cars Archives - Business Intelligence Info

Tag Archives: cars

AI Weekly: Autonomous cars need better safety metrics to move the industry forward

January 10, 2020 Big Data

On Monday, Waymo — the subsidiary of Google parent company Alphabet that’s developing a full-stack driverless vehicle platform — announced that its cars have driven a combined 20 million autonomous miles to date, up from 10 million miles in October 2018. The metric signifies Waymo’s logistical and technological superiority, implied CEO John Krafcik, who equated the miles driven to 1,400 years of driving experience for an average American.

But some experts assert that measuring driverless systems’ progress by miles is a flawed approach.

This week in a conversation with VentureBeat at the 2020 Consumer Electronics Show (CES), Dmitry Polishchuk, the head of Russian tech giant Yandex’s autonomous car project, said that miles aren’t very meaningful without context to accompany them. “It’s tough to directly compare miles driven,” he said. “Obviously, the more miles [you] have, the better, but we believe that the environments that you’re in have a huge impact.”

Yandex isn’t without a horse in the race — its over 100 autonomous cars in Innopolis and Skolkovo, Russia; Las Vegas; and Tel Aviv have driven 1.75 million miles as of January, up from 1.5 million miles and 1 million miles last December and October, respectively. But policymakers as well as competitors in the nearly $ 41.25 billion global autonomous car segment have expressed similar sentiments.

Noah Zych, head of system safety at Uber’s Advanced Technologies Group, told Wired in an interview that mileage critically omits details like situations encountered, obstacles, and accidents. “You need to know … ‘What was the objective of the testing in [any given area]?” he said. “Was it to collect data? Was it to prove that the system was able to handle those scenarios? Or was it to just run a number up?”

And at a conference organized by Nvidia in Washington two years ago, Derek Kan, U.S. secretary for policy at the U.S. Department of Transportation, stressed the need for objective and agreed-upon measures of driverless systems performance. Separately, David Friedman, former acting administrator of the National Highway Traffic Safety Administration (NHTSA) and vice president at Consumer Reports, recently urged Congress to direct the NHTSA to implement privacy protections, minimum performance standards, and accessibility rules for self-driving cars, trucks, SUVs, and crossovers.

Disengagements — or deactivations of cars’ autonomous modes when failures occur or when drivers are forced to take over — have been adopted by agencies including California’s Department of Motor Vehicles as an alternative to miles driven. (By law, companies actively testing self-driving cars on public roads in the state are required to publish disengagement reports.) But Polishchuk argues that this, too, is an imperfect metric.

“We have kind of been waiting for some sort of industry standard,” he said, noting that Yandex hasn’t yet released a disengagement report. “Self-driving companies aren’t following the exact same protocols for things. [For example, there might be a] disengagement because there’s something blocking the right lane or a car in the right lane, and [the safety driver realizes] as a human that [this object or car] isn’t going to move.”

For its part, whenever Yandex deploys new code into production, the company conducts real-world tests to ensure that systems performance (and by extension, safety) isn’t degraded. It takes 10 cars — five equipped with the codebase from half a year ago and five with the latest code — and it runs them for a day on the same route such that they encounter identical obstacles and weather conditions. It even switches up the safety drivers behind the wheel to prevent bias from influencing the results.

“We look back at the numbers and check the correlation … using hundreds of different parameters,” said Polishchuk. “The absolute number of disengagements doesn’t matter.”

Unfortunately for companies like Yandex, less regulatory guidance — not more — seems the likelier near-future path, at least in the U.S. At CES on Wednesday, Transportation Secretary Elaine Chao announced Automated Vehicles 4.0 (AV 4.0), new guidelines regarding self-driving cars that seek to promote “voluntary consensus standards” among autonomous vehicle developers. It requests but doesn’t mandate regular assessments on self-driving vehicle safety, and it permits those assessments to be completed by automakers themselves as opposed to by a standards body.

