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Nakatsuji, Takashi, and Akira Kawamura. "Relationship Between Winter Road-Surface Conditions and Vehicular Motion: Measurements by Probe Vehicles Equipped with Global Positioning System." Transportation Research Record: Journal of the Transportation Research Board 1824, no. 1 (January 2003): 106–14. http://dx.doi.org/10.3141/1824-12.

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A major concern of drivers in winter is the current road condition. Taxis, which move ceaselessly around a wide area, have great potential as sensors for detecting road-surface conditions in a given area. To establish a method with which to estimate road conditions based on the vehicular motion of taxis, field experiments were conducted by using probe vehicles fitted with vehicular-motion sensors and a Global Positioning System device, before implementation in taxis. Preliminary analyses were performed by using data measured on a test track, urban streets, and an expressway. The slip ratio, defined as the relative difference in speed between vehicle and tire wheel, was effective in indicating how slippery the road surfaces were. Taxi vehicular-motion data were collected for more than 1 month, although unlike with probe vehicles, the wheel speed was not measured. Some features of vehicular motion specific to slippery roads were identified, and the discriminability of road conditions, whether icy or dry, without the use of wheel-speed data, was examined.

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Gui, Kang, Lin Ye, Junfeng Ge, Faouzi Alaya Cheikh, and Lizhen Huang. "Road surface condition detection utilizing resonance frequency and optical technologies." Sensors and Actuators A: Physical 297 (October 2019): 111540. http://dx.doi.org/10.1016/j.sna.2019.111540.

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Kyslytsyna, Anastasiia, Kewen Xia, Artem Kislitsyn, Isselmou Abd El Kader, and Youxi Wu. "Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks." Sensors 21, no. 21 (November 8, 2021): 7405. http://dx.doi.org/10.3390/s21217405.

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Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods.

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Bhatt, A., S. Bharadwaj, V. B. Sharma, R. Dubey, and S. Biswas. "AN OVERVIEW OF ROAD HEALTH MONITORING SYSTEM FOR RIGID PAVEMENT BY TERRESTRIAL LASER SCANNER." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 173–80. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-173-2022.

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Abstract. Structural health monitoring (SHM) applications for roads should be created in order to save finances, protect public safety, and provide long-lasting road infrastructure. The terrestrial laser scanner (TLS) will be employed in this project for collecting data, used for monitoring purposes. LiDAR camera mounted on moving vehicle generating 3D point cloud is used for monitoring purpose. Poorly maintained roads result in lower productivity, higher fuel consumption, increased mechanical wear, hazardous operating conditions, driver discomfort, and higher rolling resistances. Road management agencies suffer with pavement repair methods and the finances to keep the existing road networks in good working order. The goal of this research work is to create a low-cost smart road health monitoring system that uses camera-based monitoring and smart phone sensors to identify the road section for maintenance. We have discovered that using accelerometers for pothole detection is ideal for this application. The road patches or pot holes for 2 km area of the RGIPT campus using accelerometer is being done. The smart phone will upload the position and any kind of undulated road surface to the cloud when the vehicle passes over it. Use of accelerometer may detect internal damage of the pavements before it appears on the top surface of the road. When other vehicles move towards an irregular road surface, the cloud will issue an undulated road surface reminder to make sure that the vehicle may safely and smoothly drive through the area. The system is simply dependent on a single phone setting and uses raw accelerometer measurements, which can record irregular driving or quick brakes. The data in this system are collected from the mobile phone and sensor for monitoring and forecasting of road surface. So, every pavement defect has different classification and treatment approach, as well as severity levels.

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Rani, Bandi Mary Sowbhagya, Vasumathi Devi Majety, Chandra Shaker Pittala, Vallabhuni Vijay, Kanumalli Satya Sandeep, and Siripuri Kiran. "Road Identification Through Efficient Edge Segmentation Based on Morphological Operations." Traitement du Signal 38, no. 5 (October 31, 2021): 1503–8. http://dx.doi.org/10.18280/ts.380526.

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Road identification from high-precision images is important to programmed mapping, urban planning, and updating geographic information system (GIS) databases. Manual identification of roads is slow, costly, and prone to errors. Therefore, it is a hot topic among remote sensing experts to develop programmed techniques for road identification from satellite images. The main challenge lies in the variation of width and surface contents between roads. This paper presents a road identification and extraction strategy for satellite images. The strategy, denoted as ESMIRMO, recognizes roads in satellite images through edge segmentation, using morphological operations. Specifically, morphological operations were employed to enhance the quality of the original image, laying a good basis for accurate road detection. Next, edge segmentation was adopted to detect the road in the original image accurately. After that, the proposed strategy was compared with traditional methods. The comparison shows that our strategy could identify roads from satellite images more accurately than traditional methods, and overcome many of their limitations. Overall, our strategy manages to enhance the quality of satellite images, pinpoint roads in the enhanced images, and provide drivers and repairers with real-time information on road conditions.

