Summary of Autonomous Vehicles: Evolution Of Artificial Intelligence and Learning Algorithms, by Divya Garikapati and Sneha Sudhir Shetiya
Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms
by Divya Garikapati, Sneha Sudhir Shetiya
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the evolution of Artificial Intelligence (AI) within autonomous vehicles, examining its role in shaping decision-making capabilities and tracing advancements from foundational principles to recent developments. The study provides an overview of AI’s current landscape in autonomous vehicles, highlighting its fundamental importance in developing life cycles for vehicles. It addresses ethical considerations and bias in AI-driven software development, analyzing the usage and types of AI/learning algorithms over time. The paper also discusses parameters refining algorithms for trucks and cars, enabling them to adapt, learn, and improve performance. Finally, it outlines different levels of autonomy, illustrating AI’s role in automating key tasks at each level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how Artificial Intelligence (AI) helps make self-driving cars better. It shows how AI has changed over time, from the basics to newer advancements. The study talks about what makes AI important for making decisions in self-driving vehicles and how it’s used to develop new software. The paper also looks at what kinds of algorithms are being used and why they’re getting better. It even explains how cars can learn and improve on their own. |