Advocacy groups including the Advocates for Highway and Auto Safety criticized the policy for its vagueness. “Without strong leadership and regulations … [autonomous vehicle] manufacturers can and will continue to introduce extremely complex supercomputers-on-wheels onto public roads … with meager government oversight,” said president Cathy Chase in a satatement. “Voluntary guidelines are completely unenforceable, will not result in adequate performance standards, and fall well short of the safeguards that are necessary to protect the public.”

Indeed, regulation could go a long way to convincing a skeptical public.

Two studies — one published by the Brookings Institution and another by the Advocates for Highway and Auto Safety (AHAS) — found that a majority of Americans aren’t convinced of driverless cars’ safety. More than 60% of respondents to the Brookings poll said that they weren’t inclined to ride in self-driving cars, and almost 70% of those surveyed by the AHAS expressed concerns about sharing the road with them. Elsewhere, a study conducted by think tank HNTB found that 59% of people expect self-driving cars will be “no safer” than cars driven by humans.

In the U.S., legislation remains stalled at the federal level, unfortunately. More than a year ago, the House unanimously passed the SELF DRIVE Act, which would create a regulatory framework for autonomous vehicles. But it has yet to be taken up by the Senate, which in 2018 tabled a separate bill, the AV START Act, that made its way through committee in November 2017.

Polishchuk predicts that legislation will only emerge when some “reasonable amount” of self-driving cars hit public roads. Optimistic projections peg the number at 10 million by 2030. “When this happens, we would have statistics, and basically, statistics will push regulators,” he said.

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers and AI editor Seth Colaner — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

Let’s block ads! (Why?)

Big Data – VentureBeat

Baidu secures licenses to test self-driving cars in Beijing

January 1, 2020 Big Data

Tech giant Baidu is slowly but surely progressing toward the launch of a commercial robot-taxi fleet in mainland China. This week, the company announced that it has secured 40 licenses to test driverless cars carrying passengers on designated roads on Beijing, making it one of the first to do so in the Chinese capital. It also revealed that its self-driving cars have traveled more than three million kilometers (or about 1.8 million miles) during tests in 23 Chinese cities to date, up from 2 million kilometers (roughly 1.2 million miles) across 13 cities as of July.

Baidu vice president Zhang Dongchen reiterated the company’s commitment to road safety in a statement (translated): “Beijing’s achievements in the field of autonomous driving are obvious to all, and it is undoubtedly [reflective] of the development of China’s autonomous driving industry. Baidu … will strictly abide by the relevant road test management regulations, fully ensure the safety of road test, and continuously improve its self-driving technology, so that a simpler and better transportation life will come soon.”

As of the end of December, Beijing has allowed 151 roads totaling more than 500 kilometers (approximately 310 miles) in length to be used for autonomous vehicle tests, although the roads are closed to the public during testing. China has designated five levels for self-driving test permits ranging from T1 to T5, which are similar (but not necessarily analogous) to the automation levels issued by the Society of Automotive Engineers.

The Beijing Municipal Commission of Transport allocated its entire first batch of T4 autonomous driving test permits to Baidu earlier this summer, in July. The company noted at the time that the T4 — China’s highest-level permit — is an open-road test license, enabling it to deploy prototypical vehicles on urban roads, tunnels, school zones, and elsewhere.

Ongoing partnerships

In related news, Baidu recently inked a strategic partnership with Geely to equip the Hangzhou, China-based automaker’s cars with DuerOS for Apollo, a set of AI-based IoV solutions with voice assistant, augmented reality, and motion detection capabilities. The company is also collaborating with Chinese state-owned car company FAW Group, which develops the Hongqi line of luxury cars, to deploy driverless vehicles in the Hunan capital of Changsha within the next few months.

Baidu’s other automotive partners include Ford, with which it embarked last year on a two-year project to test self-driving vehicles on Chinese roads. The tech giant separately inked a deal with Volvo to develop autonomous electric cars specifically for the Chinese market, and in 2017, it launched a $ 1.52 billion driving fund — the Apollo Fund — as part of a wider plan to invest in 100 autonomous driving projects over the next three years.