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Bokade, Rushikesh. "Street Pothole and Speed breaker detection with Theft Prevention Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1008–14. http://dx.doi.org/10.22214/ijraset.2021.38531.

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Abstract: Potholes are very harmful road surface conditions that prevent a safe, secure and reliable transportation and movement of people, goods and services. Road surface obstacles such as potholes affect the safety and comfort of most road users and commuters. Bad road networks hamper the smooth movement of goods and services and contribute to the poor growth and development of the economy whiles good road networks provides access to markets and enable fast and smooth transportation of goods and services from producers to consumers. Early detection and maintenance of potholes helps to create a conducive and reliable road network that facilitates the smooth movement of people, goods and services. Transportation Plays a Big Part of our daily Lives. Every year, People are Increasingly using vehicle Especially Two-wheeler Motorcycle their Common mode of Transportation. The increases of Motorcycle users, Motorcycle theft also rampant over the year. This paper contains a survey on various potholes and speed breaker detection techniques with theft protection techniques. Keywords: Street pothole, Speed breaker, machine learning, GSM, Theft prevention system, Micro-controller.

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32

Yamada, M., K. Ueda, I. Horiba, S. Yamamoto, and S. Tsugawa. "The Road Surface Condition Detection Technique for Deployment on a Vehice." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 116–17. http://dx.doi.org/10.1299/jsmermd.2004.116_5.

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Du, Ronghua, Gang Qiu, Kai Gao, Lin Hu, and Li Liu. "Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor." Sensors 20, no. 2 (January 13, 2020): 451. http://dx.doi.org/10.3390/s20020451.

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In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the k-nearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and low-cost road maintenance.

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Tao, Liu, Li Jia, Zheng Zhi-gang, Huang Zhi, Jiang Jian, Hong Shao-you, Li You-zhi, and Wu Yin-tan. "Research on road engineering detection method based on GPR technology." E3S Web of Conferences 165 (2020): 04014. http://dx.doi.org/10.1051/e3sconf/202016504014.

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GPR is an effective non-destructive testing technology. This paper introduces its composition principle and operation method, explains the process of parameter setting and image optimization, obtains the dielectric constant of 10000 points, compares it with the density, and then obtains the uniformity distribution law of construction quality based on image. By calibrating the thickness of the road surface, the effective detection of road diseases can be realized, and the theoretical basis and practical application conditions of GPR technology can be clarified.

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35

McManus, Kerry J., Aaron S. Blicblau, Christopher J. Broadhurst, and Ashley M. S. Carter. "Real-Time Detection of Unsealed Surfaces During Skidding." Transportation Research Record: Journal of the Transportation Research Board 1819, no. 1 (January 2003): 237–43. http://dx.doi.org/10.3141/1819a-35.

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The antilock braking system (ABS) fitted to modern passenger vehicles is intended to provide reliable and efficient braking under critical road conditions or in emergency situations. Thus, ABS-equipped vehicles should remain steerable and maintain directional stability in the event of emergency braking. The ABS on vehicles operates on the principle of detection of brake lockup and release of the lockup to prevent an uncontrollable skid developing on sealed roads. However, on gravel roads or snow-covered roads braking distances can be reduced if brake lockup occurs and a wedge of gravel or snow is allowed to form in front of the wheels. The intervention of ABS prevents the wedge from forming to any significant degree, thereby extending the braking distance. An investigation was carried out of a method of discriminating between sealed and unsealed road surfaces in which a signal can be developed so that an alternative ABS algorithm can be applied specifically for gravel-covered surfaces. An attempt was made to identify and measure the buildup of gravel in front of the wheel directly, using an infrared distance-measurement sensor. Initial tests have shown that the system can provide a signal to the ABS, which will allow a timely response to enable intervention in the activation of the algorithms in the ABS.

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36

Liu, Zhen, Wenxiu Wu, Xingyu Gu, Shuwei Li, Lutai Wang, and Tianjie Zhang. "Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance." Remote Sensing 13, no. 6 (March 12, 2021): 1081. http://dx.doi.org/10.3390/rs13061081.

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Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ/km (16.94%) and 56,289 kg/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.

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Ochoa-Ruiz, Gilberto, Andrés Alonso Angulo-Murillo, Alberto Ochoa-Zezzatti, Lina María Aguilar-Lobo, Juan Antonio Vega-Fernández, and Shailendra Natraj. "An Asphalt Damage Dataset and Detection System Based on RetinaNet for Road Conditions Assessment." Applied Sciences 10, no. 11 (June 8, 2020): 3974. http://dx.doi.org/10.3390/app10113974.

