Primarily intended to transport heavy duty goods like oil, it rocks a sporty and futuristic look.

Basically, the plan is to make an electric autonomously driving truck with two options:

Option A is an electric truck autonomously driving for highway. Option B is an electric truck show car.

Audi decided to develop both directions to choose the best one. Plan failed. They were unable to choose the best one, so They are presenting you both versions.

Because people gravitated toward ride-hailing from other transportation modes, the authors conclude that ride-hailing is contributing to more vehicle miles being driven in major cities.

For people who began to use ride-hailing services, their public transit use was reduced 6%, according to the study. Although it revealed a reduction in bus service and light rail use following the use of Lyft and Uber, an uptick in commuter rail service occurred. It's unclear why.

The study surveyed more than 4,000 people in Boston, Chicago, Los Angeles, New York, Washington D.C., Seatlte and San Francisco between 2014 and 2016.

"Everyone is still working to figure out how to get the benefits of [ride-hailing services] and managing the problematic aspects," said Bruce Schaller, a transportation consultant who has documented how ride-hailing has increased traffic in New York City. "The fact is people are often paying more than they paid before to get around, so it must be [working well] on an individual level. The issue is to make sure it works for the city and the society as a whole."

It takes a lot more space on the road to transport a person in a car, compared to a bus, bicycle or subway system. If citizens continue to gravitate toward ride-hailing, there will be increased strain on areas that are at or near capacity, the study suggests.

In response to the study, a Lyft spokesperson told CNN Tech the company will focus on increasing occupancy in cars and working with transit agencies to provide efficient, affordable transportation options to make car ownership a thing of the past.

Uber did not immediately respond to a request for comment.

A big question in the transportation world is whether ride-hailing services will complement or replace public transportation. Some previous research has shown ride-hailing is a complement to existing systems.

According to Schaller, if the convenience and reliability of ride-hailing motivates public transportation systems to improve their services, that's a good thing for cities. But if government leaders and the public see less of a need for it amid growth from companies like Uber and Lyft, it'll only add to the congestion problem.

"People need to be able to move in cities in a way that they're able to enjoy their activities that doesn't completely clog our streets," Regina Clewlow, the report's author, told CNN Tech.

Clewlow highlighted several ways to improve mobility in the ride-hailing era, including congestion pricing and priority lanes for high-occupancy vehicles.

She also stressed the importance of cities receiving more data from mobility companies, so that they aren't planning transportation in the dark.

Google has never publicly shared how much it spends on its self-driving cars. At first, Project Chauffeur was hidden away in Google’s ultra-secret X moonshot program. When that went public, its costs were bundled together in a vague “Other Bets” category that includes the company’s fiber Internet service, home automation, and life science spin-offs.

Now, a court filing in Waymo’s epic and ongoing lawsuit against Uber has accidentally revealed just how big a bet Google placed on autonomous vehicles. Between Project Chauffeur’s inception in 2009 and the end of 2015, Google spent $1.1 billion on developing its self-driving software and hardware, according to a recent deposition of Shawn Bananzadeh, a financial analyst at Waymo.

Bananzadeh was testifying as part of the lawsuit, in which Uber stands accused misappropriating trade secrets and violating patents from Waymo, Google’s self-driving-car offshoot. Because Waymo has yet to commercialize any of its technology in a meaningful way, the company thinks any damages in the case should be calculated on the basis of how much it spent building the technology in question..

When asked by an Uber lawyer how an estimate for developing one of the trade secrets, number 90, was arrived at, Bananzadeh replied: “My understanding is that it is a cost that captures the entire program spend from inception to the period of time where it stops.” He later clarified that meant from 2009, when Sebastian Thrun got the go-ahead for the project from Larry Page, to the end of 2015.

Throughout Bananzadeh’s deposition, every dollar amount was redacted to protect Waymo’s confidential commercial information. Every time, that is, except in the Uber lawyer’s very next question: “The calculation that was the basis of the $1.1 billion cost estimate for Trade Secret 90 is the same calculation that was done for Trade Secret 2 and Trade Secret 25?”

Waymo had apparently given an identical $1.1 billion cost estimate for each of the trade secrets being discussed. Bananzadeh was unable to provide a clear answer as to why that might be, except to say, “Insofar as it is part of the entirety of this self-driving system…. therefore, all of the costs of the program since inception… are what then informs that number.”