Perhaps not coincidentally, Changsha will serve as the pilot site for Apollo Go, Baidu’s ongoing robo-taxi project. A future 5G network and an “intelligent” roadway upgrade dubbed the Apollo Intelligent Vehicle Infrastructure Cooperative System — both of which are part of Hunan’s smart city initiative — will lay the groundwork for what Baidu is claiming will be China’s largest self-driving taxi fleet. When it rolls out in earnest, customers will be able to hail a ride via the Apollo Go smartphone app, and the cars will integrate with smart road infrastructure to “improve safety.”

Apollo Go and Apollo 5.0

Today’s announcement comes months after the release of Apollo 5.0, the latest version of Baidu’s open source autonomous driving platform, which launched in April 2017. It enables dynamic calibration for vehicles in just 30 minutes via the cloud at a rate of 100 vehicles per minute, and it ships with Baidu’s Valet Parking framework, a “cost-effective method for transforming traditional parking facilities into smart infrastructure.”

Baidu claims its Apollo-based intelligent solutions have helped to reduce traffic delays in cities like Baoding, Hebei province by 20-30%. Moreover, it says the Apollo family now spans 156 partners, more than 60 auto brands, and over 300 vehicle models. Notable collaborators include Chinese automobile manufacturers Chery, BYD Auto, and Great Wall, in addition to Hyundai Kia, Ford, and VM Motori.

Apollo has grown to 400,000 lines of code (more than double the 165,000 lines of code the company announced in January 2018), and it’s now being tested, contributed to, or deployed by big-name brands like Intel, Nvidia, and NXP. According to Baidu, the number of developers who have sourced Apollo’s code from the project’s GitHub repository stands at 12,000, a 20% increase from mid-2018.

Baidu seeks to gain the upper hand over rivals like Tencent and Alibaba. In April, Alibaba confirmed that it has been conducting self-driving car tests with the goal of achieving Level 4 autonomous capability and said that it’s looking to hire as many as 50 engineers for its AI research lab. And in May, Tencent secured a license from the Chinese government to begin testing autonomous cars in Shenzhen, China.

Baidu and its rivals are racing toward a veritable goldmine of a market. Autonomous vehicles and mobility services in China are expected to be worth more than $ 500 billion by 2030, according to a McKinsey report, when roughly 8 million self-driving cars hit public roads.

Let’s block ads! (Why?)

Big Data – VentureBeat

MIT CSAIL teaches autonomous cars to navigate occluded intersections safely

November 5, 2019 Big Data

Truly autonomous vehicles — those without human safety drivers at the wheel — must be capable of determining when it’s safe to merge into traffic. Intersections with obstructed views make this somewhat challenging, but researchers at Toyota and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) say they’ve developed an AI model that can estimate collision risk highly accurately.

The key turned out to be uncertainty. The model — which the team designed specifically for junctions that lack a stoplight, forcing cars to yield for traffic — weighs several factors in tabulating risk, including visual occlusions, sensor noise and errors, the speed of other cars, and the attentiveness of other drivers. It also considers how long it’d take the car to steer through the intersection, along with all safe stopping spots for crossing traffic.

The model splits the road into segments, enabling it to determine if any one section is occupied by another car. The moment a passing car travels into a segment, its speed informs a prediction of the car’s progression through subsequent segments. Simultaneously, the model considers the road segments the car passed through before the intersection, the rationale being that cars occupying a high number likely spotted the autonomous car.

The aforementioned risk estimate is updated continuously. In the presence of multiple occlusions, the model directs the car to nudge forward in order to reduce uncertainty. And when the risk bottoms out, the model has it drive through the intersection without stopping so as to avoid increasing the risk of collision by lingering.

The team managed to run the model on remote-control cars in real time, suggesting it’s efficient and fast enough to deploy into full-scale autonomous test cars in the near future. They concede that it needs more rigorous testing, but they believe it could serve as a supplemental risk metric that an autonomous vehicle system can use to better reason about driving through intersections safely.

