ER4105 Intelligent Machines

Your assignment must be submitted electronically via BlackBoard (Assignments tab) by the submission time. The report should be contained in a Word document, or PDF No other means of submission will be accepted. The software code will be submitted independently.

Any assignment submitted late, but within 5 working days of the deadline, will be given a maximum mark of 50%. Assignments submitted more than 5 working days after the deadline will not be marked, and a mark of 0% will be recorded.

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

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

1- A review/insight of

i) Describe different machine learning methods that had been utilized in the AV navigation,

ii) What is the advantage that the reinforcement has over the other machine learning techniques that made it a potential method in AV navigation?

iii) How the AV will be integrated in smart city such that it will utilize the available updated data during navigation.

iv) Smart parking system is a potential application in smart city, describe its operation and discuss its integration in the era of AV.

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

i. Build a YOLO version2 (for specifying detected objects with bounding boxes) or use a provided MATLAB code.

ii. An image pretrained model should be fine-tuned to be trained for car boundary detection. The theoretical background for each command, like anchor boxes, etc. must be included.

iii. Provide the necessary code to test your model with a camera as a sensor (computer vision),

iv. Provide a practical method to calculate a relative distance of the next car that the model can detect and report it on the generated annotated video.

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

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

3- Suggest smart city application that could benefit the AV system such that it can improve the navigation and reduce incidences using intelligent machines techniques. Use block diagrams to describe the functionality of each elements that you can use.

Describe different machine learning methods that had been utilized in the AV navigation,

What is the advantage that the reinforcement has over the other

machine learning techniques that made it a potential method in AV navigation?

How the AV will be integrated in smart city such that it will utilize the available updated data during navigation.