The SmartSociety Project’s got a slick concept video for real-time ridesharing!
The SmartSociety Project’s got a slick concept video for real-time ridesharing!
Comma.ai’s prototype offers similar prospective features as Tesla (Level 3 automation).
The goal is to sell the camera and software package for $1,000 a pop either to automakers or, if need be, directly to consumers who would buy customized vehicles at a showroom run by Hotz. “I have 10 friends who already want to buy one,” he says.
Sounds like a Chinese knockoff!
A friend introduced him to Musk, and they met at Tesla’s factory in Fremont, Calif., talking at length about the pros and perils of AI technology. Soon enough, the two men started figuring out a deal in which Hotz would help develop Tesla’s self-driving technology. There was a proposal that if Hotz could do better than Mobileye’s technology in a test, then Musk would reward him with a lucrative contract. Hotz, though, broke off the talks when he felt that Musk kept changing the terms. “Frankly, I think you should just work at Tesla,” Musk wrote to Hotz in an e-mail. “I’m happy to work out a multimillion-dollar bonus with a longer time horizon that pays out as soon as we discontinue Mobileye.”
“I appreciate the offer,” Hotz replied, “but like I’ve said, I’m not looking for a job. I’ll ping you when I crush Mobileye.”
Musk simply answered, “OK.”
Quanergy Systems, Inc. will introduce in early 2016 the world’s first solid state LiDAR (Light Detection and Ranging) sensor for self-driving cars for less than $1,000 per car, it was announced today at the Los Angeles Auto Show’s Connected Car Expo.
8-beam solid state (vs mechanical) LiDAR
Best headline ever.
Earlier this week, one of its cars was driving on the El Camino Real, a road with a speed limit of 35 mph. A police officer noticed that traffic was backing up on the road, caught up to the vehicle causing the jam, and realized it was one of Google’s koala-shaped cars, driving at a leisurely 24 mph. According to a blog post from the Mountain View Police Department, the officer wasn’t looking to give anyone (or anything) a ticket, but made contact with the car’s operators to learn more about how the car determines its speeds on certain roads—and to point out the dangers of impeding the regular flow of traffic.
When his Uber app notifies him about a surge, a price increase in an area where rider demand is high, Sollars knows just what to do: He drives his black Ford Escape somewhere else.
“The seasoned drivers don’t pay any attention to surge,” he said. “By the time you get to that part of the city, the surge is over. Often, even when I’m sitting dead center in the middle of a surge area, I don’t get a ride request. Then, as soon as the surge is off — bam! — here comes a ride.”
Still, by quashing demand, surge ensures that passengers who are willing to pay more can get a ride quickly, achieving its basic goal. “One side is working; it definitely impacts demand, but the impact on supply is minimal,” Wilson said.
“They missed a lot of what surge does with respect to supply,” said Keith Chen, a UCLA associate economics professor currently on a two-year leave to work with Uber as its head of economic research, designing the third iteration of its surge system. During busy times, the study’s method would miss cars that “got gobbled up” by riders and instantly replaced by other cars, he said.
Idea: perhaps Uber should provide an estimated duration for the surge to its drivers.
Paper: Peeking Beneath the Hood of Uber, IMC 2015
How did PhantomAlert discover the theft?
PhantomAlert claims that it became aware of the data theft after realizing that Waze displayed its proprietary information. The traffic app maker claimed that Waze used information for which they never obtained authorization or consent. PhantomAlert goes ahead to say that Waze not only copied it’s database, but also went a step further and incorporated the same on its platform. PhantomAlert made these claims through its lawyers when filing the lawsuit against Waze.
RideWith uses technology developed by Waze, an Israeli start-up bought by Google in 2013 for about $1bn.
Its navigation system, which uses data from users’ smartphones to give live traffic information, learns the routes drivers most frequently take to work and matches them up with people wanting to travel in the same direction.
It is aimed at people who work for the same company and live reasonably close to each other.
An estimated 200,000 people participate in carpooling in Israel already.
Thus Waze knows exactly where we live and work, as well as our preferred routes for getting between the two. Moreover, they know precisely the time that we leave these locations, even if we have not activated the app on our devices.
It is clear to see how Google is tip toeing around now, so as not to broadcast a clear and present threat to the local cabbies, and avoid confrontations with regulators who in turn could cause a legal fuss for their users. Google is calling this a “ride sharing service”, saying that it is a “green and social way to get to work”. They have even gone so far as to euphamize the payment system, saying that users are “pitching in”, just like people have done for years with a few bucks for gas when their friend gives them a ride.
The results are interesting, if predictable. In general, people are comfortable with the idea that self-driving vehicles should be programmed to minimize the death toll.
This utilitarian approach is certainly laudable but the participants were willing to go only so far. “[Participants] were not as confident that autonomous vehicles would be programmed that way in reality—and for a good reason: they actually wished others to cruise in utilitarian autonomous vehicles, more than they wanted to buy utilitarian autonomous vehicles themselves,” conclude Bonnefon and co.
And therein lies the paradox. People are in favor of cars that sacrifice the occupant to save other lives—as long they don’t have to drive one themselves.
Enjoyed reading a comprehensive working paper from the Harvard Business School on the upsides, downsides, and potential downsides of two-sided markets (aka platforms); implications and challenges for the legal system with respect to regulating these new types of services. The paper suggests ending “protectionist” regulation and discusses negative externalities (many of which have initial evidence but require further study), asymmetry in information (as compared to incumbent services), cognitive biases such as racial profiling based on profile picture, not being incentivized to provide “universal service”. The paper provides a nice overview of both driver and consumer perspectives, concerns, and challenges.
New software platforms use modern information technology, including full-featured web sites and mobile apps, to allow service providers and consumers to transact with relative ease and increased trust. These platforms provide notable benefits including reducing transaction costs, improving allocation of resources, and information and pricing efficiencies. Yet they also raise questions of regulation, including how regulation should adapt to new services and capabilities, and how to correct market failures that may arise. We explore these challenges and suggest an updated regulatory framework that is sufficiently flexible to allow software platforms to operate and deliver their benefits, while ensuring that service providers, users and third parties are adequately protected from harms that may arise.
It appears that the potential and role of government in regulating these services is to provide sustainability of these services (whether through private or public, one or many players); this means long-term and consistent reliability and wide-spread and fair access. The role of government is to look beyond the capitalistic systems upon which corporations operate and look beyond the short-sighted-ness of individual citizens, as a way to protect it’s people.
Regulation can usefully set minimum standards to protect consumers who fail to recognize potential problems and to protect against problems prior consumers could not notice… Many long standing transportation requirements address aspects of safety that customers would struggle to access even after a ride–for example, requiring vehicle inspection with heightened frequency or rigor.
What is role of statistics and control theory in these questions of providing assurances to the consumer?
Citation: Edelman, Benjamin G., and Damien Geradin. “Efficiencies and Regulatory Shortcuts: How Should We Regulate Companies like Airbnb and Uber?” Harvard Business School Working Paper, No. 16-026, September 2015.