“When you approach an intersection there is potential danger for collision. Cameras and other sensors require line of sight. If there are occlusions, they don’t have enough visibility to assess whether it’s likely that something is coming,” said director of CSAIL Daniela Rus in a statement. “In this work, we use a predictive-control model that’s more robust to uncertainty, to help vehicles safely navigate these challenging road situations.”

As a next step, the researchers aim to incorporate risk factors such as the presence of pedestrians in and around the road junction in the model.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tier IV raises over $100 million to develop open source software for driverless cars

July 6, 2019 Big Data

Tier IV, a Japan-based driverless car software maintainer and provider, this week announced the closure of a round north of $ 100 million led by Sompo Japan Nipponkoa Insurance, with participation from Yamaha Motor, KDDI, JAFCO, and Aisan Technology. The fresh capital brings the company’s total raised to nearly $ 130 million following seed rounds totaling $ 28 million, and founder Shinpei Kato said it’ll fuel the global commercialization and expansion of Tier IV’s self-driving technology platform.

“Tier IV has a mission to embody disruptive creation and creative disruption with self-driving technology. We have derived a solid software platform and successfully integrated it with real vehicles,” said Kato. “It is time to step forward to real services, embracing functional safety and risk management.”

Tier IV, a University of Tokyo spinout founded in December 2015, spearheads the development of Autoware, which it describes as an “all-in-one” open source and BSD-licensed solution for autonomous vehicles. The platform supports things like 3D localization and mapping, 3D path planning, object and traffic signal detection, and lane recognition, plus tasks like sensor calibration and software simulation.

Tier IV funds this development in part by selling support equipment like remote controllers and logging devices, as well as desktops and laptops with Autoware preinstalled. Additionally, it offers subscription access to its data sets, labeling tools, and deep learning training services for $ 1,000 per year.

Tier IV’s stated mission is to “democratize” intelligent cars by enabling “any individual or organization” to contribute to their development. To this end, it and partner companies Apex.AI and Linaro 96Boards launched the nonprofit Autoware Foundation last December, which seeks to deploy Autoware in production products and services. The Foundation counts 30 companies among its membership, and Tier IV claims that Autoware has already been adopted by more than 200 organizations around the world to date, including Udacity (for its Nanodegree Program), the U.S. Department of Transportation Federal Highway Administration (in its CARMA platform), automotive manufacturers, and “many” self-driving startups.

Field tests of Autoware-powered cars have been conducted in over 60 regions in Japan and overseas “without incident,” according to Tier IV, and the company claims that vehicles running on its platform achieved level 4 autonomy (meaning they could operate safely without oversight in select conditions) as early as December 2017.

Tier IV competes to an extent with Baidu, which offers an open source driverless software stack of its own in Apollo. The Beijing-based tech giant claims that Apollo — which has grown to 400,000 lines of code, more than double the 165,000 lines of code the company announced in January 2018 — is now being tested, contributed to, or deployed by Intel, Nvidia, NXP, and over 156 global partners, including 60 auto brands. Notable Apollo collaborators include Chinese automobile manufacturers Chery, BYD Auto, and Great Wall, Hyundai Kia, Ford, and VM Motori.

Sign up for Funding Daily: Get the latest news in your inbox every weekday.

Let’s block ads! (Why?)

Big Data – VentureBeat

Here’s how Cruise’s autonomous cars navigate double-parked cars

June 27, 2019 Big Data

Cruise, the driverless car startup acquired by GM for $ 581 million in 2016, today detailed in a blog post how its fleet of over 180 self-driving Chevrolet Bolts are learning to anticipate human drivers’ behaviors. It’s part of a new series the company is publishing on Medium called How Self-Driving Cars Think, each installment of which will spotlight a different component of Cruise’s autonomous stack.

“Every day, San Franciscans drive through six-way intersections, narrow streets, steep hills, and more. While driving in the city, we check mirrors, follow the speed limit, anticipate other drivers, look for pedestrians, navigate crowded streets, and more,” wrote Cruise software engineer Rachel Zucker and staff software engineer Shiva Ghose. “In SF, each car encounters construction, cyclists, pedestrians, and emergency vehicles up to 46 times more frequently than in suburban environments, and each car learns how to maneuver around these aspects of the city every day.”

