This actually appears to have nothing to do (at least directly) with self-driving cars. Rather, Intel is ramping up chip production, which is a capital-intensive process.
However, this line from the article caught my eye: “Israel traditionally competes with Ireland in benefits offered to Intel in exchange for investment.”
I had never really thought of that, but I’m sure it’s a fact of life for technology executives in both countries. Two small, somewhat isolated, highly-educated, technology-focused countries on opposite edges of Europe, with strong ethnic and expatriate connections to the United States. Of course Israel traditionally competes with Ireland. Now that I think about it, they seem like practically the same country.
And this is interesting because Israel has such a dynamic autonomous vehicle industry. Mobileye, of course, but also research centers for many automotive manufacturers and suppliers, and a cluster of autonomous vehicle startups.
Ireland has been less active in the autonomous vehicle market, but if you believe the theory that Ireland and Israel are practically the same country, then presumably the autonomous vehicle industry is coming to Ireland.
“A fleet of Hyundai Motor Company’s next generation fuel cell electric cars have succeeded in completing a self-driven 190 kilometers journey from Seoul to Pyeongchang. This is the first time in the world that level 4 autonomous driving has been achieved with fuel cell electric cars, the ultimate eco-friendly vehicles.”
The idea that self-driving trucks will actually boost the number of driver jobs is not new to me. However, the recent cross-country trip by self-driving truck startup Embark got me thinking about it.
The Embark drive was only a Level 2 endeavor, and it seems like there were multiple disengagements, but the days of Level 4 trucking on the highway seem near.
Embark’s model is to have autonomous vehicles drive from hub-to-hub on the highways, and human drivers handle the last mile deliveries.
“The autonomous trucks would haul trailers from hub to hub on the freeway, but local drivers would continue to handle the more complex driving tasks associated with the beginning and end of each trip — from origin to highway and from highway to final destination.”
It’s at least plausible that this would result in a net increase in driving jobs, if long-haul costs dropped so dramatically that interstate commerce surges.
Enjoy a look at some of the projects our students are building, including Finding Lane Lines, Traffic Sign Classifier, Behavioral Cloning, and more!
Students in our Self-Driving Car Engineer Nanodegree program engage in a project-based curriculum, and from the moment they enroll, they begin addressing key challenges and topics through building specialized projects. Here are all of the projects they build!
Finding Lane Lines
This is the first project students complete, one week into the program.
They learn to work with images, color spaces, thresholds, and gradients, in order to find lane lines on the road. Stack: Python, NumPy, OpenCV
Traffic Sign Classifier
In this project, students train a convolutional neural network to classify traffic signs.
To do so, they use the German Traffic Sign Recognition Benchmark dataset. This particular student went above and beyond to train his network to not only classify signs, but also localize them within the image, and applied his classifier to a video. Stack: Python, NumPy, TensorFlow
Behavioral Cloning
Here, students record training data by manually driving a car around a track in a simulator.
Then they use this camera, steering, and throttle data to train an end-to-end neural network for driving the vehicle, based on NVIDIA’s famous research paper. Stack: Python, NumPy, Keras
Advanced Lane Finding
By applying advanced computer vision techniques, such as sliding window tracking, to a dashcam video, students are able to track lane lines on the road under a variety of challenging conditions. Stack: Python, NumPy, OpenCV
Vehicle Detection and Tracking
Students use machine learning techniques and feature extraction to identify and track vehicles on a highway. Stack: Python, NumPy, scikit-learn, OpenCV
Extended Kalman Filter
An extended Kalman filter merges noisy simulated radar and lidar data to track a vehicle. Stack: C++, Eigen
Unscented Kalman Filter
An unscented Kalman filter merges noisy, highly non-linear simulated radar and lidar data to track a vehicle. Stack: C++, Eigen
Kidnapped Vehicle
Students develop a particle filter in C++ to probabilistically determine a vehicles location relative to a sparse landmark map. Stack: C++
PID Controller
Students build and tune a proportional-integral-derivative controller to steer a vehicle around a test track, following a target trajectory. Stack: C++
Model Predictive Control
Students build and optimize a model predictive controller to steer a vehicle around a test track, following a target trajectory. Stack: C++, ipopt
Path Planning
In this project, students construct a path planner for highway driving based on a finite state machine.
The planner has three components: environmental prediction, maneuver selection, and trajectory generation. Stack: C++
Semantic Segmentation
Students train a pixel-wise segmentation network that identifies and colors road pixels to identify free space for driving. Stack: Python, TensorFlow
Safety Case
Students build a prototype of a safety case for a lane-keeping assistance ADAS feature, including the safety plan, hazard analysis and risk assessment, functional safety concept, technical safety concept, and software requirements.