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The analysis and follow up of asphalt infrastructure using image processing techniques has received increased attention recently. However, the vast majority of developments have focused only on determining the presence or absence of road damages, forgoing other more pressing concerns. Nonetheless, in order to be useful to road managers and governmental agencies, the information gathered during an inspection procedure must provide actionable insights that go beyond punctual and isolated measurements: the characteristics, type, and extent of the road damages must be effectively and automatically extracted and digitally stored, preferably using inexpensive mobile equipment. In recent years, computer vision acquisition systems have emerged as a promising solution for road damage automated inspection systems when integrated into georeferenced mobile computing devices such as smartphones. However, the artificial intelligence algorithms that power these computer vision acquisition systems have been rather limited owing to the scarcity of large and homogenized road damage datasets. In this work, we aim to contribute in bridging this gap using two strategies. First, we introduce a new and very large asphalt dataset, which incorporates a set of damages not present in previous studies, making it more robust and representative of certain damages such as potholes. This dataset is composed of 18,345 road damage images captured by a mobile phone mounted on a car, with 45,435 instances of road surface damages (linear, lateral, and alligator cracks; potholes; and various types of painting blurs). In order to generate this dataset, we obtained images from several public datasets and augmented it with crowdsourced images, which where manually annotated for further processing. The images were captured under a variety of weather and illumination conditions and a quality-aware data augmentation strategy was employed to filter out samples of poor quality, which helped in improving the performance metrics over the baseline. Second, we trained different object detection models amenable for mobile implementation with an acceptable performance for many applications. We performed an ablation study to assess the effectiveness of the quality-aware data augmentation strategy and compared our results with other recent works, achieving better accuracies (mAP) for all classes and lower inference times (3× faster).

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SATO, Masato, Hiroshi TACHIYA, Masahiro HIGUCHI, Taisei ISE, and Tomohiko SASANO. "Detection of road surface condition by measuring strains of a side surface of a tire." Proceedings of the Materials and Mechanics Conference 2016 (2016): OS14–04. http://dx.doi.org/10.1299/jsmemm.2016.os14-04.

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39

Jeong, Hye-C., Suk-T. Seo, Sang-H. Lee, In-K. Lee, and Soon-H. Kwon. "An Autonomous Mobile System based on Detection of the Road Surface Condition." Journal of Korean institute of intelligent systems 18, no. 5 (October 25, 2008): 599–604. http://dx.doi.org/10.5391/jkiis.2008.18.5.599.

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40

Ballinas-Hernández, Ana Luisa, Ivan Olmos-Pineda, and José Arturo Olvera-López. "Marked and unmarked speed bump detection for autonomous vehicles using stereo vision." Journal of Intelligent & Fuzzy Systems 42, no. 5 (March 31, 2022): 4685–97. http://dx.doi.org/10.3233/jifs-219256.

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A current challenge for autonomous vehicles is the detection of irregularities on road surfaces in order to prevent accidents; in particular, speed bump detection is an important task for safe and comfortable autonomous navigation. There are some techniques that have achieved acceptable speed bump detection under optimal road surface conditions, especially when signs are well-marked. However, in developing countries it is very common to find unmarked speed bumps and existing techniques fail. In this paper a methodology to detect both marked and unmarked speed bumps is proposed, for clearly painted speed bumps we apply local binary patterns technique to extract features from an image dataset. For unmarked speed bump detection, we apply stereo vision where point clouds obtained by the 3D reconstruction are converted to triangular meshes by applying Delaunay triangulation. A selection and extraction of the most relevant features is made to speed bump elevation on surfaces meshes. Results obtained have an important contribution and improve some of the existing techniques since the reconstruction of three-dimensional meshes provides relevant information for the detection of speed bumps by elevations on surfaces even though they are not marked.

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41

Zhang, Wei Wei, and Xiao Lin Song. "Apartitioned Approach to Real Time Lane Detection at Different Weather Conditions." Advanced Materials Research 671-674 (March 2013): 2870–74. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.2870.

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A partitioned approach to real time lane detection is proposed based on the ARM core microprocessor S3C6410. With the help of the dedicated camera interface in S3C6410, the original image can be converted to RGB format and got window-cut in hardware, leaving the target region of interest (ROI). The pixels in ROI are partitioned into two parts to deal with some hostile weather conditions when lane markings in far field are hard to be distinguished from the homogenous road surface. Hough transform is applied into the top part to utilize lane continuum, and the pixel in bottom part is detected in some fixed search bars to reduce computation complexity. Experiments show that the detection algorithm possesses real time performanceand good robustness at different weather conditions.

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42

Miraliakbari, A., M. Hahn, and H. G. Maas. "Development of a Multi-Sensor System for Road Condition Mapping." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 265–72. http://dx.doi.org/10.5194/isprsarchives-xl-1-265-2014.

