Self-correcting neural network in road pavement diagnostics

We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.

Section: Introductionmentioning

confidence: 99%

Vehicle Classification Using the Convolution Neural Network Approach

Trivedi¹,

Devi²,

Dave³

2021

“…Every day around the world, road accidents occur. Driver errors, poor transportation system and poor road infrastructure contribute to these accidents [1][2][3][4][5]. Accumulated pollutants on roads surfaces decrease skid resistance significantly and increase the risk of accidents.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effect of Various Surficial Pollutants and Environmental Condition on Surface Friction Performance of Road Pavement

Khabiri¹,

Meybodi²,

Montazeri³

2021

Skip resistance of asphalt is an important parameter that can influence the safety of drivers on roads. Although there is a linear relationship between slipping on road surfaces and accidents, the impacts of pollutants for decreasing friction of roads is clear to researchers. Moisture and temperature influence friction and safety. In this research in SMA samples, three different gradations with the maximum nominal sizes of 19, 12.5 and 9.5, based on international standards were used. For polluting the surface, five materials that are found on roads were used, including fine-grained soil, sand, oil, soot and rubber powder. To measure the skip resistance, the British pendulum tester was used and for analysing macro-texture, the sand patch method was used. The results of this research showed that by increasing the maximum nominal size of aggregates, the depth of macro-texture in surfaces are grown and this is due to the decrease of fine aggregates in larger gradations. Because of the higher flexibility of pure bitumen, the applied compression pressure on rigid aggregates can cause indentations in the substrate and result in declining the roughness height of aggregates in the mixed surface. This leads to declining the hysteresis part of friction by increasing temperature.

“…The comfort of driving a car depends on the road surface, road condition and the construction of the car itself [14,18,19,21]. The last of these factors can reduce the adverse effects of the other two.…”

Section: Introductionmentioning

confidence: 99%

Kinematic Analysis of Trapezoidal Suspension

Hajdučík¹,

Škrabala²,

Medvecky³

et al. 2019

This article deals with the kinematic analysis of trapezoidal suspension. Specifically, it focuses on the behaviour of the chassis when obstacle crossing was monitored. Our team is developing an autonomous mountain vehicle that will be equipped with different working adapters such as a cutter bar and a picker. The device was designed for work on slopes, hence, must be able to overcome certain natural obstacles. This implies the need to analyse the wheel suspension kinematics. The vehicle was built on a trapezoidal suspension, which has proven to be the most suitable option with respect to operating conditions. From the results obtained, it was possible to analyse the driving characteristics of the obstacle, track the rollover limits and overall safety of operation.