Introduction

This tutorial aims to guide you through the process of building an AI-powered autonomous vehicle. By the end of this tutorial, you will have a clear understanding of the processes involved, such as sensor fusion, path planning, and control systems.

What You Will Learn:

• Understanding the basic concepts of autonomous vehicles
• Sensor fusion and its importance
• Implementing path planning
• Understanding control systems

Prerequisites:

• Basic understanding of programming and artificial intelligence
• Familiarity with Python
• Knowledge of basic physics

Step-by-Step Guide

Sensor Fusion:

Sensor fusion is the process of combining sensory data from different sources. It helps the vehicle understand its environment better, ensuring safer navigation.

# We use Kalman filter for sensor fusion
from pykalman import KalmanFilter

Path Planning:

Path planning refers to the process of creating a path from the vehicle's location to its destination.

# Implementing A* path planning
def A_Star(start, goal):
    # Your code here

Control Systems:

Control systems are essential for an autonomous vehicle to follow the planned path.

# PID controller
class PIDController:
    def __init__(self, Kp, Ki, Kd):
        # Your code here

Code Examples

Sensor Fusion:

# Kalman filter for sensor fusion
from pykalman import KalmanFilter

kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 1.0]])

# Define the initial state
initial_state_mean = [0, 0]

# Define the initial state uncertainty
initial_state_covariance = [[1, 0], [0, 1]]

# Define the observed data
observations = [[1,0], [0,0], [0,1]]

# Apply the Kalman Filter
(kalman_state_means, kalman_state_covariances) = kf.filter(observations)

Path Planning:

# Implementing A* path planning
def A_Star(start, goal):
    # Your code here

Control Systems:

# PID controller
class PIDController:
    def __init__(self, Kp, Ki, Kd):
        self.Kp = Kp
        self.Ki = Ki
        self.Kd = Kd

Summary

In this tutorial, we covered the basics of building an AI-powered autonomous vehicle, including sensor fusion, path planning, and control systems. The next steps would be to delve into each of these topics in more depth, as well as exploring additional topics such as obstacle avoidance and decision making.

Practice Exercises

1. Implement a function to calculate the Euclidean distance between two points. This will be useful for the A* path planning algorithm.
2. Implement a basic PID controller.

Solutions

# Euclidean distance
def euclidean_distance(point1, point2):
    return ((point1[0]-point2[0])**2 + (point1[1]-point2[1])**2)**0.5

# PID controller
class PIDController:
    def __init__(self, Kp, Ki, Kd):
        self.Kp = Kp
        self.Ki = Ki
        self.Kd = Kd
        self.error_sum = 0
        self.last_error = 0

    def control(self, error, delta_time):
        self.error_sum += error * delta_time
        delta_error = error - self.last_error
        self.last_error = error
        return self.Kp * error + self.Ki * self.error_sum + self.Kd * delta_error / delta_time

Further Practice

Try implementing these concepts in a simulated environment such as CARLA or AirSim.