June-July EV highlights from Clean Technica’s Zach Shahan. Aug 2018
UPS started testing electric heavy-duty trucks from Californian startup Thor. UPS also placed an order for a whopping 950 Workhorse N-GEN electric delivery vans. I just had to double check, but that’s right — 950!
Kia Niro EV now on sale in South Korea. Maybe we’ll see it in California and Alabama soon. Or not.
The Hyundai Kona EV, a cousin of the Kia Niro, landed a whopping 7,000 orders in Norway in just two weeks on the market there. Let’s hope Hyundai lines up the batteries.
Nissan announced that a new LEAF was being sold every 10 minutes in Europe. That sounds like a lot. That said, it’s nothing close to Tesla Model 3 demand. (CleanTechnicareport coming.)
Volkswagen announced it will manufacture electric cars in the United States. Volkswagenwill also offer the market electric vehicles for carsharing via its WE service — starting in Germany in 2019 and then expanding to large cities in Europe, North America, and Asia.
Jaguar says it is investing $18 billion into electric drivetrain development in the next three years.
Porsche, meanwhile, bought a 10% stake in noted Croatian EV startup Rimac Automobilii.
BMW’s US plug-in vehicle sales are now 7% of all BMW passenger car sales in the country, making it a leading plug-in vehicle automaker in relative terms.
BYD joined the CharIN Ev charging standard association.
Boeing and the Japan Aerospace Exploration Agency announced they are working on lidar systems for electric flight.
Equator Aircraft’s P2 Xcursion electric-hybrid seaplane prototype took its maiden flight.
The 2019 Alta Redshift EXR electric motorcycle was rolled out.
European EV sales rose 37% in June 2018 compared to June 2017.
Chinese EV sales rose 77% in June 2018 compared to June 2017.
In the Netherlands, EV sales rose 141% and Tesla had a record sales month.
In France, EV sales rose a much more modest 13%.
In the US, Loren McDonald noted that new EV models are now accounting for the majority of EV sales.
Tesla sales have skyrocketed. After passing a production target of 5,000 units a week, the Model 3 is actually the 7th or 8th best selling car in the United States and is crushing competitors in the small & midsize luxury car market. Furthermore, Tesla passed its 200,000th US delivery, kicking in the long phaseout of the federal EV tax credit for Tesla buyers (unless that tax credit policy gets modified). Actually, Tesla’s production and sales rate has seemingly passed up Jaguar’s and Porsche’s. The company also announced a Shanghai Gigafactory and noted that the project would likely be funded via local bank loans. Tesla is also looking at building a Gigafactory in Germany. On the autonomous side of things, Tesla has been quietly developing much quicker and better Autopilot tech. Want a deep dive into this topic? You can watch Tesla’s director of AI chat about developing a neural network for Autopilot here. On the battery front, the Silicon Valley company emphasized that it is aiming to get cobalt content in its batteries down from 3% to 0%. Additionally, Panasonic noted that it is increasing battery cell production 30% by the end of this year to meet growing Tesla demand. And … Tesla opened its 10,000th Supercharger.
Waymo has partnered with Walmart & 4 other companies for self-driving taxi service.
Waymo’s Jaguar I-PACE vehicles are also just hitting the streets of San Francisco.
Sacramento is getting electric carsharing via VW settlement funding for its diesel emissions scandal. It’s great to see dirty money put to good use!
More from Clean Technica: Tesla Director Of AI Discusses Programming A Neural Net For Autopilot (Video) June 11th, 2018 by Kyle Field
Tesla’s Director of AI, Andrej Karpathy, took to the stage at TRAIN AI 2018 and then proceeded to unpack the company’s approach to building its Autopilot computer vision solution. His talk was titled, “Building the Software 2.0 Stack.”
Andrej took on the task of delineating traditional rule-based programming methods from the programming methods used when a neural network — also known as machine learning or artificial intelligence — runs the show. In typical internet lingo, he dubs neural net programming software 2.0, with rule-based programming taking up the software 1.0 moniker.
It turns out that the differences are considerable and programming a neural net is very different from programming a webpage or smartphone app. This has become increasingly evident in recent years as computer vision has struggled to define rules for every possible object in an image that could be identified. Being hard did not stop programmers from trying and even executing extremely complex computer vision analysis.
Early learnings from 1990–2010 in the analysis of photos laid the foundation for the modern focus on video image analysis, which, with their higher frame rates, put significantly more strain on computer resources. Applications like Tesla’s Autopilot require that all processing to be real-time, even using real-time data to predict what nearby drivers will do or might do, in order to mitigate the impact. [Editor’s note: This heavy use of computer processing for high-quality autonomous driving is a key reason George Hotz, aka geohot, has stated that Tesla is head and shoulders above conventional automakers. See: “Geohot: Tesla Autopilot = Apple iOS, Comma.ai = Android (CleanTechnica Exclusive)” for more on that.]
Tesla’s Autopilot solution relies heavily on computer vision, rather than lidar and other sensors, as Tesla’s team believes that it is fundamentally superior and that a robust array of cameras is more than sufficient to support a full self-driving solution.
Andrej kicks things into high gear in minute 15 when he digs into the approach Tesla’s team used to cracking the computer vision nut for Autopilot. Tesla’s Autopilot programming team is broken into two major groups. The first team builds the neural net itself, while the second group focuses on the actual programming of the neural net, which consists of selecting a set of labeled images that the neural net will learn from.
Just as programming code had to be efficient and effective, Andrej notes that the images used to program the neural net must be large, varied, and clean. Programming a neural net is much more about identifying the abnormalities and programming the software 2.0 stack for the proper behavior than it is about programming the system for normal situations.
An easy way to think about programming a neural net with images is the traffic signals at intersections. Most have the standard red-yellow-green stack and can be modeled by providing images of a red light and labeling that as the signal indicating the vehicle should stop. Conversely, a green light indicates that the vehicle can continue through the intersection. Yellow is an equally important indicator but appears much less frequently than its red and green bedfellows. The neural net must be programmed to understand all three equally well, even though the frequency of yellow lights is much lower than green and red in the real world.
Fundamentally, Tesla believes its Autopilot solution will deliver a much safer driving experience while on the road with cars operated by humans. That’s meaningful and important today but only hints at the broader possibility of a vehicle that can drive itself in any situation on the road, anywhere in the world. Tesla’s self-driving cars deliver a 4× reduction in fatalities today and CEO Elon Musk believes he can deliver at least a 10× improvement vs human drivers in the future.
Andrej noted that Tesla has the largest deployment of robots in the world, with 250,000 on the road today with varying degrees of autonomous driving capability due to the hardware each has onboard. Tesla has not achieved “Full Self-Driving” today, but it is so confident that it will be able to get there that it is already selling Full Self-Driving as an option on new Model S, X, and 3 orders.
Andrej’s full 30 minute talk is worth watching for the data geeks out there who want to stay up to date on the evolution of self-driving vehicles and computer vision … or you can skip to minute 15 for the update on Tesla’s approach to computer vision for its Autopilot solution.
Check out the video of his talk on Vimeo, as it cannot be embedded due to its security settings.