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

这项研究的长期目标是开发一种与现有产品不同的自动驾驶汽车（货运拖车），它可以在不需要驾驶员坐在方向盘后面的情况下在街道上行驶。提议的工作需要探索艺术的状态，并到达一个小型拖车的硬件和软件原型。实现这一目标的主要挑战如下：设想的后续车辆，FV或货运拖车，是一辆自动驾驶汽车。在这个项目中，我们可能会使用现有的自动驾驶汽车进行一些改装，或者开发我们自己的自动驾驶汽车。牵引车辆、低压或拖曳车辆不通过传统的承重物理链路牵引FV。为了满足自动驾驶汽车需要全职驾驶员的监管条件，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资源，通过具有7个自由度的非承重物理链路。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顺序决策/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.