<![CDATA[A thermal camera can function well in various environmental conditions with low visibility and high contrast. Thermal cameras can be used to identify objects through semantic segmentation. The image obtained by the thermal camera on the Autonomous Electric Vehicle (AEV) can be used to segment the location of cars, roads, trees, buildings, etc. at each pixel. Fully Convolutional Network (FCN)-ResNet-50 is one of the ResNet models that can be used to segment roads. The results of the AEV route segmentation are then used to determine the steering direction. The process takes place by utilizing the OpenCV library to determine the Region of Interest (ROI), changing the HueSturation-Value (HSV) value. This process is used to change the road segmentation to white, while the other segmentation will change color to black. Because the white color has a value of 255, andthe black color has a value of 0, it can be calculated and obtained as a comparison of the values of each pixel of the road segmentation image between the right and left sides of the road segmentation image. From the research results, the guidance system through determining the direction of the steering based on the results of road segmentation using the results of deploying the FCN-Resnet-50 model was successfully carried out through several stages, such as determining the Region of Interest (ROI), changing the Hue-Sturation-Value (HSV) value, etc. The error in giving steering recommendations is caused by the FCN-Resnet-50 model used which still has a lot of segmentation faults.Keywords: Autonomous electric vehicles, Guidance System, FCN-Resnet-50, Steering, Semantic Segmentation]]>
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