Introduction

This tutorial aims to explore the challenges involved in implementing AI in autonomous vehicles. We will discuss the difficulties faced in this domain and propose potential solutions. By the end of this tutorial, you should have a comprehensive understanding of the obstacles in using AI for autonomous vehicles, and ideas on how to tackle them.

Prerequisites:
Basic understanding of AI (Artificial Intelligence) and autonomous vehicles would be beneficial. However, the content is beginner-friendly, so no advanced knowledge is required.

Step-by-Step Guide

Understanding the Challenges

1. Data Collection and Processing: One of the key challenges is acquiring and processing a massive amount of data required for training the AI models. Autonomous vehicles need to be trained with diverse scenarios, which means collecting data for various weather conditions, traffic situations, pedestrian behaviors, etc.

2. Real-time Decision Making: Autonomous vehicles need to process data and make decisions in real-time. This requires advanced machine learning algorithms and powerful processors.

3. Security: With the incorporation of AI and connectivity, autonomous vehicles are vulnerable to cyber-attacks. Ensuring the security of the AI systems in these vehicles is a significant challenge.

4. Legislation and Liability: As of now, there are no concrete laws for autonomous vehicles, which makes it a gray area. Determining the liability in case of accidents is also a challenge.

Possible Solutions

1. Simulations: Simulations can help in generating training data for different scenarios. This can help in training the AI models effectively.

2. Advanced AI Algorithms and Hardware: Implementing advanced AI algorithms and using powerful hardware can help in real-time decision making.

3. Cybersecurity Measures: Implementing robust cybersecurity measures can help in protecting the vehicles from cyber-attacks.

4. Clear Legislation: Governments and policy makers need to come up with clear legislation for autonomous vehicles to solve the issue of liability.

Code Examples

This tutorial doesn't include code examples as it is more about understanding the challenges and potential solutions for implementing AI in autonomous vehicles. However, there are numerous open-source platforms available for simulating autonomous vehicles, such as CARLA, where you can practice implementing AI algorithms.

Summary

In this tutorial, we discussed the various challenges of implementing AI in autonomous vehicles including data collection, real-time decision making, security, and legislation. We also discussed possible solutions like using simulations for generating training data, implementing advanced AI algorithms and powerful hardware, robust cybersecurity measures, and the need for clear legislation.

Practice Exercises

Since this tutorial doesn't involve coding, we suggest the following exercises to further understand the subject:

1. Research about different autonomous vehicle simulation platforms and their features.
2. Investigate about the current legislation around autonomous vehicles in different countries.
3. Explore the various AI algorithms used in autonomous vehicles and understand how they work.

Remember, the key to learning is consistency and practice. So, keep exploring and learning. Happy learning!

Additional Resources

1. CARLA: Open Source Simulator for Autonomous Driving
2. Understanding the role of AI in Autonomous vehicles
3. Challenges and Solutions for Autonomous Driving