One of these obstacles is double-parked cars — lots and lots of double-parked cars. The probability of encountering one in downtown San Francisco is 24:1 compared with the suburbs, according to Cruise, making learning to maneuver around them safely a necessity.

In order to do this, Cruise’s cars must first identify them, which they accomplish by “looking” for a number of cues such as vehicles’ distance from road edges, the appearance of brake and hazard lights, and distance from the furthest intersection. They additionally use contextual cues like vehicle type (delivery trucks double-park frequently), construction activity, and the relative scarcity of nearby parking.

Cruise’s Bolts perceive these things through sensors — specifically lidar sensors from Velodyne, as well as short- and long-range radar sensors, articulating radars, and video cameras. Cameras recognize vehicle indicator light state and road features (such as safety cones or signage), while lidars and radars measure distance and speed, respectively. Then, machine learning models running on onboard computers derive from the raw bitstreams objects like bikes, pedestrians, and other vehicles.

A type of AI architecture called a recurrent neural network (RNN) determines whether a vehicle is double-parked, given all available sensory and map information (including parking availability, road type, and lane boundaries). Zucker and Ghose note that RNNs are unique in their ability to remember long-term dependencies, which effectively enable Cruise’s cars to accumulate decision-making confidence.

Sussing out a driving trajectory requires a generalizable policy, and Cruise’s is Model Predictive Control (MPC), a collection of algorithms that leans on a model of system behavior to figure out the best action at each step. The end result are driverless cars that can overtake double-parked cars in sunshine or in rain, while yielding to cyclists and oncoming traffic.

Cruise is considered a pack leader in a global market that’s anticipated to hit revenue of $ 173.15 billion by 2023. Although it hasn’t yet launched a driverless taxi service (unlike competitors Waymo and Yandex) or sold cars to customers, it’s driven more miles than most — around 450,000 in California last year, according to a report it filed with the state’s Department of Motor Vehicles — and it’s attracted about $ 1.15 billion (bringing its total raised to $ 7.25 billion) at a $ 19 billion valuation from T. Rowe Price Associates, General Motors, SoftBank Vision Fund, Honda, and other investors.

For all of its successes so far, though, Cruise has had its fair share of setbacks.

It backtracked on plans to test a fleet of cars in a five-mile square section in Manhattan, and despite public assurances that its commercial driverless taxi service remains on track, it’s declined to provide timelines or launch sites. In more disappointing news, Cruise drove less than 450,000 collective miles all of last year in California, falling far short of its projected one million miles a month. For the sake of comparison, Alphabet’s Waymo, which was founded about four years before Cruise, has logged more than 10 million autonomous miles to date.

But the company isn’t letting that deter it from its driverless ambitions. Cruise is currently testing third-generation vehicles in Scottsdale, Arizona and the metropolitan Detroit area, with the bulk of deployment concentrated in San Francisco. And Cruise earlier this year announced a partnership with DoorDash to pilot food and grocery delivery in San Francisco this year for select customers, shortly after revealing a fourth-generation car featuring automatic doors, rear seat airbags, and other redundant systems that lacks a steering wheel.

Let’s block ads! (Why?)

Big Data – VentureBeat

Zoox CEO explains what makes the company’s self-driving cars better: four-wheel steering, big batteries, AI

June 7, 2019 Big Data

Aicha Evans, CEO of Foster City, California-based Zoox, spoke during a keynote about the challenges facing self-driving cars and the promise they might hold if those challenges are overcome. And for the first time, she detailed a few of the hardware and software stacks underlying the company’s custom-designed vehicle platform — stacks which in part convinced investors to pledge more than $ 800 million in capital toward Zoox’s research and development.

Evans believes that three core competencies are required to build the driverless car of the future: artificial intelligence, fully autonomous driving, and a battery-powered electric frame designed for AI. “The new era of mobility requires a radically different approach, not an incremental approach — one that was optimized for human drivers,” said Evans.

