A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation data.The classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS domain.Recently,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods.The ability to extract important features is vital in making RST classification more accurate.This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models.We used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification performance.Comparative experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art models.The proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt roads.Moreover,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

Robust Identification of Road Surface Condition Based on Ego‑Vehicle Trajectory Reckoning

The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing methods are solely based on a dynamics-based method or an image-based method,which is susceptible to road excitation limitations and interference from the external environment.Therefore,this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will expe-rience.First,a road feature extraction model based on multi-task learning is conducted,which can simultaneously segment the drivable area and road cast shadow.Second,the optimized candidate regions of interest are classified with confidence levels by ShuffleNet.Considering environmental interference,candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results.Finally,the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels.The performance of the entire framework is verified on a specific dataset with shadow and split curve roads.The results reveal that the proposed method can identify the RSC with accurate predictions in real time.

Sustainability of lime stabilized road subgrade in mountainous regions of Türkiye

In this paper the use of lime stabilized subgrade for low volume roads in two regions with high mountains and different frost penetration conditions in Türkiye was investigated in terms of design,performance,and cost.Pavements on unstabilized and stabilized subgrade were designed for two regions(Izmir and Van),covering all climate variations.The resilient modulus of the lime stabilized subgrade with different soil pulverization levels for non-freezing and freezing conditions were taken from a previous laboratory study.Frost effects were considered in pavement design using two different approaches,including limited subgrade frost penetration method and reduced subgrade strength method.Detailed application and evaluation were performed for all steps.Lime stabilized subgrades significantly reduced the thickness of base courses,and the benefit of lime stabilization was highly dependent on soil pulverization level.A detailed cost analysis on the unstabilized and stabilized cases found that the use of lime stabilization in the subgrade provided significant initial cost savings.Comparative analysis by using the AASHTO(1993)method and KENPAVE software,and quantity effect of soil pulverization level on the performance of low volume roads from a service life perspective,show that subgrade resilient modulus can be estimated.It is also possible to make correct performance estimation in the field.The results of the study show that lime stabilization is a good solution for low volume roads in the mountainous regions of Türkiye.

Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter(PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks(BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under highvolume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.

Highway icing time prediction with deep learning approaches based on data from road sensors

In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there are few studies on highway icing time prediction due to the scarcity and complexity of data.In this study,variables of icing temperature,friction,ice percentage,road surface temperature,water film height,saline concentration,and road condition were collected by road sensors distributed on a highway in China.A large-scale time series highway surface information dataset called HighwayIce is formed.Furthermore,a deep learning approach called IceAlarm,composed of long short-term memory neural network(LSTM),multilayer perceptron(MLP),and residual connection,has been developed to predict when the highway will ice.The LSTM is used to process dynamic variables,the MLP is used to process static variables,and the fully-connected layers with residual connections are used to make a deep fusion.The experimental results show that the average mean absolute error before icing using the IceAlarm model is about 6min and outperforms all baseline models.The HighwayIce dataset and IceAlarm model can help improve the prediction accuracy and efficiency of forecasting real-world road icing time,therefore reducing the impact of icy road conditions on traffic.

Why Dirt Roads Develop A Washboard Surface

在沙土路面上行驶的车辆往往会使路面形成象搓衣板一样硌硌楞楞的痕迹,无论是小型私家车还是大型卡车经过时都因为颠簸而苦不堪言,而路政部门似乎除了定期用推土机将路面铲平之外别无他法。这是轮胎压出的印迹还是由于别的原因出现的呢?什么情况下可以避免使路面成为搓衣板呢?英国剑桥的一组研究人员最近通过实验室模拟和电脑分析找出了答案。

Characterization of road surfacing aggregates based on their mineralogical fingerprint

To ensure the safety of infrastructure users,the long-term skid resistance is a crucial factor and is determined in large by the mineralogical and morphological characteristics of surfacing aggregate.Most studies have investigated these aggregate properties separately without considering the interrelation between one another.The objective of this study is to consider the morphological characteristics as well as the mineralogical fingerprint of aggregate to develop an innovative approach to optimize the aggregate selection process.The investigations are based on 11 different aggregate types with a broad range of mineralogy,commonly used in Germany.The long-term influence of polishing and wearing on the surface aggregate was simulated by means of the Aachen Polishing Machine and the MicroDeval test respectively.To evaluate the impact of these tests,the aggregate shape was characterized by means of an imaging system called Aggregate Image Measurement System while the skid resistance of aggregates was evaluated with the British Pendulum Test.The test results show that the quartz and calcite are the key crystals to determine the anti-wear resistance of aggregates.A correlation between the skid resistance,morphological properties and mineralogy is derived,which proves the mineralogical fingerprint technology is practical for characterization of aggregates used in pavement surface layers.