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

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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.