She didn’t reveal Zoox’s production vehicle design, but reiterated that it uses a combination of RGB cameras and lidar (sensors that measure the distance to target objects by illuminating them with laser light and measuring the reflected pulses). Each vision sensor can see 270 degrees, and if one fails, the car retains a 360-degree view of the environment.

It isn’t your average self-driving electric, zero-emissions driverless car. Zoox’s model has four-wheel steering, which Evans claims allows it to follow trajectories with “much higher” accuracy than cars with two-wheel steering. It’s also got a dual power train and dual batteries, and the capacity of both batteries together is substantially larger than that of any single car battery today, Evans claims.

The idea is to reduce congestion through fleet management, and to minimize trips back to base stations for charging overnight. Zoox’s shuttle-like car — which is fully driverless — is designed to operate in a shared fleet in order to maximize efficiency and cut down on ride trip times.

It’s not unlike the self-driving ride-sharing network Tesla CEO Elon Musk described earlier this year during Tesla’s inaugural Autonomy Day.

“[When] you’re not using it, someone else is. This is a much better use of resources,” said Evans. “We believe this technology can solve the challenges facing cities around the world. With it, we can imagine a world where you can choose to live without owning or operating a car.”

Like other self-driving vehicles, Zoox’s use machine learning algorithms to contend with fraught environments they’ve never seen before, like a construction zone. They take in visual data and chart new courses around obstructions and obstacles, all without the need for human intervention.

Zoox is testing the bulk of its vehicles in San Francisco (and a few in Las Vegas), much like Cruise, its GM-owned rival. That’s a conscious choice: As Evans pointed out, San Francisco’s roads pose a challenge even for human drivers. “Our goal is to be safer than humans in developing this,” she said. “The multitude of challenges teach our way our basically how to be better and safer.”

Perhaps unsurprisingly, Zoox is using data collected from the real world to inform simulations that continuously improve its systems’ performance. Its cars drive by city blocks to create topologies, and its engineers use these topologies to create three-dimensional representations that AI agents traverse through endlessly and self-improve. .

Ultimately, when Zoox’s fleet deploys commercially, Evans believes it’ll save riders valuable time — and perhaps more importantly, give them control over their time. She pointed out that an estimated 400 billion annual hours are spent driving cars, and that drivers in San Francisco alone devote a collective 400,000 hours every day to commuting.

“In a more comfortable setting, we will be more productive, connect with friends enjoy music, and yes, some of us may work,” said Evans.

That’s not the only paradigm Zoox’s self-driving cars could change, Evans asserts. They might reduce the need for parking spaces and structures, minimize city congestion (a third of which is caused by drivers searching for parking), and reduce roughly a fourth of air pollution. On that last point, in fact, Evans said that entirely electric self-driving ride-sharing fleets could lead to a reduction in CO2 metric tons 85,000 metric tons of CO2.

They could reduce accidents, moreover. About 94% of car crashes are caused by human error. In 2016, the top three causes of traffic fatalities were distracted driving, drunk driving, and speeding, and the National Safety Council pegs Americans’ odds of dying in a car crash at one in 114.

“We believe in design from the ground up for autonomy, as opposed to [retrofitted] cars designed for human drivers is the right strategy to enable this new era,” said Evans. “But we think it’s a worthy one.”

Let’s block ads! (Why?)

Big Data – VentureBeat

Wind-tunnels and Aerodynamics: How Mercedes-AMG Petronas Motorsport Designs Some of the Fastest F1 Cars

May 13, 2019 TIBCO Spotfire

2018 Azerbaijan Grand Prix, Saturday - Steve Etherington — Großer Preis von Aserbaidschan 2018, Samstag – Steve Etherington

Mercedes-AMG Petronas Motorsport’s leadership comes from every aspect of the program delivering at peak performance. And one of the crucial areas that need to be at peak performance is the car and engine design. As a five-time consecutive FIA™ Formula One™ World Champions, the team thrives on a data-driven culture. Over the last several years, Mercedes-AMG Petronas Motorsport has leveraged data analytics and insights to gain a competitive advantage over its competition.

