Connected Autonomous Vehicles (CAVs) are key components of the Intelligent Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs) are indispensable tools for a wide range of applications such as disaster response, automated mining, agriculture, military operations, search and rescue missions, and planetary exploration. Experimental validation is a requisite for CAV and AGV research, but requires a large, safe experimental environment when using full-size vehicles which is time-consuming and expensive. To address these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled vehicle platform for Connected autonomy and All-terrain Research), an open-source, cost-effective proportionally one-tenth scaled experimental vehicle platform governed by the same physics as a full-size on-road vehicle. XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed Controller (ESC) with drivers written for both versions of the Robot Operating System (ROS 1 & ROS 2) to facilitate experimental CAV and AGV perception, motion planning and control research, that incorporate state-of-the-art computationally expensive algorithms such as Deep Reinforcement Learning (DRL). XTENTH-CAR is designed for compact experimental environments, and aims to increase the accessibility of experimental CAV and AGV research with low upfront costs, and complete Autonomous Vehicle (AV) hardware and software architectures similar to the full-sized X-CAR experimental vehicle platform, enabling efficient cross-platform development between small-scale and full-scale vehicles.

翻译：网联自动驾驶车辆（CAV）是智能交通系统（ITS）的关键组成部分，而全地形自主地面车辆（AGV）在灾害响应、自动化采矿、农业、军事行动、搜索救援任务及行星探测等广泛应用中不可或缺。实验验证是CAV与AGV研究的必要环节，但使用全尺寸车辆需要庞大且安全的实验环境，耗时且成本高昂。为解决上述挑战，我们开发了XTENTH-CAR（面向网联自动驾驶与全地形研究的十分之一缩比实验车辆平台）——一种开源、成本效益显著的十分之一比例缩比实验车辆平台，其物理特性与全尺寸道路车辆保持一致。该平台搭载业界领先的NVIDIA Jetson AGX Orin系统模组（SOM）、立体相机、二维激光雷达及开源电子调速器（ESC），并配备针对机器人操作系统双版本（ROS 1与ROS 2）编写的驱动程序，以支持融合深度强化学习（DRL）等前沿计算密集型算法的CAV与AGV感知、运动规划与控制实验研究。XTENTH-CAR专为紧凑型实验环境设计，旨在以低初始成本提升CAV与AGV实验研究的可及性，其完整的自动驾驶车辆（AV）软硬件架构与全尺寸X-CAR实验车辆平台高度一致，从而支持小规模与全尺寸车辆间的高效跨平台开发。