Introduction to Self-Driving Cars

Go to Course: https://www.coursera.org/learn/intro-self-driving-cars

Introduction

## Course Review: Introduction to Self-Driving Cars (University of Toronto) In the rapidly evolving world of technology, few advancements have captured the public imagination quite like self-driving cars. As we stand on the brink of a new era of transportation, the **Introduction to Self-Driving Cars** course offered by the University of Toronto on Coursera serves as an essential entry point for anyone looking to delve into the inner workings of autonomous vehicles. This course is the first step in their comprehensive **Self-Driving Cars Specialization**, expertly curated to help learners understand the fundamental concepts underlying this groundbreaking field. ### Course Overview The **Introduction to Self-Driving Cars** course addresses the essential terminology, design considerations, and safety assessments that are crucial for understanding autonomous vehicles. Upon completion of this course, learners will have a solid grounding in various aspects of self-driving technology, including: - Key hardware components associated with self-driving cars - The primary structure of the self-driving software stack - Techniques for vehicle modeling and control - Important safety frameworks necessary for safe operation on public roads. ### Syllabus Breakdown The course comprises a comprehensive syllabus divided into modules, each focusing on critical aspects of self-driving car technology: 1. **Welcome to the Self-Driving Cars Specialization**: This introductory module sets the stage for the specialization by outlining major advancements and progress made over the last two decades in the field of autonomous driving. 2. **The Requirements for Autonomy**: Here, learners engage with the complexities of defining the autonomous driving task and recognize the intricacies of the driving environment. 3. **Self-Driving Hardware and Software Architectures**: This module explores the diversity of system architectures for self-driving vehicles while addressing the trade-offs that impact design decisions, including cost, reliability, and performance. 4. **Safety Assurance for Autonomous Vehicles**: Safety is paramount in the development of self-driving technology. This segment evaluates the challenges faced by developers, providing insights into various safety assurance methodologies and their application in real-world scenarios. 5. **Vehicle Dynamic Modeling**: Understand the mechanics of how vehicles move based on inputs like steering, throttle, and brake commands. This module delves into physics-based models that contribute to vehicle control and motion planning. 6. **Vehicle Longitudinal Control**: Focused on speed profile tracking, learners will develop a baseline controller that functions effectively across a variety of driving conditions. 7. **Vehicle Lateral Control**: This section covers how to maintain accurate path tracking through environmental navigation, exploring both geometric control and model predictive control strategies. 8. **Putting it All Together**: In the final module, students will engage in practical simulations where they can apply their knowledge to design vehicle control for an autonomous car on a racetrack, allowing for a hands-on experience to refine their skills. ### Recommendation **Who Should Take This Course?** The **Introduction to Self-Driving Cars** course is ideal for: - **Students**: Those pursuing degrees in computer science, engineering, or robotics will find the content relevant and enriching. - **Professionals**: Individuals currently working in automotive engineering or those looking to pivot their career towards emerging technologies. - **Enthusiasts**: Anyone with a keen interest in technology and transportation innovation will benefit from this foundational course. ### Conclusion In conclusion, the **Introduction to Self-Driving Cars** course offers a thorough overview of the concepts, technologies, and challenges surrounding autonomous vehicles. Its well-structured modules provide valuable knowledge to learners at various stages of their professional or academic journey. With its engaging content and practical applications, I wholeheartedly recommend this course to anyone eager to understand the mechanics of self-driving technology and its implications for the future of transportation. Enroll today on [Coursera](https://www.coursera.org/) and take the first step toward becoming a part of the revolution in mobility!

Syllabus

Module 0: Welcome to the Self-Driving Cars Specialization!

This module will introduce you to the main concepts and layout of the specialization and discusses the major advances made in the field over the last two decades, highlighting the most recent progress made by major players in terms of safety and performance metrics, where available.

Self-driving cars present an extremely rich and inter-disciplinary problem. This module introduces the language and structure of the problem definition, defining the most salient elements of the driving task and the driving environment.

System architectures for self-driving vehicles are extremely diverse, as no standardized solution has yet emerged. This module describes both the hardware and software architectures commonly used and some of the tradeoffs in terms of cost, reliability, performance and complexity that constrain autonomous vehicle design.

As the self-driving domain matures, the requirement for safety assurance on public roads become more critical to self-driving developers. You will evaluate the challenges and approaches employed to date to tackle the immense challenge of assuring the safe operation of autonomous vehicles in an uncontrolled public road driving environment.

The first task for automating an driverless vehicle is to define a model for how the vehicle moves given steering, throttle and brake commands. This module progresses through a sequence of increasing fidelity physics-based models that are used to design vehicle controllers and motion planners that adhere to the limits of vehicle capabilities.

Longitudinal control of an autonomous vehicle involves tracking a speed profile along a fixed path, and can be achieved with reasonable accuracy using classic control techniques. This week, you will learn how to develop a baseline controller that is applicable for a significant subset of driving conditions, which include most non-evasive or highly-dynamic motions.

This week, you will learn about how lateral vehicle control ensures that a fixed path through the environment is tracked accurately. You will see how to define geometry of the path following control problem and develop both a simple geometric control and a dynamic model predictive control approach.

For the last week of the course, now you will get hands on with a simulation of an autonomous vehicle that requires longitudinal and lateral vehicle control design to track a predefined path along a racetrack with a given speed profile. You are encouraged to modify the speed profile and/or path to improve their lap time, without any requirement to do so. Work and play!