讲解 WM997-15 - Smart, Connected and Autonomous Vehicle Fundamentals讲解 Prolog

Module title & code

WM997-15 - Smart, Connected and Autonomous Vehicle Fundamentals

Module tutor

Umang Parekh

Assessment type

Question bank

Weighting of mark

80%

Assignment brief

In this PMA you have to demonstrate an understanding of smart connected and autonomous vehicles fundamentals.

You have been asked to answer the questions below from six different subject areas.

100 marks available from PMA questions

Question 1 - SAE Level of Autonomy, Requirements, and Scenario Testing – Total 25 Marks

Q 1.1 Write at least 6 semi-formal requirements of Emergency Braking Control. Each requirement should be clear, concise, and specify the necessary conditions, actions, and expected outcomes. For each requirement, provide a brief explanation to clarify its purpose and importance. (8 Marks)

Q 1.2    Create a P-Diagram (Parameter Diagram) for the Emergency Braking Control system. Explain the key parameters and their significance in the system. (10 Marks)

Q 1.3 Design and describe two scenarios to test Emergency Braking Control. Explain the purpose of these tests and how they ensure the system's effectiveness in preventing collisions. (5 Marks)

Q 1.5 For automotive, what are the benefits and drawbacks of simulation testing, explain in detail. (2 Marks)

Question 2 - HTI and Mobility as a Service – Total 20 Marks

Q 2.1   Explain the role of Driver Monitoring Systems (DMS) in enhancing vehicle safety and improving road user wellbeing. Provide a detailed example of a DMS in operation, and discuss the challenges and potential solutions associated with its implementation. (10 Marks)

Q 2.2   Explain the concept of ride-sharing services and analyze their significance in the future of transportation. Provide a detailed example of a ride-sharing service in operation, and discuss the economic, environmental, and social impacts of widespread adoption. (8 Marks)

Q 2.3   Discuss the importance of user interface design in autonomous vehicles. (2 Marks)

Question 3 - Automotive Standards – Total 10 Marks

Q 3.1 What are the benefits of using ISO 21434 while ensuring cybersecurity in CAVs? Who will obtain the most benefits from this standard? (5 Marks)

Q 3.2 Critically compare and explain the difference between ISO 21448 (SOTIF) and ISO 26262. Discuss the primary focus and objectives of each standard. Highlight the scope of each standard and the types of automotive systems they apply to. (5 Marks)

Question 4 - Automotive Perception Sensors – Total 25 Marks

Q 4.1 Based on your learning from the modules and additional research, provide a comprehensive analysis of three key sensor types used in autonomous vehicles: Ultrasonic, Radar, and LiDAR. (20 Marks)

1. Working Principle (5 Marks):

a. Describe the working principle of each sensor type (Ultrasonic, Radar, LiDAR) in detail.

b. Include diagrams or figures to illustrate the operation of each sensor.

2. Strengths and Weaknesses (5 Marks):

a. Discuss the strengths and weaknesses of each sensor type.

b. Support your discussion with examples from scientific literature or case studies.

3. Real-World Applications (5 Marks):

a. Provide examples of real-world applications for each sensor type.

b. Explain how LiDAR is used for 3D mapping, how Radar assists with adaptive cruise control, and how Ultrasonic sensors are employed in parking assistance systems.

Q 4.2 For the above sensor types, identify their weaknesses/drawbacks backed with references. (5 Marks)

Question 5 - Wired & Wireless Communication in AVs – Total 14 Marks

Q 5.1 Provide detailed examples of how specific automotive communication protocols, such as CAN (Controller Area Network), LIN (Local Interconnect Network), FlexRay, and Automotive Ethernet, correspond to the layers of the OSI model. Your answer should include an analysis of each protocol, identifying the relevant OSI layers they interact with and their significance in automotive networking. (10 Marks)

Q 5.2  Conduct a comprehensive analysis of the differences between DSRC (Dedicated Short-Range Communications) and C-V2X (Cellular Vehicle-to-Everything) technologies, focusing on their applications, performance metrics, and future potential in autonomous vehicles. (4 Marks)

Question 6 - Machine Learning – Total 6 Marks

Using the 1D (one dimensional) polynomial regression on 5 data points with one feature, xxx, and one label, yyy, given by:

(x(1),y(1))=(1,3)

(x(2),y(2))=(2,5)

(x(3),y(3))=(3,3)

(x(4),y(4))=(4,4)

(x(5),y(5))=(5,5)

Calculate the mean value of the given data and plot a Regression line on the graph using a Polynomial Regression Algorithm. (6 Marks)

Word count

No word count limit

Module learning outcomes (numbered)

1. Demonstrate an in-depth knowledge of key principles underpinning human interaction and apply it to compare/criticise the design. [AHEP: M1, M3, M4, M5]

2. Evaluate and compare the performance of different automotive perception sensors. [AHEP: M1, M2, M3]

3. Demonstrate a critical high-level understanding of challenges associated with data science and machine learning techniques, in the context of safe Automated Vehicles. [AHEP: M1, M2, M3, M6]

4. Demonstrate an applied knowledge of automotive system complexity and their testing. [AHEP: M1, M2, M6]

5. Critically evaluate wired and wireless communication technologies in the SCAV context. [AHEP: M1, M3, M6]

6. Collaborate as a team to apply acquired knowledge in critically evaluating and implementing technical choices in the design of autonomous vehicles (AVs). [AHEP: M1, M4, M5, M16, M17]

Learning outcomes assessed in this assessment (numbered)

LO from 1 to 5

Marking guidelines

80%-100% of the mark will be given to complete answers with an extremely high level of technical competence shown by fully appropriate selection and correct application of tools/ techniques/ methodologies.

70-79% of the mark will be given to almost complete answers, near-complete conceptual understanding and a high level of technical competence with only insignificant errors.

60-69% of the mark will be given to answers showing good conceptual understanding and a good level of technical competence although there may be a few gaps leading to some minor errors.

50-59% for answers showing a grasp of the subject matter with possibly some confusion or gaps but none that is major, and a fair understanding of the concepts

40-49% for answers showing some familiarity with the subject matter, but with major gaps and serious misconceptions

<40% for answers showing serious gaps in knowledge of the subject matter and many areas of confusion, with major errors

Academic guidance resources

N/A