My old boss and friend, Sebastian Thrun, spent an hour talking with my current boss, Steve Forbes.
They cover AI, transportation, digital medicine, autonomous flight, Udacity, the future of technology, and more. You even get to hear Sebastian talk about a refrigerator flirting with a dishwasher.
(I contribute Forbes.com; itâs a stretch to call Steve Forbes my âbossâ. Iâve actually never met Steve Forbes myself, but just go with it.)
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. Iâm 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 đ
Iâm a little self-conscious about sharing it, because itâs 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 Iâll be up to next đ
On Wednesday, 10â11am PT, Iâll take over the Udacity Twitter account to host an AMA about robotics and automation in the post-COVID19 world. Or, anything else youâd like to ask!
Join me!
Today Udacityâs AWS Cloud Architect Nanodegree Program launched!
This is outside my usual wheelhouse of autonomous vehicles and robotics, but I played a significant (albeit behind-the-scenes) role teaching the first course in the three-course program.
Tom Verbiscer and I worked together to design and build a sequence of lessons on high-availability, resilience, and redundancy. Tom teaches about:
And thatâs just the first course of the program!
I hope everyone that enrolls learns as much from taking the program as I did working with Tom to build the program.
We hear from a lot of Udacity students about their experiences in our programsâââthe good and the bad. Both positive and negative feedback are valuable, and itâs always nice to hear when weâve done a good job.
So I was very happy to read this review of our Self-Driving Car Engineer Nanodegree Program from Akshay Vikhe.
I came to Udacity three years ago to build the Self-Driving Car Engineer Nanodegree Program. Since then, weâve had thousands of students enroll in and graduate the program, and weâve built other Nanodegree programs:
Udacity builds all of its programs with the goal of helping students get jobs and advance in their careers.
This week Levi Sumagaysay at the San Jose Mercury News published an article about how Nanodegree programs lead to jobs, particularly in the exciting world of autonomous vehicles. Itâs awesome to make the hometown paper đ°
My Udacity colleague, Michele Cavaioni, has written a post about flying cars. You should read it!
Michaele has worked extensively with our Flying Car and Autonomous Flight Engineer Nanodegree Program and knows his stuff. In this post, he focuses on the advantages and disadvantages of rotary versus fixed-wing aircraft.
You might be able to deduce the strengths and weaknesses of each modality from these nifty graphics, but you should read the whole post for more đ đ âď¸
Udacityâs Sensor Fusion Nanodegree Program launched yesterday! I am so happy to get this one out to students đ
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 weâve 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 2: Radar
Instructor: Abdullah Zaidi, Metawave
Lesson: Radar Principles. Measure an objectâs range using the physical properties of radar.
Lesson: Range-Doppler Estimation. Perform a fast Fourier transform (FFT) on a frequency modulation continuous wave (FMCW) radar signal to create a Doppler map for object detection and velocity measurement.
Lesson: Clutter, CFAR, AoA. Filter noisy radar data in order to reduce both false positives and false negatives.
Lesson: Clustering and Tracking. Track a vehicle with the Automated Driving System Toolbox in MATLAB.
Lesson: Radar Target Generation and Detection Project. Design a radar system using FMCW, signal processing, FFT, and CFAR!
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 filtersâââa 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.
There is a quote, from a completely different context, âIt took forever and then it took a night.â
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 wasnât going to happen. Then we got the right group of instructors together it came together faster than I ever imagined, and itâs 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. Itâs a fun episode đ
This interview is part of Zoeâs Smart Community podcast, which covers everything from infrastructure, to data, to climate change, to mobility.
Prior to Viennaâs interview, I got to take Zoe for a spin in Carla, Udacityâs self-driving car. Zoe was delightful and I think youâll 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 Bjarneâs 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 Bjarneâs career so successful. Thereâs a lot more, too đ
And if you are interested in learning C++ from Bjarne (and me, and many other instructors), enroll in Udacityâs C++ Nanodegree Program!
David Silver 