"Our sense is management believes that investors still severely underestimates the impact of AI and the size of the potential market," Evercore analyst C J Muse wrote in a note on Friday after hosting Nvidia's management. Nvidia has been rapidly expanding into newer technologies including artificial intelligence, cloud computing and self-driving cars, away from designing graphics-processing chips for which the company was known for. Bank of America Merrill Lynch analyst Vivek Arya listed Nvidia a "top pick", basing his view "on (Nvidia's) underappreciated transformation from a traditional PC graphics vendor, into a supplier into high-end gaming, enterprise graphics, cloud, accelerated computing and automotive markets," according to Seeking Alpha. In May, Nvidia announced a partnership with Toyota Motor Corp through which the Japanese car maker would use Nvidia's AI technology to develop self-driving vehicle systems planned for the next few years.
SAE International has created the now-standard definitions for the six distinct levels of autonomy, from Level 1 representing only minor driver assistance (like today's cruise control) to Level 6 being the utopian dream of full automation: naps and movie-watching permitted. Many of the features of AI-assisted driving center around increased safety, like automatic braking, collision avoidance systems, pedestrian and cyclists alerts, cross-traffic alerts, and intelligent cruise control. A connected vehicle could also share performance data directly with the manufacturer (called "cognitive predictive maintenance"), allowing for diagnosis and even correction of performance issues without a stop at the dealer. Although it may not at first appear directly tied to automotive AI, the health and medical industry stands to experience some significant disruptions as well.
The two companies, which are among the leaders in the race to develop driverless car technology, officially announced their partnership in a blog post penned by Intel CEO Brian Krzanich. There were no financial terms revealed, and other details about the partnership were not disclosed, but we did gain some new insights about Waymo's driverless vehicle platform. We now know that Waymo was using Intel's tech well before the partnership was made official earlier today. Intel played a big part in developing Waymo's in-house self-driving hardware platform, which is used in its fleet of Chrysler Pacifica minivans.
The chipmaker admitted it had worked with the company during the design of its compute platform to allow autonomous cars to process information in real time. The announcement marked the first time Waymo, formerly Google's autonomous program, has acknowledged a collaboration with a supplier. Intel began supplying chips for then-Google's autonomous program beginning in 2009, but that relationship grew into a deeper collaboration when Google began working with Fiat Chrysler Automobiles (FCHA.MI) to develop and install the company's autonomous driving technology into the automaker's minivans. Intel began supplying chips for then-Google's autonomous program beginning in 2009.
Following the completion of the UK's largest collaborative trial of autonomous cars, which started in 2015, UK Autodrive has now been given the green light to start testing the driverless technology in public spaces around Milton Keynes and Coventry. Google's Waymo has now progressed to the point where it's cut the number of human interventions needed for its driverless cars by more than half and plans to start testing a minivan version this year. It's a community-generated navigation platform that allows drivers to add traffic congestion alerts and see them in real time. Beyond potential app integration, passenger data has even more promise.
In last week's Huawei Connect conference, Shenzhen's Traffic Police Technology Chief Li Quiang announced the launch of their Traffic Brain system. So it's no surprise that one of the most advanced traffic management systems in the world is being rolled out there first. This traffic management system represents some seriously advanced tech. But if the Shenzhen Traffic Brain system is proven to work, you can expect to see cities around the world rolling out their own versions over the coming decade.
The three technologies driving these changes are vehicle connectivity, artificial intelligence, and autonomous operating systems. The transmission will use that information, combined with data from GPS systems, mapping systems, internal route memorization, lateral controls and other systems, to shift smoothly, optimize fuel economy and keep a driver fully alert at all times. Ten years ago, truck and engine makers were adding a brand-new electronic control module to trucks to help manage exhaust aftertreatment systems required by new federal emissions regulations, says Jason Krajewski, manager of DTNA's connected vehicle insight team. "Since then, sensors and ECMs have been added regularly, with three or four new powerful number crunchers added in the past couple of years to handle data from new mapping systems and capacity for cameras, radar and active vehicle safety systems.
A revolutionary NASA Technology Demonstration Mission project called Dragonfly, designed to enable robotic self-assembly of satellites in Earth orbit, has successfully completed its first major ground demonstration. Over time, the system will integrate 3-D printing technology enabling the automated manufacture of new antennae and even replacement reflectors as needed. Vijay Kumar kicks things off with a talk about "research to enhance tactical situational awareness in urban and complex terrain by enabling the autonomous operation of a collaborative ensemble of microsystems." Next, Sean Humbert from UC Boulder talks about develping the fundamental science, tools, and algorithms to enable mobility of heterogeneous teams of autonomous micro-platforms for tactical situational awareness.
Fully autonomous cars are expected to dramatically increase driving safety when they eventually hit the roads, but it could cause new hazards for other road users. Researchers logged 150 hours of data over 1,800 miles and activated external signals on the car to gauge pedestrians' reactions. Standardization push: It would be a huge failure on the industry's part if different automakers come to market with different strategies for these types of signals, Shutko said. Ford and VTTI decided to explain the research after last month's media attention so that people wouldn't think the research project was "just a prank."
The proposed regulations preempt state regulation of vehicle design, and allow companies to apply for high volume exemptions from the standards that exist for human-driven cars. There is a new research area known as "explainable AI" which hopes to bridge this gap and make it possible to document and understand why machine learning systems operate as they do. The most interesting proposal in the prior document was a requirement for public sharing of incident and crash data so that all teams could learn from every problem any team encounters. The new document calls for a standard data format, and makes general motherhood calls for storing data in a crash, something everybody already does.