After four and a half years, today is my last day at Udacity. On Monday, I will return to my roots in core self-driving car engineering. I’m excited!
Udacity has been the most successful and fun experience of professional life. I leave with memories of amazing students, terrific colleagues, and work of which I am proud.
I am so grateful to Sebastian Thrun and the Udacity team for recruiting me here in 2016. Together we built the Self-Driving Car Engineer Nanodegree Program, which has trained thousands of autonomous vehicle engineers, along many other amazing programs an courses, ranging from artificial intelligence to data science to web development to cloud computing, and beyond.
This small collection of photos captures a few of my many wonderful experiences with this amazing company.
Udacity is full of such wonderful people! My colleagues made me an amazing farewell video 🙂
I’m a little self-conscious about sharing it, because it’s hardly modest. But the video is a tour in and of itself through my time at Udacity, and it makes me so happy and proud.
If you pay attention, you can even get some hints about what I’ll be up to next 😉
We hear from a lot of Udacity students about their experiences in our programs — the good and the bad. Both positive and negative feedback are valuable, and it’s always nice to hear when we’ve done a good job.
The goal of this program is to offer a much deeper dive into perception and sensor fusion than we were able to do in our core Self-Driving Car Engineer Nanodegree Program. This is a great option for students who want to develop super-advanced, cutting-edge skills for working with lidar, camera, and radar data, and fusing that data together.
The first three months of the program are brand new content and projects that we’ve never taught before. The final month, on Kalman filters, comes from our core Self-Driving Car Nanodegree Program. The course is designed to last four months for new students. Students who have already graduated the core Self-Driving Car Engineer Nanodegree Program should be able to finish this specialized Sensor Fusion Nanodegree Program in about three months.
Course 1: Lidar Instructor:Aaron Brown, Mercedes-Benz Lesson: Introduction. View lidar point clouds with Point Cloud Library (PCL). Lesson: Point Cloud Segmentation. Program the RANSAC algorithm to segment and remove the ground plane from a lidar point cloud. Lesson: Clustering. Draw bounding boxes around objects (e.g. vehicles and pedestrians) by grouping points with Euclidean clustering and k-d trees. Lesson: Real Point Cloud Data. Apply segmentation and clustering to data streaming from a lidar sensor on a real self-driving car. Lesson: Lidar Obstacle Detection Project. Filter, segment, and cluster real lidar point cloud data to detect vehicles and other objects!
Course 3: Camera Instructor:Andreas Haja, HAJA Consulting Lesson: Computer Vision. Learn how cameras capture light to form images. Lesson: Collision Detection. Design a system to measure the time to collision (TTC) with both lidar and camera sensors. Lesson: Tracking Image Features. Identify key points in an image and track those points across successive images, using BRISK and SIFT, in order to measure velocity. Project: 2D Feature Tracking. Compare key point detectors to track objects across images! Lesson: Combining Camera and Lidar. Project lidar points backward onto a camera image in order to fuse sensor modalities. Perform neural network inference on the fused data in order to track a vehicle. Lesson: Track An Object in 3D. Combine point cloud data, computer vision, and deep learning to track a moving vehicle and estimate time to collision!
Course 4: Kalman Filters Instructors: Dominic Nuss, Michael Maile, and Andrei Vatavu, Mercedes-Benz Lesson: Sensors. Differentiate sensor modalities based on their strengths and weaknesses. Lesson: Kalman Filters. Combine multiple sensor measurements using Kalman filters — a probabilistic tool for data fusion. Lesson: Extended Kalman Filters. Build a Kalman filter pipeline that smoothes non-linear sensor measurements. Lesson: Unscented Kalman Filters. Linearize data around multiple sigma points in order to fuse highly non-linear data. Project: Tracking with an Unscented Kalman Filter. Track an object using both radar and lidar data, fused with an unscented Kalman filter!
One of the highlights of working at Udacity is partnering with world experts to teach complex skills to anybody in the world.
In this program we are fortunate to work especially closely with autonomous vehicle engineers from Mercedes-Benz. They appear throughout the Nanodegree Program, often as the primary instructors, and sometimes simply offering their expertise and context on any other topic.
MathWorks has also proven terrific partners by offering our students free educational licenses for MATLAB. The radar course in this program is taught primarily in MATLAB and leverages several of their newest and most advanced toolboxes.
That sums up how I felt building this Nanodegree Program. We spent over a year kicking around ideas for this program, starting work and stopping work, and there were times I thought it wasn’t going to happen. Then we got the right group of instructors together it came together faster than I ever imagined, and it’s beautiful.
My Udacity colleague Vienna Harvey sat down with Australian podcaster Zoe Eather to discuss the role of both ethics and education as they relate to self-driving cars. It’s a fun episode 🙂
This interview is part of Zoe’s Smart Community podcast, which covers everything from infrastructure, to data, to climate change, to mobility.
Prior to Vienna’s interview, I got to take Zoe for a spin in Carla, Udacity’s self-driving car. Zoe was delightful and I think you’ll enjoy listening to her and Vienna geek out about self-driving cars.
Recently I sat down with Bjarne Stroustrup, the creator of C++, to discuss his career and the evolution of C++ over years.
We discussed Bjarne’s origins in Denmark, his PhD work at Cambridge, the origins of C++ at Bell Labs, how to teach C++, the ISO committee that governs C++, and what exactly made Bjarne’s career so successful. There’s a lot more, too 😀