Waymo’s position seems to be that all of its trade secrets are inextricably linked to the whole self-driving car project, and any damages should reflect that fact.

In a filing, Otto Trucking called Waymo’s damages theory “entirely speculative” and “over the top,” and called on the court to forbid Waymo from offering any evidence or argument beyond the actual damages it has incurred.

Though $1.1 billion is unquestionably a massive figure, it actually seems quite reasonable compared to the recent over-heated market for self-driving car acquisitions. In March 2016, General Motors paid a billion for San Francisco–based Cruise Automation, a company that was a seller of after-market semi-autonomous vehicle kits. In February of this year, Ford invested the same amount in a joint venture with Argo AI, a two-month-old Pittsburgh start-up headed by a former Google self-driving car engineer. The largest self-driving acquisition to date, however, was Intel’s $15.3 billion purchase of Mobileye in March. The Israeli company had originally provided vision-based semi-autonomous technology for Tesla vehicles.

Uber shelled out a reported $680 million for self-driving truck maker Otto in August 2016, sight almost unseen. But it’s the circumstances surrounding the acquisition of Otto, and in particular its lidar technology, that are at the heart of Waymo’s case against Uber. Otto’s founder, Anthony Levandowski, allegedly had a draft contract for the purchase of the company before he even quit Google.

By spending its money earlier than others and mostly in-house, Google’s billion-dollar investment now looks relatively modest—almost a bargain. Waymo has, by far, the most sophisticated self-driving software. It has simulated over a billion miles of driving, and its cars have had the most self-driving experience on real streets (over 3 million miles in multiple cities).

The court case seems to suggest that Waymo has also built up an enviably solid platform of intellectual property. So, undesirable as this peek into its books might be for Google today, the company should pride itself on demonstrating that in-house R&D can still make a lot of financial sense.

Yes, you read that correctly: Ford put a man in a car seat disguise so that a Ford Transit could masquerade as a true self-driving vehicle. Why? To evaluate how passers-by, other drivers on the road and cyclists reacted to sharing the road with an autonomous vehicle.

The trial, conducted with the Virginia Tech Transportation Institute, also made use of a light bar mounted on the top of the windshield to provide communication about what the car was doing, including yielding, driving autonomously or accelerating from a full stop.

So why engage in such elaborate dress-up, especially when it’s not even Halloween? Mainly because you actually still do need to have someone behind the wheel in real-world testing, and also because for the purposes of this experiment, Ford and VTTI didn’t actually need a self-driving car – they just needed people to believe wholeheartedly they were using one.

The Transit Connect van used for the trial would indicate its behavior using signals including a slow white pulse for yielding, a rapid blinking for accelerating from a stop, and staying solid if it’s actively in self-driving mode. The bar is positioned roughly where a driver’s eye line would be, to try to catch the attention of those around it who would look in its direction

Ford’s chosen signals for the project are simple, but they’re intended to be, and they’re designed to not just replicate existing vehicle signalling apparatus, like break lights and turn signals, but to fill in gaps where we currently communicate via subtle gestures, eye contact and other less obvious mechanisms.

Ford and VTTI conducted VR testing to discover that these definitely need to be learned – people need a few different exposures before they clue in. But there’s potential for them to become widely accepted, provided they’re repeated often and consistently.

The test is just the start, though already Ford and VTTI have run 150 hours of tests covering around 1,800 miles in their urban testing ground, with a dense concentration of pedestrians, other drivers and cyclists. The eventual goal is to continue with light signal research, and then to work together with industry standards organizations including the International Organization for Standardization and SAE International to make these shared in common across automotive and transportation companies.

There’s a lot of work ahead – I hope that seat costume is comfortable, since previous similar examples suggest wearable upholstery isn’t.

A new Far Infrared (FIR) sensor from Israeli company AdaSky could help bring vehicles with Level 3, 4 and 5 autonomy features to market faster. The solution is the first to offer an FIR camera with built-in machine learning software for processing the results, in a solid-state form factor with no moving parts, at a cost that AdaSky says is “suited for mass market” use.

AdaSky’s solution is called “Viper,” and it works by passively collecting FIR signals sent out as heat by warm objects, including other cars and humans. Its range means that it can pick up signals from as many as a few hundred meters away, allowing it to detect pedestrians earlier than other sensors, and work in concert with different tech including LiDAR and traditional cameras to verify that pedestrians are in fact pedestrians ahead of time.