Development of an enhanced simulated driver autonomous vehicle******

这项研究的长期目标是开发一种自动驾驶汽车（货运拖车），与现有产品不同，它可以在街道上行驶，而不需要驾驶员坐在方向盘后面。拟议的工作需要探索最先进的技术，并达到一个小规模的拖车硬件和软件原型。** 实现这一目标面临的主要挑战如下：**1。 设想的跟随车辆，FV或货物拖车，是自主车辆。在这个项目中，我们可以使用现有的自动驾驶汽车进行一些修改或开发我们自己的。引导车辆、LV或牵引车辆不通过传统的承载物理链路牵引FV。** 2. 为了满足自动驾驶汽车的监管条件，需要一个全职的驾驶员在方向盘后面，我们需要开发一种替代方法来模拟驾驶员。为此，我们使用LV驱动程序的行为作为一种资源，通过一个非承载的物理链接与7自由度。逆运动学将用于计算LV和FV的位置。** 3. 强化学习（RL）算法与机器学习、计算机视觉和图像处理一起被认为是解决复杂序列决策/控制问题的有效方法。

The long-term goal of this research is to develop an autonomous vehicle (cargo trailer) that, unlike existing products, can drive on the street without requiring a driver sitting behind the wheel. The proposed work entails exploring the state of the art and arriving at a small-scale trailer hardware and software prototype. **There are major challenges toward this objective, as follows,**1. The envisioned following vehicle, FV, or cargo trailer, is an autonomous vehicle. In this project we may use an existing autonomous vehicle with some modification or develop our own. The lead vehicle, LV, or towing vehicle does not pull the FV through a conventional load-bearing physical link.**2. To satisfy the regulatory condition for autonomous vehicles that require a fulltime attending operator behind the wheel, we need to develop an alternate way to simulate a driver. For this purpose, we use the LV driver's behaviour as a resource through a non-load-bearing physical link with 7 degrees of freedom. Inverse kinematics will be used to calculate the locations of the LV and FV.**3. Reinforcement learning (RL) algorithms, along with machine learning, computer vision and image processing, are considered as effective approaches to solve complex sequential decision/control problems.