Assignment Instructions on Autonomous Vehicles: AI and Safety Challenges
Assignment 18
General Assessment Guidance
This assessment represents the primary evaluation for the module, requiring a critical investigation into autonomous vehicle technologies, with a focus on AI-driven decision-making, safety protocols, and operational challenges. Students are expected to produce a cohesive and evidence-informed report that examines both technical mechanisms and societal implications of autonomous driving systems.
Submissions must be uploaded exclusively via Turnitin. Other formats, including email attachments or physical copies, will not be accepted. Include only your Student Reference Number (SRN), personal names are strictly prohibited. Late submissions will not be marked.
The report must adhere to Harvard referencing standards, and any AI assistance may be used only for language correction or structural refinement. A completed Assignment Cover Sheet is mandatory for valid submission.
Assessment Brief
Context of Autonomous Vehicles and AI
This assignment requires a detailed evaluation of AI applications within autonomous vehicle (AV) systems, highlighting sensor integration, perception algorithms, and safety validation frameworks. Students should investigate real-world case studies, regulatory challenges, and emerging trends in AV technology.
The report should demonstrate critical understanding of AI model performance, the complexity of autonomous decision-making, and ethical considerations in the deployment of self-driving vehicles. Practical examples may include highway automation, urban navigation, collision avoidance systems, and emergency response protocols.
Learning Objectives
LO1 – Examine AI architectures and sensor fusion techniques in autonomous vehicles.
LO2 – Critically evaluate safety challenges, ethical dilemmas, and reliability concerns.
LO3 – Integrate empirical research to assess AV performance in controlled and uncontrolled environments.
LO4 – Develop evidence-based recommendations for the safe and ethical deployment of AV technologies.
Key Report Sections
- Historical and Technological Evolution of Autonomous Vehicles
- AI Architectures and Sensor Integration
- Safety, Testing, and Risk Management Protocols
- Ethical, Legal, and Social Considerations
- Comparative Evaluation of AV Systems
- Strategic Recommendations for Deployment
Each section should integrate academic research, industry reports, and real-world case studies. The report must maintain coherence, logical progression, and depth of analysis.
Suggested Report Structure
- Declaration Page (PP)
- Title Page
- Table of Contents
- Historical and Technological Evolution of Autonomous Vehicles
- AI Architectures and Sensor Integration
- Safety, Testing, and Risk Management Protocols
- Ethical, Legal, and Social Considerations
- Comparative Evaluation of AV Systems
- Strategic Recommendations for Deployment
- Harvard References
- Appendices (if required)
Word Count Breakdown (Approximate)
Historical and Technological Evolution – 400
AI Architectures and Sensor Integration – 500
Safety, Testing, and Risk Management Protocols – 500
Ethical, Legal, and Social Considerations – 400
Comparative Evaluation of AV Systems – 400
Strategic Recommendations for Deployment – 300
Total – approximately 2,500 words
Historical and Technological Evolution of Autonomous Vehicles
Trace the development of AV technologies, from early driver-assistance systems to fully autonomous prototypes:
- Milestones in AV history: from cruise control to Level 5 autonomy
- Evolution of onboard computing systems and AI algorithms
- Integration of LiDAR, radar, ultrasonic sensors, and cameras
- Progression of machine learning models for perception, localization, and control
- Current research trends in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication
Include timelines, diagrams, and case examples showing technological progression and adoption challenges.
AI Architectures and Sensor Integration
Examine the technical foundations enabling autonomous decision-making:
- Sensor fusion methods combining LiDAR, radar, and visual data
- Perception algorithms for object detection, tracking, and classification
- Deep learning models for path planning, trajectory prediction, and situational awareness
- Reinforcement learning approaches for adaptive control in dynamic environments
- Real-world challenges: sensor noise, adverse weather conditions, and edge-case scenarios
Visualizations such as data flow diagrams or network architectures may be used to illustrate AI pipelines.
Safety, Testing, and Risk Management Protocols
Assess the strategies for ensuring AV safety:
- Simulation testing, real-world pilot programs, and closed-track experiments
- Redundancy mechanisms in perception and control systems
- Fail-safe protocols and emergency intervention strategies
- Incident analysis and lessons from AV accidents
- Regulatory compliance and standardization frameworks (e.g., ISO 26262, SAE J3016)
Demonstrate critical evaluation by comparing theoretical safety models with practical implementations in commercial AV platforms.
Ethical, Legal, and Social Considerations
Investigate the broader societal impact of autonomous vehicles:
- Moral decision-making in unavoidable collision scenarios
- Data privacy and cybersecurity concerns in networked AV systems
- Liability and accountability frameworks for manufacturers, software developers, and operators
- Public acceptance, user trust, and societal readiness for autonomous driving
- Policies to mitigate bias in AI decision-making for diverse road conditions
Use recent case studies, government reports, and ethical frameworks to support your analysis.
Comparative Evaluation of AV Systems
Conduct a critical assessment of existing autonomous vehicle solutions:
- Tesla Autopilot, Waymo, Cruise, and other prominent platforms
- Strengths and limitations regarding sensor technology, AI robustness, and safety features
- Performance metrics: accident rates, disengagement frequencies, and reliability indices
- Scalability, interoperability, and urban vs highway deployment challenges
- Recommendations based on empirical evidence from peer-reviewed studies and pilot programs
Include tables, charts, or heatmaps summarizing AV performance comparisons.
Strategic Recommendations for Deployment
Provide practical, evidence-based guidance for safe and effective AV integration:
- Optimal AI architectures for reliability and adaptability
- Testing protocols for urban, highway, and mixed environments
- Policies for cybersecurity, ethical AI, and regulatory compliance
- Methods to improve public trust and societal acceptance
- Future directions: collaborative autonomous systems, edge computing, and smart city integration
Conclude by highlighting the strategic value of AI-enhanced AVs in reducing accidents, improving mobility, and advancing transportation innovation.
References and Presentation
- Apply Harvard referencing consistently for all sources
- Maintain formal academic style, professional formatting, and numbered pages
- Label all figures, tables, and diagrams correctly
- Draw on academic journals, technical whitepapers, and industry reports to ensure breadth of perspective