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We present a concept for a vehicle based road condition mapping system using infrared spectrometers, high resolution RGB cameras and a laser scanner. Infrared spectrometry is employed to monitor the deterioration of the surface material and pavement condition, in particular by aging. High resolution RGB imaging enables automatic asphalt crack detection and provides base images for spectrometry spots. Laser scanning aims at the detection of geometrical road irregularities and pavement failures such as potholes and ruts. These three major recordings contribute to the analysis of the pavements condition. All mapping sensors are synchronised with a navigation sensor to collect geo-referenced data. The concept of road condition mapping relies on a separate analysis of the different sensor data which are related to road sections. Processing results like the percentage of the road section area related to cracks, pot holes, ruts etc. are merged to achieve an assessment for the road section. The processes for assessing deterioration from the spectrometer data, the detection of ruts from the laser data and cracks from the images are discussed in detail and outlined with some experiments.

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43

Park, Sung-Sik, Van-Than Tran, and Dong-Eun Lee. "Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection." Applied Sciences 11, no. 23 (November 26, 2021): 11229. http://dx.doi.org/10.3390/app112311229.

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Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 665 images in 720 × 720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subsets. A mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) is used to measure the performance of models. The study result shows that the mAP_0.5 of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It confirms that the YOLOv4-tiny is the best fit model for pothole detection.

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44

Kongrattanaprasert, Wuttiwat, Hideyuki Nomura, Tomoo Kamakura, and Koji Ueda. "Wavelet‐based neural networks applied to automatic detection of road surface conditions using tire noise from vehicles." Journal of the Acoustical Society of America 125, no. 4 (April 2009): 2730. http://dx.doi.org/10.1121/1.4784506.

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45

Martinelli, Alessio, Monica Meocci, Marco Dolfi, Valentina Branzi, Simone Morosi, Fabrizio Argenti, Lorenzo Berzi, and Tommaso Consumi. "Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach." Sensors 22, no. 10 (May 16, 2022): 3788. http://dx.doi.org/10.3390/s22103788.

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Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

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46

Li, Jiantao, Xinqun Zhu, Siu-Seong Law, and Bijan Samali. "A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter." International Journal of Structural Stability and Dynamics 20, no. 10 (September 2020): 2042006. http://dx.doi.org/10.1142/s0219455420420067.

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Drive-by bridge inspection using acceleration responses of a passing vehicle has great potential for bridge structural health monitoring. It is, however, known that the road surface roughness is a big challenge for the practical application of this indirect approach. This paper presents a new two-step method for the bridge damage identification from only the dynamic responses of a passing vehicle without the road surface roughness information. A state-space equation of the vehicle model is derived based on the Newmark-[Formula: see text] method. In the first step, the road surface roughness is estimated from the dynamic responses of a passing vehicle using the dual Kalman filter (DKF). In the second step, the bridge damage is identified based on the interaction force sensitivity analysis with Tikhonov regularization. A vehicle–bridge interaction model with a wireless monitoring system has been built in the laboratory. Experimental investigation has been carried out for the interaction force and bridge surface roughness identification. Results show that the proposed method is effective and reliable to identify the interaction force and bridge surface roughness. Numerical simulations have also been conducted to study the effectiveness of the proposed method for bridge damage detection. The vehicle is modeled as a 4-degrees-of-freedom half-car and the bridge is modeled as a simply-supported beam. The local bridge damage is simulated as an elemental flexural stiffness reduction. Effects of measurement noise, surface roughness and vehicle speed on the identification are discussed.The results show that the proposed drive-by inspection strategy is efficient and accurate for a quick review on the bridge conditions.

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47

KIM, Youngmin, and Namcheol BAIK. "The Method of Wet Road Surface Condition Detection With Image Processing at Night." Journal of Korean Society of Transportation 33, no. 3 (June 30, 2015): 284–93. http://dx.doi.org/10.7470/jkst.2015.33.3.284.

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48

Kim, Jonghoon, Youngmin Kim, Namcheol Baik, and Jaemoo Won. "A Development of Stereo Camera based on Mobile Road Surface Condition Detection System." Journal of the Korean Society of Road Engineers 15, no. 5 (October 15, 2013): 177–85. http://dx.doi.org/10.7855/ijhe.2013.15.5.177.

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49

Wolcott, Ryan W., and Ryan M. Eustice. "Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving." International Journal of Robotics Research 36, no. 3 (March 2017): 292–319. http://dx.doi.org/10.1177/0278364917696568.

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This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g. puddles and snowdrifts), poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional (3D) LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the [Formula: see text]-height and reflectivity distribution of the environment—which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.

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50

Kim, Dae-Hyun. "Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors." Sensors 20, no. 1 (January 6, 2020): 324. http://dx.doi.org/10.3390/s20010324.

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An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

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