Programming a Real Self-Driving Car
For this project, students form teams to drive a real self-driving car around the Udacity test track.
The car is required to negotiate a traffic light and follow a waypoint trajectory. Code is built first in the simulator, and then deployed to Udacity’s self-driving car in California. Stack: Python, ROS, Autoware, TensorFlow
Six months ago I wound up on a plane next to an executive from BYTON, an autonomous vehicle startup targeting the Chinese market.
At the time, I was unfamiliar with the company. But since then, BYTON has appeared more in the press. Most recently, they announced a partnership with Chris Urmson’s Aurora to power the autonomous stack in BYTON vehicles.
BYTON is notable for a few things.
First, the company seems to be a hybrid of China, Europe, and Silicon Valley, with leaders coming from all three locations. I wonder if more startups will be organized that way in the future.
Third, they are betting big on China’s electric vehicle mandate. The exact number or percent of vehicles that must be electric is a little hard to pin down, but it seems to be on the order of ten percent by 2020. BYTON is presumably hoping that being an electric-first vehicle company will give them an advantage.
To our friends at Alphabet: we are partners, you are an important investor in Uber, and we share a deep belief in the power of technology to change people’s lives for the better. Of course, we are also competitors. And while we won’t agree on everything going forward, we agree that Uber’s acquisition of Otto could and should have been handled differently.
My wife’s car is finally showing its age. At 14 years and 216,000 miles, it’s starting to remind us that it needs replaced.
So we’ve been car shopping, which has called my attention to the gap between the self-driving cars the world is gearing up for, and what’s actually out on the market.
Some major brands just got their first adaptive cruise control this year. In other cases, in order to get ADAS features, you have to step all the way up to the highest trim level.
Want an Autopilot-like experience? There are a few options, but they’re all on cars that sell for $60,000-plus.
I’m excited for where self-driving cars are taking us, and how quickly we are getting there. But going car shopping illustrates how far this must seem to people who don’t work in the space very day.
In this project, students build and train a deep neural network to classify images from the German Traffic Sign Recognition Benchmark dataset. There are about 40 different types of German traffic signs in the dataset, each 32×32 pixels big. That’s not very big!
Nonetheless, each image is big enough for students to train a convolutional neural network to recognize what type of sign it is, with 95%+ accuracy. That’s close to, or even better than, the accuracy that humans like you and I reach when we classify images by sight.
The lesson starts out with a tour of LeNet, one of the canonical network architectures for image classification. We step through how to implement LeNet in TensorFlow, highlighting data preparation, training and testing, and configuring convolutional, pooling, and fully-connected layers.
We also show students how to spin-up a GPU-enabled EC2 instance from our partners at Amazon Web Services. Thank you to AWS Educate for providing free AWS credits to Udacity students!
Ready to start learning how to build self-driving cars yourself? Great! If you have some experience already, you can apply to our Self-Driving Car Engineer Nanodegree program here, and if you’re just getting started, then we encourage you to enroll in our Intro to Self-Driving Cars Nanodegree program here!
“The first structural change is to imagine whether the car value chain becomes a little like the airplane value chain. When you book a flight today your primary loyalty is to a carrier. Does the carrier go to the city that I want does it go, when I want, and is the price right? And so you think mostly about a relationship that you have with Southwest or Delta or China Southern, depending on where you live and what loyalty plan you belong to. You don’t make a decision primarily on the type of aircraft because that’s a decision the airline makes.
The car value chain is not like that at all today. We make very personal decisions about the cars that we drive and the models that we drive and the options that we put into our cars.
But if we shift to self driving, this value chain could actually look a lot like the airline value chain. Your primary decision about who will drive you around will have to do with brand loyalty and safety and whether the fleet operator has the type of car and whether it’s close enough and how long it’s going to take for them to come pick you up and price and that type of thing. The make and model of the car will become the least important; in fact you don’t care about that decision in the same way that most of us don’t care about whether we’re riding in a Boeing or Airbus or Embraer airplane. So the value chain could end up looking a lot like the airline value chain.”
That’s a really interesting, and easy-to-grasp, take on the future of the mobility industry.
We talked about Udacity’s Flying Car Nanodegree Program, our upcoming free course on Baidu’s Apollo open-source self-driving car stack, why now is a great time to be in the industry.
.@Udacity and Baidu are teaching thousands of engineers how to leverage open-source technology to advance self-driving cars and autonomous vehicles. #CheddarLivepic.twitter.com/mlWiHJSiAu
David Silver 