## 1 Introduction  
Autonomous vehicles are an innovative mode of transportation based on advanced sensors, computer vision, and artificial intelligence technologies. They do not require human intervention, thus performing driving tasks in various environments more safely and efficiently. For autonomous vehicles, in addition to performing tasks on regular, structured road scenes, sometimes they also need to work in off-road scenes, such as battlefields, post-disaster scenes, etc. In this case, the road is usually unstructured, which is described as an off-road environment. As we can see, it is especially crucial to establish a perception algorithm to deal with autonomous vehicles driving in off-road environments, which will help the unmanned platform realize full-scene perception.  
Environmental perception is an important part of the unmanned platform. Cameras and LiDAR (light detection and ranging) are the two main sensors used to obtain environmental information. Camera-based semantic segmentation algorithms mostly rely on the texture or color features of roads, such as boundaries [1], lane lines [2], or vanishing points [3]. Some depth camera-based methods [2] use depth information as an auxiliary to perform semantic segmentation in off-road environments. Although good segmentation results have been achieved, they are not robust enough due to the influence of illumination changes. Essentially, camera-based environmental perceptions are realized based on color and texture, which are greatly affected by light, thus causing them to fail at night. In off-road environments, it is necessary to work at night, so the camera sensor is not suitable to be the main sensor. As an active detection sensor, LiDAR is also widely used in environmental perception algorithms. Compared to other onboard sensors, LiDAR can provide richer environmental information [4]. [PERSON] et al. [5] used LiDAR to perceive street light poles, and [PERSON] et al. [6] used LiDAR to greatly improve the detection distance of vehicles, pedestrians, and cyclists. Unlike a passive receiving device such as a camera, LiDAR is a suitable sensor to be used on rainy or foggy days and where the light intensity changes drastically. The advantage is that its sensitivity to the environment is extremely low. Therefore, LiDAR has been widely used in the field of environmental perception on the vehicle side. Additionally, its high robustness to environmental perception makes it very suitable for off-road environments.  
For point cloud semantic segmentation tasks and their application in off-road environments, researchers have carried out a lot of related research (in Section 2: Related Work). However, due to the lack of datasets and the high cost of labeling, the problem of low semantic segmentation accuracy and few classes in off-road environments remains to be solved. Therefore, in this paper, focusing on off-road environments, we designed the MAPC-NET. Additionally, we apply the CARLA [7] simulator for scene construction and data simulation to achieve efficient and high-precision semantic segmentation results. We summarize our contributions as:  
1. A gated fusion module is used to fuse PointTensor and Cylinder to achieve an efficient end-to-end semantic segmentation network.
2. A multi-layer receptive field module is designed to effectively realize the feature extraction of objects of different scales, especially at small scales.
3. The CARLA simulator is used to build an off-road environment dataset. In addition, linear interpolation is used to enhance the off-road dataset to improve the training effect.