To make their cars even faster, Mercedes-AMG Petronas Motorsport looks at the car’s aerodynamics using a cutting-edge wind tunnel in conjunction with dynamometric (otherwise known as “dyno”) testing. The wind tunnel is described by the Mercedes-AMG Petronas Motorsport team as “a large hairdryer”. A scale model of an F1 car is fixed inside and the wind is blown over it at various speeds (limited to 180 kilometers per hour) to gauge the impact of the design elements.

The dyno is essentially a test bench that replicates and examines all of the car’s moving parts and exposes them to environmental conditions that could impact the drivers’ experience on the race track. The goal is to make sure all of the parts of the car are reliable and optimized for race day culminating in a fast and reliable car. Data collected about the performance of these elements are taken to the dyno group, race engineering teams, and design office. These groups collaborate to create the optimal aerodynamic package.

Making sense of the large amounts of data from the wind tunnel

Because all of the aerodynamic testing components produce large amounts of data, the team needs to find clarity in the clutter, finding the pieces of data that impact the team the most. The team uses TIBCO® Spotfire and TIBCO® Streaming to analyze the data collected during the testing of the car’s design components.

TIBCO analytics is a critical component to Mercedes-AMG Petronas Motorsport’s performance on the track. From improved aerodynamics allowing for more efficient use of time in the wind tunnel, to visual real-time analysis of car and driver execution during testing, to creating more reliable components that decrease the likelihood of part failures resulting in early swap penalties or a “did-not-finish” (DNF) designation, the team has found optimal ways to leverage data to gain an edge on the competition.

In addition, new for 2019, FIA imposed a fresh set of aerodynamic testing restrictions limit the amount of testing time in the wind tunnel. This means that the team has to use each test run wisely, using the data collected to its advantage to make the most of the time in the wind tunnel.

Translating aerodynamics testing to use in other industries

The method Mercedes-AMG Petronas Motorsport uses to test aerodynamic components can be translated to businesses in other industries: applying speed of learning, quickly analyzing data, and deriving insights to make more informed decisions. Like in dyno testing, businesses can take a closer look at their operational efficiency to identify areas for significant improvements.

With the world creating more and more data, companies need to implement analytics tools to make sense of large data sets. Leaders, in no matter what industry, can use data in a strategic way. By treating data as an asset, companies become more data-driven, innovating and disrupting in their own industries.

To learn more about how Mercedes-AMG Petronas Motorsport uses TIBCO Spotfire to make sense of their aerodynamic data for optimal car design, read the full case study.

Let’s block ads! (Why?)

The TIBCO Blog

CarGurus: 32% of consumers are ‘excited’ about self-driving cars

April 28, 2019 Big Data

According to marketing firm ABI, as many as 8 million driverless cars will be added to the road in 2025. Meanwhile, Research and Markets is predicting that in the U.S. alone, there will be some 20 million autonomous cars in operation by 2030.

But how do consumers feel about them? According to a survey of 1,146 automobile owners published by online car marketplace CarGurus, they’re both more enthusiastic and less worried about driverless cars than a year ago, and close to a third think they’ll own a self-driving car within the next decade.

“Consumer sentiment around self-driving cars is changing fast, with enthusiasm rapidly replacing skepticism,” said CarGurus’ director of customer insights Madison Gross. “These benchmarked results demonstrate that today’s consumers are becoming more comfortable with the idea of either owning an autonomous vehicle, or having them on the road, and it will be fascinating to continue to monitor this perception shift.”

Specifically, 32% of respondents (up from 21% in 2018) say they’re “excited” about the development of self-driving cars while 37% say they’re “concerned” (down from 37%). Among those who expressed misgivings, 87% said they wouldn’t be comfortable relying on self-driving cars for safety and cited liability, expense, and technological immaturity as their other top fears.

