ER4105: The Scientific Principles and Concepts of Intelligent Machines - Engineering Assignment Help

Assignment Task:

Learning Outcome to be assessed: 

1 Comprehensive understanding of the scientific principles and concepts relevant to and Intelligent Machines engineering

2 Awareness of relevant regulatory requirements governing engineering activities in the context of Intelligent Machines engineering

3 Apply and investigate new and emerging technologies

4 Plan self-learning and improve performance, as the foundation for lifelong learning

Task: 

‘BlueBird Electric` is an electronic company that develops diverse types of innovative autonomous robotic units for different industries to support a wide range of systems. One of the future objectives of BlueBird Electric is to be a leading company on autonomous technologies, particularly on autonomous driverless cars. 

You have been asked as an engineer in the ‘BlueBird Electric’ company to develop an autonomous driverless car application. You can use any programming language you are comfortable with. In this manner: 

Provide a detailed, professional report for your company that contains the following:

1- A review/insight of 

i) Current autonomous driverless car applications along with their main components employed by three automobile companies, 

ii) Why and how Deep Learning (DL) and Reinforcement Learning (RL) techniques are recently being forged together to result in better Artificial Intelligence (AI) applications by analysing successful real-world application, 

iii) Likely use of this forging approach in autonomous driverless cars within smart transportation and smart city, 

iv) How 5G communication technologies will revolutionise the autonomous driverless cars within smart transportation and smart city. 

2- Development of a DL application that satisfies/includes the following features: 

i. Build a faster R-CNN (for specifying detected objects with bounding boxes) or use a provided MATLAB code. 

ii. The software algorithm should either be trained with at least 10 traffic signs. Detection accuracy is expected to be greater than 95% regarding sensitivity and specificity values, or to be trained for one traffic sign like stop sign. For the latter case, the theoretical background for each command, like anchor boxes, etc. 

iii. Use one camera as a sensor (computer vision), 

iv. Incorporate natural language processing (i.e., text-to-speech) into the application, 

v. Provide driving information as a speech output along with the bounding box for the detected objects on camera images, 

vi. The software algorithm is required to detect road lanes drawn by white lines while performing other tasks, which requires multi-threaded programming,

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