Recently, I invested a significant amount of my free time in completing Udacity’s Self-driving Car Engineer Nanodegree program. In this article, I aim to share my firsthand experience and provide an honest evaluation of this program. My goal is to offer prospective students a clear understanding of what to expect, especially if you’re considering enrolling to advance your career in the rapidly evolving field of autonomous technology. As someone currently working in the autonomous vehicle industry, my insights are designed to help you determine if this Self Driving Car Nanodegree Program truly aligns with your professional objectives and aspirations.
This Udacity program has established itself over time, with its initial iterations dating back to 2017 when I first became aware of it during my Master’s studies. However, it’s crucial to note that both the curriculum and project assignments have undergone substantial updates to keep pace with the dynamic advancements in autonomous vehicle technology. The current course content is significantly more relevant to contemporary industry practices compared to its earlier versions. This evolution is partly due to Udacity’s partnerships with industry leaders like Waymo and Mercedes-Benz, who contribute to the material design. Notably, students in the program utilize Waymo’s open-source dataset for project work, providing real-world experience. The course structure mirrors a typical autonomous vehicle software stack, progressing logically through modules: starting with computer vision and sensor fusion, advancing to localization and planning, and culminating in control systems.
Computer Vision Module: Object Detection with Neural Networks
The computer vision module is dedicated to the practical application of convolutional neural networks (CNNs) for object detection. This module, in particular, has seen considerable updates in its scope compared to previous versions of the self driving car nanodegree program. It begins with a foundational overview of the machine learning workflow, then delves into camera sensors, including calibration exercises, image manipulation, and pixel-level transformations. This foundational knowledge becomes essential for conducting exploratory data analysis on the datasets provided. Even for individuals not directly involved in machine learning, this understanding is invaluable for developing robust data pipelines in data engineering or analyst roles within the autonomous vehicle domain.
Moving into core machine learning topics, the module introduces fundamental models such as linear regression, Gradient Descent, and Stochastic Gradient Descent, before focusing on convolutional neural networks. For students new to machine learning, like myself at the time, Udacity’s approach of progressively introducing various models without overwhelming deep dives is effective. They adeptly explain the advantages of CNNs over traditional feedforward neural networks. Throughout this module, students are exposed to a range of CNN architectures through assigned readings of relevant research papers. Ultimately, we apply transfer learning principles, utilizing a pre-trained model like RESNET50 on the Waymo dataset for our project.
This module adopts a broad educational approach, aiming to equip students with sufficient knowledge to initiate their own projects and delve deeper into neural networks. While the course provides a wide overview, some students might find that it lacks in-depth explanations of the underlying mechanisms for each architecture. However, the program compensates by providing access to numerous research papers, encouraging students to explore these topics in greater detail independently.
Sensor Fusion Module: Combining Lidar and Camera Data
This sensor fusion module concentrates on integrating lidar and camera data, crucial for creating a comprehensive environmental understanding in autonomous driving. Given that the program utilizes Waymo’s open-source dataset, a significant portion of the module is dedicated to understanding lidar point cloud data structure, alongside the hardware aspects of lidar sensors themselves. Building upon the computer vision skills acquired in the previous module, we progress to using a sophisticated YOLO (You Only Look Once) model to detect objects within the lidar point cloud system.
However, a central learning point of this module is the implementation of Kalman filters and Extended Kalman Filters (EKF). The Kalman filter section is taught by Sebastian Thrun himself and is remarkably effective at demystifying this complex concept (this portion is also available for free on YouTube). Furthermore, a highly recommended blog offers a visually engaging explanation of Kalman filters. The key takeaway was dissecting the entire sensor fusion process into manageable steps and understanding its practical implementation.
We begin with track initialization and update processes. Then, we integrate various sensor measurements—camera and lidar in this case—to perform the prediction step of the EKF. We then consider the available measurements for each sensor, transforming them into a common coordinate system for the measurement update step. For track updating, we explore a method known as simple nearest neighbor (SNN) association, further refining computational efficiency through gating rules.
I found this module particularly aligned with industry practices. Udacity presents a highly practical approach to coding the EKF method, which is a valuable and directly transferable skill in the autonomous vehicle field. This hands-on experience is a significant benefit of the self driving car nanodegree program.
