The purpose of this research is to identify the influence and advantages of artificial intelligence in the automotive industry. This research uses descriptive research method where data is obtained from existing facts. The results of this research explain how important driverless cars technology is in the application of artificial intelligence in the automotive industry, and how the advantages and disadvantages of driverless cars technology are applied nowadays. The results of the research were obtained because of the increase of human needs in the era of technology industry 4.0, so some companies developed driverless cars technology. This research was conducted to discuss the influence of artificial intelligence in the automotive industry by applying driverless cars technology.

Pavement conditions could be degraded throughout its service life. Hence, a pavement management system is needed to ensure pavement performance according to its design life. To support a reliable pavement, a pavement condition survey needs to be conducted most effective and practical way. One of the technologies used in pavement condition surveys is the Unmanned Aerial Vehicle (UAV) or known as drone. The use of UAVs for road maintenance will reduce the cost and time and with the 3D model, the accuracy level is at the centimeter level which indicates UAVs are an excellent and promising tool for road work.In this study, a PCI method of pavement condition evaluationwill be used to asses pavement through both manually surveyed and 3D model calculation with the use of Agisoft application. Later on, statistical test will be carried out such as ANOVA and correlation to determine the relationship between pavement condition obtained from two different survey method as well as the comparison in identifying pavement distresss, PCI value and pavement condition. In result, those two methods can identify identical types of damage, with a fairly high percentage of > 60%. While, manual method could identify a higher percentage of degree of severity than 3D model with the help of application. Themanual of pavement condition assessment can identify in more detail than the Agisoft Metashape application. Based on statistical tests,the two methods used show a close and unidirectional relationship.

The implementation of UAVs in Indonesia on road infrastructure projects still widely used at the planning stage of new road routes and the supervision stage to determine the progress of the work. Meanwhile, at the post-work monitoring stage, the UAV is only used to identify and inventory the pavement distress in determining the type of damage, not followed by the further pavement assessment. This study aims to determine the type of damage and the dimensions (area and depth) of the damage and determine the level of pavement distress. Data acquisition was carried out by taking direct photos using a drone of some damage to the road segment based on different road gradients in the study location, which was a 1% -6% slope. Photos taken from the drone are processed according to the rules of photogrammetry to produce an orthophoto as a 3D model. Furthermore, identification and inventory of types of pavement distress are carried out, as well as calculating the dimensions (area and depth) of pavement distress on photo maps. A minimum value difference of 0.3cm to 3cm is obtained from the calculation of the dimensions of the pavement distress in the model against the results of measurements in the field. The level of precision of the dimensions of the pavement distress in the model is not affected by the gradient of the road, meaning that the 3D model does not make the value of the depth of the pothole on a high road gradient more precise.