Development of Platooning Technology with Road Condition Monitoring

In this paper, we present a complete platooning system using a time-delay algorithm. The platooning is achieved by measuring the driver inputs from the lead vehicle and sending these inputs to the trail vehicle with a time-delay so that the trail vehicle can exactly mimic the motion of the lead vehicle. This system also does a road condition monitor as an add-on benefit which will help in assisting the driver of the trail vehicle/vehicle which takes the same path. The function of this monitoring system is to analyse the road surface using a lead vehicle and acquire sensor data, this acquired sensor data helps in assisting drivers who take the same track. The combination of both this platooning method and road condition monitoring system could potentially reduce the current risk of utilising this semi-automated driving system. Index terms: Platooning, Semi-automated driving, Road condition monitoring, Time-delay algorithm.

Low Cost Road Health Monitoring System: A Case of Flexible Pavements

A healthy road network plays a significant role in the socio-economic development of any country. Road management authorities struggle with pavement repair approaches and the finances to keep the existing road network to its best functionality. It has been observed that real-time road condition monitoring can drastically reduce road and vehicle maintenance expenses. There are various methods to analyze road health, but most are either expensive, costly, time-consuming, labor-intensive, or imprecise. This study aims to design a low-cost smart road health monitoring system to identify the road section for maintenance. An automized sensor-based system is developed to assist the road sections for repair and rehabilitation. The proposed system is mounted in a vehicle and the data have been collected for a more than 1000 km road network. The data have been processed using SPSS, and it shows that the proposed system is adequate for detecting the road quality. It is concluded that the proposed system can identify the vulnerable sections to add to the pavement maintenance plan. In the future, the created application can be launched as a smart citizen app where each car driver can install this application and can monitor the road quality automatically.

Optimasi travel speed alat angkut area North West 2 di PT Saptaindra Sejati

Di area Pit North West 2 yang terletak di Tutupan area Low Wall, pada saat ini kondisi jalan angkut yang kurang baik dan belum memenuhi standar parameter geometri jalan yang berpengaruh pada travel speed serta sistem lalulintas yang kurang aman yang berpengaruh pada pada safety lalulintas. Adapun beberapa geometri jalan angkut yang masih belum memenuhi desain standar parameter. Geometri jalan angkut aktual di area North West 2 meliputi lebar jalan lurus antara 21.69 – 30.90 meter, lebar jalan tikungan sebesar 30.17 meter, superelevasi sebesar - 0.6% dengan arah kemiringan terbalik serta cross slope yang terbentuk yaitu single cross slope dan masih ada yang kurang dari 2%. Road condition monitoring atau problem jalan yang ditemukan setiap minggu, lebih dominan pada banyaknya spoil yang mengakibatkan penyempitan jalan serta adanya jalan berdebu tebal yang juga berpengaruh pada lalulintas.Metode yang digunakan pada penelitian ini dengan cara peninjauan lapangan untuk melakukan pengamatan secara langsung terhadap situasi, kondisi, dan aktifitas di lokasi penelitian dan didasarkan pada metode pengukuran aktual di lapangan. Melakukan perhitungan geometri jalan berdasarkan rumus Suwandhi 2004, menganalisis problem jalan berdasarkan 5R (Ringkas, rapi, resik, rawat dan rajin) standar dari perusahaan serta membuat perbandingan travel speed pada bulan Oktober 2017 dengan bulan November 2017.Adapun rekomendasi desain untuk perencanaan geometri jalan angkut yang sesuai untuk dilewati dump truck Caterpillar 789 C adalah lebar jalan pada jalan lurus 28 meter, lebar jalan tikungan 34 meter, superelevasi maksimal 5%, cross slope 2%-4% dan grade jalan maksimum 8%. Alternatif solusi yang digunakan adalah alternatif A dengan melakukan pelebaran jalan dan menstandarkan geometri jalan angkut. Selalu melakukan 5R pada tiap minggunya serta selalu memperhatikan dan mematuhi rambu-rambu keselamatan kerja yang aman agar tidak terjadinya kecelakaan lalulintas selama kegiatan berproduksi berlangsung. Nilai travel speed pada bulan Oktober 2017 sebesar 18.06 km/jam dan pada bulan November 2017 sebesar 20.69 km/jam. Kata-kata kunci: safety, superel­evasi, cross slope, grade, road condition monitoring

The development of integrated road condition monitoring system for developing countries using smartphone sensors and dashcam in vehicles