Localization Module: Vehicle Positioning in 3D Maps
The primary objective of the localization module is to grasp the principles of vehicle localization. We aim to localize a vehicle within a pre-existing point cloud 3D map of an environment using the Carla simulator and the vehicle’s onboard lidar sensor. This module is notably math-intensive, delving into Bayesian concepts, specifically Bayes’ theorem and Bayes filters, which are then simplified using Markov’s theorem. Once the recursive structure of these calculations is established, we derive a set of formulas ready for C++ implementation.
We then develop a scan matching algorithm to compare two sets of lidar points: one from the onboard sensor and the other from the pre-mapped environment. This comparison helps determine the vehicle’s relative translation with respect to its previous position. Two methods are presented for this: Iterative Closest Point (ICP) and Normal Distributions Transform (NDT). Both approaches offer distinct methodologies, and their respective advantages and disadvantages are demonstrated through the project code.
The project work in this module was particularly engaging, marking the first use of the Carla simulator, a widely used tool for training and project development in autonomous technology. I also appreciated Udacity’s choice to cover this module in C++, a standard language in production software development for autonomous systems, thus bridging the gap between academic learning and industry application, a key advantage of this self driving car nanodegree program.
Planning Module: Reactive and Deliberative Path Planning
Path planning is a vital component of the autonomous vehicle technology stack. It essentially dictates how a vehicle can best and safely navigate various driving scenarios. Path planning algorithms are broadly categorized as reactive or deliberative. Reactive algorithms generate paths based on real-time sensor data, while deliberative algorithms pre-define paths based on the known environment. This course provides an overview of both methodologies for incorporation into planning algorithms. We tackle path planning using a finite state machine combined with a cost function.
For trajectory generation, we explore algorithms like A* and Hybrid A*. A* is effective in unstructured environments like parking lots or warehouses (similar to the technology in robotic vacuum cleaners). However, autonomous vehicles require structured environments, leading us to polynomial trajectory generation techniques based on jerk minimization principles. This method allows us to incorporate constraints related to speed, steering angle, and road infrastructure rules, such as stop signs and lane markings.
The project in this module again utilized the Carla simulator, tasking us with generating multiple trajectories and selecting the optimal one based on a minimum cost function. This module serves as an excellent introduction to motion and path planning, establishing a solid foundation for further exploration. The practical project work is a significant highlight of the self driving car nanodegree program.
Control Module: Vehicle Control Systems
Finally, we arrive at vehicle control, the last stage of the autonomous vehicle stack. With my background in control systems, I found this module to be a basic introduction to the expansive field of vehicle control. However, the key objective was to learn about PID controllers and practice tuning them within the Carla simulator. PID control remains a widely used and effective solution in ADAS (Advanced Driver-Assistance Systems) technology. The industry is progressing towards more advanced controllers that utilize predictive or nonlinear methods for complex applications. One particularly interesting topic was the Twiddle algorithm, a novel concept for me. It’s a straightforward algorithm for automatically tuning PID gains. While introductory, this module provides essential groundwork in vehicle control as it applies to self driving car nanodegree program objectives.
Key Takeaways from the Self Driving Car Nanodegree Program
- Hands-on Experience: The course is rich in intensive exercises, providing valuable first-hand experience in code implementation using industry-standard datasets.
- Enhanced Industry Understanding: As an ADAS engineer, this program deepened my understanding of critical concepts and technologies in the field, especially sensor fusion and localization. I highly recommend this course to anyone in the automotive or ADAS industry. It not only clarified my current role but also broadened my horizons for future opportunities within the autonomous vehicle sector.
- Structured Learning and Support: While some course content is available freely online, the true value of this program lies in its structured curriculum and Udacity’s support system. The organized timeline and course structure helped maintain discipline and motivation. The feedback and guidance from instructors and Udacity’s expert network were invaluable. Furthermore, the 15-week access model served as a motivator to complete the course efficiently.
In conclusion, I hope this detailed review of the self driving car nanodegree program has been beneficial in your decision-making process. Undertaking this course was a highly valuable experience for me, and I am confident it can be for you as well. If you decide to enroll, I would be interested to hear about your experiences and how it aids you in your journey towards contributing to autonomous technology. Please feel free to connect with me on LinkedIn or leave a comment below to continue this discussion. Let’s connect and keep the conversation going!
