AI in Autonomous Vehicles
A technical overview of AI systems in autonomous vehicles, covering perception, decision-making, and real-world deployment challenges.
Overview
This project comprises two research papers for SFU coursework: an informative paper on artificial intelligence in autonomous vehicles, and a persuasive paper on AI, technology, and society. Both papers are research-based and use academic sources to explain technical concepts and argue for responsible adoption of AI systems.
🧠 Informative Paper: Artificial Intelligence in Autonomous Vehicles
A research-based overview of how artificial intelligence powers modern autonomous vehicles. This paper explains core technologies such as machine learning, sensor fusion, and explainable AI (XAI), and analyzes how perception, planning, and control systems work together in self-driving cars. It also discusses real-world challenges including safety, reliability in adverse conditions, ethical decision-making, and regulatory constraints.
- Explains the AI pipeline in autonomous driving (perception → localization → planning → control)
- Covers ML techniques used in AVs (CNNs, RNNs, reinforcement learning)
- Discusses sensor fusion (LiDAR, radar, cameras) and real-world limitations
- Connects technical systems with ethical and societal implications of AVs
✍️ Persuasive Paper: AI / Technology & Society
A persuasive research paper that argues for a thoughtful, evidence-based approach to adopting advanced AI technologies in society. The paper evaluates technical benefits, risks, and ethical considerations, and builds a structured argument supported by academic sources to advocate for responsible development and deployment of AI systems.
- Builds a structured, evidence-based argument using peer-reviewed sources
- Analyzes societal and ethical implications of AI technologies
- Demonstrates critical thinking, technical communication, and research writing
- Connects computer science developments with public policy and social impact
Tech Stack
Paper Summaries
Informative: A technical overview of AI systems in autonomous vehicles, covering perception, decision-making, and real-world deployment challenges.
Persuasive: A persuasive analysis of the societal implications of AI, arguing for responsible and informed adoption of emerging technologies.