The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. ...However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.
Herein, we show that an enzymatic reaction can generate peptide assemblies that sequestrate proteins to selectively kill cancer cells. A phosphopeptide bearing the antagonistic motif (AVPI) to the ...inhibitors of apoptotic proteins (IAPs) enters cancer cells and normal cells by caveolin‐dependent endocytosis and macropinocytosis, respectively. The AVPI‐bearing peptide assemblies sequestrates IAPs and releases bortezomib (BTZ), a proteasome inhibitor, in the cytosol of cancer cells, but rescues the normal cells (namely, HS‐5 cells) by trafficking the BTZ into lysosomes. Alkaline phosphatase (ALP) acts as a context‐dependent signal for trafficking the peptide/BTZ assemblies and selectively induces the death of the cancer cells. The assemblies of AVPI exhibit enhanced proteolytic resistance. This work, which utilizes the difference in endocytic uptake of enzymatically formed peptide assemblies to selectively kill cancer cells, promises a new way to develop selective cancer therapeutics.
The enzymatic self‐assembly: A proapoptotic‐peptide–bortezomib (BTZ) assembly enters cancer cells by caveolin‐dependent endocytosis and is dephosphorylated by alkaline phosphatase (ALP), releasing BTZ. The peptides sequestrate the inhibitors of apoptotic proteins (IAPs), promoting cell death. In contrast, this assembly enters normal cells by macropinocytosis and is transported to the lysosome, reducing side effects of cancer therapy for normal cells.
Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that ...greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.
To enhance the efficiency of pavement roughness measurement and reduce the cost, an integrated and wireless transfer based measuring system was developed. The proposed system can obtain vehicles ...status and location data via wireless acceleration sensors and GPS, calculate the international roughness index (IRI) by power spectral density analysis, and provide reports automatically. This paper presents the architecture of the proposed system, consisting of data collector, car mounted terminal, and information platform. Two wireless communication systems (ZigBee and 3G modules) were utilized to transfer the data and construct network between the components. The information platform implemented an acceleration-IRI model to calculate IRI, and a GPS based distance algorithm was employed to segment the measured road per 1 km. The various results are saved in an Oracle database, displayed on the digital map and made available to the mobile terminal. Several field tests of the prototype system were conducted in Huzhou, Zhejiang province in China. The results show that, compared to the laser roughness testing method, the relative error of this proposed system is less than 10%, which verifies the accuracy, effectiveness, and reliability of the proposed measuring system.
This paper presents a method to assess the load transfer efficiency (LTE) of concrete pavement joints using distributed optical vibration sensors. First, a theoretical analysis of concrete pavement ...vibration was conducted to investigate how to reflect LTE by spectral amplitude. Second, distributed optical vibration sensor (DOVS) was applied to measure vibration around joints distributedly. Third, the corresponding processing method for DOVS data was proposed to calculate the ratio of spectral amplitude from different slabs through power spectral density (PSD) analysis. Then, field tests were conducted on nine concrete pavement slabs with three different types of joints (dummy joint, rabbet joint, and dowel bars). The deflection-based method as well as the proposed vibration-based method were employed to assess the LTE of eleven joints on two different dates. The comparative analysis results indicate the deflection-based LTE (DLTE) and the ratio of PSD (RPSD) have a strong correlation (0.871) and a slight difference (<±0.03) overall. The correlation is robust in different dates and types of joints (0.844~0.88). These findings prove the accuracy and effectiveness of the proposed vibration-based method.
The pavement macro-texture and micro-texture are crucial factors for evaluating pavement performance as they have a significant correlation with friction, water film formation, and driving safety. ...During pavement construction, the macro-texture and micro-texture are significantly related to compaction operations. However, the current approach for evaluating pavement texture still relies on post-construction acceptance, with few considerations on the evolution patterns of pavement texture during the compaction process. Therefore, this study aimed to investigate the texture evolution law during compaction by implementing a laboratory compaction method. High-precision texture data from various asphalt mixtures were collected using 3D laser scanning during laboratory compaction. Macro-texture and micro-texture parameters were used to evaluate surface texture. Nineteen traditional geometric parameters were calculated at the macro-level to analyze macro-texture characteristics, while a 2D wavelet transform approach was applied at the micro-level to extract micro-texture, and the energy of each level and relative energy were calculated as indicators. This study analyzed the evolution law of parameters and found that certain parameters tend to converge. Moreover, geometric parameters and energy at lower levels of the samples could be utilized as supervising factors to regulate the compaction process.
