Self Driving Highlights (MIX 2)
Self Driving Highlights
Authors:
Mª Pilar Calderón Remacho
Rubén Moros Martin-Falquina
ABSTRACT
This report talks about the main progress in self-driving car technology, especially in artificial intelligence and machine learning. It compares Tesla and Waymo to show their strengths and weaknesses, and concludes that Tesla is the winner in terms of performance and commercial adoption. The report also looks at ethical and legal problems that can slow down progress. The goal is to give a clear idea of how this technology is growing and what challenges it faces.
Autonomous vehicles show great potential in reducing accidents and improving traffic efficiency but challenges remain. Ethical dilemmas, inconsistent regulations and legal challenges slow down their widespread adoption. To succeed, the industry must improve AI reliability, establish clear ethical guidelines, and harmonize global laws.
INTRODUCTION
The target audience is peers with a similar academic level, who share an interest in technology and innovation. The purpose of this report is to provide an overview of key advancements in self-driving technology, discussing its benefits and current challenges.
SOURCES AND METHODOLOGY
The most relevant sources include academic articles on artificial intelligence and automation, industry reports from companies like Tesla and Waymo, and governmental regulations regarding autonomous vehicles. These sources offer reliable and up-to-date insights.
KEY FINDINGS
Advances in Artificial Intelligence
Learning, recognizing, and making real-time decisions, artificial intelligence is the greatest advancement that makes autonomous driving possible. It enables autonomous vehicles to learn, recognize objects and make real-time decisions. Some of the most important advances in artificial intelligence are as follows:
Perception systems: including sensors, cameras and radars to identify the environment in which the autonomous vehicle is moving. Recent developments, including radar technology with 3D imaging and AI-enhanced sensor fusion, have significantly improved object detection and environmental awareness.
Deep learning: Neural network algorithms help the autonomous vehicle anticipate future scenarios. Newer architectures, such as YOLOv11, improve the detection of vehicles and obstacles, even in complex situations with low visibility or partial occlusion.
Reinforcement learning: the autonomous vehicle’s behavior changes and learns based on the feedback it receives. This method is being used to teach cars how to optimize decisions like line changes and speed control in real-time driving conditions. Some commercial systems, like Mercedes-Benz’s Level 3 Drive Pilot, are already applying these techniques.
Benchmarking
In this section, we analyze different platforms developed by industry leaders, such as Tesla and Waymo, and examine their performance through technical metrics and recent studies.
One of the most representative models is the Tesla Model S Plaid, which integrates the camera-based Full Self-Driving (FSD) Beta system, supported by the Dojo supercomputer. It has demonstrated a high level of autonomy in real-world conditions, standing out for its city and highway driving capability with minimal driver intervention.
Based on the analysis, Tesla is positioned as the clear winner. Tesla outperforms Waymo in safety, public and commercial availability, making it more viable today.
Safety and Ethics
The reliability of driverless cars is key to their adoption. Critical decisions in emergency situations spark debates about ethics and responsibility in accidents. The “trolley problem” remains unsolved, since an autonomous vehicle would have to decide whether to prioritize passengers or pedestrians. It is essential to analyze math models and algorithms to really understand what are the ethical implications of autonomous vehicles.
Regulatory and Legal Challenges
Without a clear legal framework, widespread adoption is complicated. Regulations are still evolving to balance innovation, safety, and legal responsibility.
CONCLUSIONS
Autonomous vehicles show great potential in reducing accidents and improving traffic efficiency but challenges remain. Ethical dilemmas, inconsistent regulations and legal challenges slow down their widespread adoption. To succeed, the industry must improve AI reliability, establish clear ethical guidelines, and harmonize global laws.
BIBLIOGRAPHY
- Safety – Waymo
- Tesla vs Waymo: A Tale of Two Self-Driving Dreams (And Why I Still Love My Spaceship Sounds)
- How To Judge If A Robocar Is Actually Good (Tesla Vs. Waymo)
- Driving Towards Autonomy: Advancements in AI Algorithms for Self-Driving Cars
- Advancements in Radar Technology for Autonomous Vehicles | Level Five Supplies
- New Trends in Sensors for Autonomous Driving Perception Systems
- The Tight Connection Between Reinforcement Learning and Autonomous Driving - Ascentt
- YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems
- The folly of trolleys: Ethical challenges and autonomous vehicles
- Navigating the road ahead: exploring the regulatory framework for autonomous vehicles in the United States
- Automated driving regulations – where are we now? - ScienceDirect
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