In developing countries like Timor-Leste, regular road condition monitoring is a significant subject not only for maintaining road quality but also for a national plan of road network construction. The sophisticated equipment for road surface inspection is so expensive that it is difficult to introduce them in developing countries, and the monitoring is usually achieved by manual operation. On the other hand, the utilization of ICT devices such as smartphones has gained much attention in recent years, especially in developing countries because the penetration rate of the smartphone is remarkably increasing even in developing countries. The smartphones equip various high precision sensors, i.e., accelerometers, gyroscopes, GPS, and so on, in the small body in low price. In this project, we are developing an integrated road condition monitoring system that consists of smartphones, dashcams, and a server. There are similar trials in advanced countries but not so many in developing countries. This system assumes to be used in developing countries. The system is very low cost and does not require trained specialists in the field side. The items that are automatically inspected in this system were carefully selected with the local ministry of public works and include paved and unpaved classification, road roughness, road width, detection and size estimation of potholes, bumps, etc., at present. All the inspected items are visualized in Google Maps, Open Street Map, or QGIS with GPS information. The survey results are collected on a server and updated to more accurate values by the repeated surveys. On the analysis, we use several state-of-the-art machine learning and deep learning techniques. In this paper, we summarize related works and introduce this project’s target and framework, which especially focused on the developing countries, and achievements of each of our tasks.

Development of automatic road width and pothole size estimation method from dashcam video for under developing countries

Road condition monitoring usually requires extremely expensive special vehicles, equipment, or many human resources. On the other hand, with the development of ICT and data science technologies in recent years, there are several research trials in which the heavy technical tasks of road asset condition monitoring are replaced by automatic inspection systems consisting of common devices such as smartphones and dashcam videos. As the system consists of low-price devices, it also suitable for developing countries. However, there are many differences in the situation and the inspection items on road condition monitoring between advanced countries and developing countries. There are few trials to develop such a road condition monitoring system in developing countries. Our project is developing an integrated road condition monitoring system focusing on developing countries like Timor-Leste. In developing countries, many parts of the road are still unpaved, and the “road width” is an important item to be inspected. In this paper, we discuss the road width and pothole size estimation as a part of the integrated system we are developing. We survey the road width of both paved and unpaved roads. We use a common dashcam to take video along the road. The estimated values are integrated into a database with GPS information and visualized in Google Map, QGIS, or the original visualization system which we developed. To estimate the real width of the road and pothole size, we need to transform the captured forward view image of dashcam video into bird’s-eye-view. For the transformation, we need to estimate the vanishing point in a captured image. However, unlike the advanced countries, it is difficult to detect the vanishing point in developing countries because there are usually no straight lines in the images in the unpaved road of the province. In this study, we propose to use the optical flow method to detect the vanishing point in the rural road. To identify the area of road and the existence of potholes in images, we apply state-of-the-art semantic segmentation using deep learning.

Smartphone-enabled road condition monitoring: from accelerations to road roughness and excess energy dissipation

We develop a framework to address the shortcomings of current smartphone-based approaches for road roughness sensing and monitoring through combining vehicle dynamics, random vibration theory and a two-layer inverse analysis. The proposed approach uses in-cabin recordings of the vehicle’s vertical acceleration measured by a smartphone positioned inside the car for the estimation of road roughness. The mechanistic road roughness–vehicle interaction model at the core of the proposed framework links the frequency spectrum of the vehicle’s vertical acceleration to the road roughness power spectral density and lends itself to the quantitative characterization of roughness-induced energy dissipation. We demonstrate that the measure of roughness provided by the stochastic model of car dynamics interacting with a rough road is fully compatible, in a statistical sense, with the spatial but deterministic definition of road roughness, and validate the identification strategy that originates from it against laser measurements of road roughness. The critical crowdsourcing features of the proposed framework, such as the marginal impact of phone position and transferability, are examined and its utility to meld with big data analytics to identify the class of vehicles travelling on a roadway network is demonstrated.

Evaluation of Road Service Performance Based on Human Perception of Vibration While Driving Vehicle

Road surface monitoring is a significant issue in providing smooth road infrastructure for vehicles, and the key to road condition monitoring is to detect road potholes that affect driving comfort and transportation safety. This paper presents a simple, efficient, and accurate way to evaluate road service performance based on the acquisition of road vibration data by vibration sensors installed in vehicles. Inspired by the discrete fast Fourier transform, the vibration acceleration is processed, and the RMS value of vibration acceleration at 1/2 octave is calculated, after which the road vibration level is calculated. The vibration level is optimized according to the human body’s sensitivity to different frequencies of vibration, resulting in road service performance indicators that can reflect the human body’s real feelings. According to the road service performance index values on the road grading, combined with GPS data on the electronic map color block labeling, the results obtained for the road condition warning, road maintenance, driver route selection have an important significance.