The new generation of smart highway (NGSH) has become an irresistible global trend to improve transport efficiency and safety. The exploration of the features and framework for NGSH can guide us to ...upgrade the current highway system. This paper summarizes the fundamental features of the NGSH from the perspective of the interactive evolution of automobile industry and road transport. In line with the popularity of automated and connected vehicles, the primary technical features of the NGSH are proposed as (I) complete elements sensing, (II) cyber-physical systems, (III) cooperative vehicle-infrastructure applications, and (IV) 5th generation mobile communication technology. The corresponding physical framework and data flow are introduced, in which three data attributes (data accuracy, dimensionality, and freshness) are highlighted to describe the data requirements for various scenarios. The development path of the NGSH is further discussed in terms of the different vehicle automation levels. The characteristics of five levels of NGSH are identified from R1 to R5. Different combinations of NGSH level and vehicle automation level lead to distinct system functions. Several urgent problems in the current stage are pointed out in terms of system compatibility, standard specification, and information security. This paper provides new insights for sustainable and reproducible highway reformation, drawing some implications for NGSH design.
Deep learning has achieved promising results in pavement distress detection. However, the training model's effectiveness varies according to the data and scenarios acquired by different camera types ...and their installation positions. It is time consuming and labor intensive to recollect labeled data and retrain a new model every time the scene changes. In this paper, we propose a transfer learning pipeline to address this problem, which enables a distress detection model to be applied to other untrained scenarios. The framework consists of two main components: data transfer and model transfer. The former trains a generative adversarial network to transfer existing image data into a new scene style. Then, attentive CutMix and image melding are applied to insert distress annotations to synthesize the new scene's labeled data. After data expansion, the latter step transfers the feature extracted by the existing model to the detection application of the new scene through domain adaptation. The effects of varying degrees of knowledge transfer are also discussed. The proposed method is evaluated on two data sets from two different scenes with more than 40,000 images totally. This method can reduce the demand for training data by at least 25% when the model is applied in a new scene. With the same number of training images, the proposed method can improve the model accuracy by 26.55%.
•A semi-supervised learning method for pavement roughness detection is proposed.•Explicit calculations of IRI using power spectral density analysis are included.•Confidences of parameters estimation ...are considered in the algorithm.•Coupled impact of the sampling rate and vehicle quantity is explored.•Model performance is tested by both field tests and simulation tests.
Rapid measurements of large-scale pavement roughness have long been a hot topic in pavement condition evaluation and maintenance. Most traditional methods rely on dedicated devices, such as laser, Lidar and so on, which should be set up on customized vehicles. With the rapid development of sensing technology, vehicles owned by the general public are empowered with the ability to collect vibration measurements themselves. This crowdsourced dataset is convenient, extensive coverage, inexpensive, and has high sampling frequency, making it a suitable source for large-scale pavement roughness evaluation. However, vehicle information is missing for these data due to privacy protection, which renders them quite difficult to directly use with traditional model-based methods. Thus, in this paper, we propose a semi-supervised learning (SSL) model to deal with the problem of incomplete data and multi-vehicle data fusion. A mathematical derivation of the ‘international roughness index’ (IRI) using in-car vibrations is established. Furthermore, given the multi-vehicle scenario, a self-training model is designed to iteratively estimate IRIs in a roadway network. Both the confidences of the vehicle parameters and IRI estimation are considered in the algorithm to improve its reliability and robustness. A full-car simulation model is constructed to verify the effectiveness of the proposed model. The results show that the overall relative error is less than 10% for 50 road sections in the network, which is a significant improvement compared to traditional multi-vehicle average models. The errors of the SSL model are found to be significantly dependent on the iteration order. Based on the proposed model, the coupled impact of the sampling rate and vehicle quantity on the model’s accuracy is further discussed. The proposed approach provides new insights into large-scale pavement roughness measurements.
The pavement skid resistance is of vital importance for road safety and maintenance. The existing studies mainly calculates various parameters of pavement texture to evaluate the skid resistance. ...However, most researchers mainly calculate the whole texture parameters, but rarely consider which part of the texture is actually involved in the friction. This paper studies the measurement and calculation of the tire-pavement contact area and its influence on the skid resistance prediction. In this study, we proposed a set of methods to predict the rubber-contacted pavement area percentage and to extract the rubber-contacted texture. Besides, the skid resistance prediction models based on high-resolution three-dimensional (3D) surface data of the whole and the contacted texture are built and compared. The results show that when the rubber-contacted percentage is set at 20% or ̃% (the predicted percentages), the accuracy of the prediction model is higher. It is demonstrated that the performance of the skid resistance prediction will be improved by extracting the contacted texture. Also, the amount of data can be considerably reduced, which will effectively relieves computational cost.
•A novel method is proposed for measuring friction contact area based on 3D scanners.•The percentage of friction contact area mainly ranges from 14% to 20%.•The friction-prediction models perform better using the predicted contact area.
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