School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea.
Department of Computer Software Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Sensors (Basel). 2020 Oct 22;20(21):5999. doi: 10.3390/s20215999.
In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.
近年来，技术进步对自动驾驶汽车的发展产生了积极的影响。电动汽车的发展、先进传感器的开发以及人工智能的进步为学术界和工业界提供了必要的工具，使他们能够开发出增强道路安全和提高交通效率的自动驾驶汽车原型。自动驾驶汽车传感器的大量部署，使得消费者使用的成本效益降低。本工作专注于使用有限数量的传感器套件开发全栈自动驾驶汽车。自动驾驶汽车的架构方面分为四层，包括传感器层、感知层、规划层和控制层。在传感器层，展示了自动驾驶汽车上的外部传感器和内部传感器的集成。感知层包括使用外部传感器对环境进行本地化和检测的感知。在规划层，通过结合路线信息、速度重新规划和障碍物回避，说明了任务和运动规划的算法。控制层构成了自动驾驶汽车的横向和纵向控制。为了验证所提出的系统，在不受约束的环境中对自动驾驶汽车进行了测试。实验结果表明了每个模块的有效性，包括本地化、目标检测、任务和运动规划、障碍物回避、速度重新规划、横向和纵向控制。此外，为了展示自动驾驶汽车的实验验证和应用方面，将所提出的系统作为自动驾驶出租车服务进行了测试。
