## 2 Method
### Autonomous Vehicle Pipeline and Vehicle Setup
#### 2.1.1 Environment Perception  
As represented in Figure 1, the proposed environment perception pipeline is performed with a Velodyne VLP-16 LiDAR sensor, a Stereolabs ZED stereocamera, and a nVIDIA Jetson Xavier high-performance computing platform. In detail, the Velodyne VLP-16 LiDAR sensor is mounted onto the front wing of the vehicle at a fixed height equal to 0.1 m from the ground. The Stereolabs ZED stereocamera sensor is mounted at a height of 1.05 m from the ground and it is fixed to the vehicle's rollbar, as represented in Figure 1. The NVIDIA Jetson Xavier high-performance computing platform is placed inside the vehicle's monocoque, fixed to its right side. Moreover, a proper wiring system has been set up to correctly interface and supply the sensors to the computing platform. The Velodyne VLP-16 LiDAR sensor provides a full 360-degree point cloud of the surrounding environment at a 10 Hz frequency to obtain an accurate real-time data reconstruction recorded by 16 light channels. It ranges up to 100 m with 30\({}^{\circ}\) vertical field-of-view  
Figure 2: Overall autonomous pipeline and PIL architecture considering either Raspberry PI model 4B or Speedgoat Real-Time Target Machine model Baseline. (FOV) and an angular resolution up to 0.1\({}^{\circ}\) in the horizontal plane [32]. The LiDAR sensor is connected to the computing platform with embedded GPUs through an Ethernet connection. Specifically, the computing platform creates a ROS network, which allows to process the information streaming from the LIDAR-based sensor. The Stereolabs ZED stereocamera is connected via 3.0 USB port to the computing platform. The considered stereocamera features stereo 2 K cameras with dual 4 MP RGB sensors. It is used as it is capable of accurately recording dense depth map information using triangulation from the geometric model of non-distorted rectified cameras up to 10 m in front of the vehicle [33]. To this end, left and right video frames are intrinsically synchronized and streamed so that several configuration parameters--resolution, brightness, contrast, and saturation--can be tuned properly [33]. Specifically, the camera is used in the high-definition 1080 mode (HD1080) at 30 Frame Per Second (FPS). The NVIDIA Jetson AGX Xavier is an embedded Linux high-performance computing platform with embedded GPUs with 32 TOPS of peak computational power in less than 50 W of needed power. The retained high-performance computing platform enables intelligent vehicles with end-to-end autonomous capabilities as it is based on the most complex System-on-Chip (SoC) ever created up to 2018, thus enabling any complete artificial intelligence software stack [34]. The driving environment is properly structured with traffic cones according to the rules listed in [35] for the purpose of FSD competitions. In fact, each traffic cone has a height equal to 0.325 m and a square base, with a side length equal to 0.228 m. The cones of the right lane boundary are yellow with a black stripe, while the right lane boundary is built with blue cones with a white stripe. Bigger orange cones indicate the starting and the ending points of the track. In this driving scenario, the LiDAR sensor records point clouds at a frequency equal to 10 Hz consisting of thousands of 3D point cloud during motion. Each point cloud contains the distance of each point in the 3D space along with the intensity of the reflected light in that point. Then, the raw point cloud is filtered by removing all the points out of the region-of-interest (ROI). Furthermore, a ground plane filtering segmentation algorithm is then applied to the raw point cloud in the considered ROI. This operation is performed in order to remove all the points belonging to the ground which can badly affect the proposed object detection method. Therefore, a clustering algorithm is applied to the filtered point cloud, then the distance to the detected obstacles is finally estimated.