It’s a somewhat surprising turn of events. Last fall, a study published by PSB Research and commissioned by Intel found that nearly half (43%) of consumers don’t feel safe around autonomous cars, and in three separate studies this past summer — by the Brookings Institution, think tank HNTB, and the Advocates for Highway and Auto Safety (AHAS) — a majority of people weren’t convinced of driverless cars’ safety. More than 60% said they were “not inclined” to ride in self-driving cars, and almost 70% expressed concerns about sharing the road with them.

Just over 27% of survey-takers told CarGurus that they’re looking forward to piloting a self-driving car (compared with 26% who aren’t), while the majority are still worried both about being a passenger in a driverless car (49% compared with 30%) and sharing the roads (52% compared with 18%) with them.

High-profile accidents haven’t helped instill much confidence. In March of last year, Uber suspended testing of its autonomous Volvo XC90 fleet after one of its cars struck and killed a pedestrian in Tempe, Arizona. Separately, Tesla’s Autopilot was found to have been engaged in the moments leading up to a fatal Model X collision this past spring — the second fatality involving Autopilot since a crash in May 2016.

As for which companies people trust the most to produce a self-driving car, Tesla led the list despite its Autopilot gaffes, followed by Toyota and Alphabet’s Waymo. Close to 29% said they have faith in Tesla, up from 25% in 2018, while 9% trust Toyota; 7% trust Waymo; 5% trust Ford and Honda; 4% trust GM and Apple; 3% trust Volvo and Daimler; and 2% trust BMW. About 17% of consumers don’t trust any company, down from 27% last year.

Interestingly, Waymo is the only firm on the list that’s launched a commercial driverless car service (Waymo One). Tesla recently announced that next year, it’ll leverage a fraction of the Tesla cars on the road to launch its own fully autonomous taxi service, though it remains to be seen whether that will come to fruition. GM’s driverless vehicle division, Cruise, has repeatedly promised to launch a commercial service this year that would feature upwards of 2,600 driverless cars; Daimler and Bosch say they’ll pilot “highly” autonomous vehicles in San Francisco as part of an on-demand ride-hailing service; and Ford says it intends to launch a fleet of “thousands” of self-driving cars in 2021.

Waymo is also leagues ahead of the competition where total autonomous miles driven are concerned, with 10 million as of October. Cruise — reportedly a close second, although it doesn’t publicly disclose total mileage — racked up only about 500,000 miles in the state of California last year, where it conducts the bulk of its testing.

The CarGurus survey also asked respondents about whether they’d consider taking a ride in self-driving cars provided by ride-hailing apps, like Uber and Lyft. Close to 35% of people who currently use such services said they’d likely take a driverless ride in the future, with Uber edging out Lyft with respect to trust (22% compared with 13%).

Perhaps Uber’s redoubled safety initiatives have convinced a formerly skeptical public. Following the 2018 crash, Uber spent months testing its self-driving technology on a closed track and completed a lengthy internal review. In a blog post published in June, the company detailed newly implemented safeguards, such as a training program focused on safe manual driving and monitoring systems that alert remote monitors if drivers take their eyes off the road. It now mandates that teams of two employees — called “mission specialists” — drive its cars, switching off every two hours.

Uber’s self-driving car division is also well-capitalized, which helps. Earlier this month, Toyota, Denso, and SoftBank invested $ 1 billion in Advanced Technologies Group, Uber’s autonomous technologies group, valuing it at $ 7.25 billion.

Let’s block ads! (Why?)

Big Data – VentureBeat

Ford to deploy up to 100 autonomous cars by the end of 2019, expand testing to third city

April 27, 2019 Big Data

Ford intends to deploy upwards of 100 driverless vehicles by the end of 2019 and begin testing in a new city as it ramps up development of its autonomous technologies, CEO Jim Hackett told investors during the company’s Q1 2019 earnings call on Thursday.

Hackett said that Ford is focusing on “more complex” environments with seasonal weather changes and “intense” metro challenges, rather than suburban areas where the roads don’t change that often, or freeways. “We laughed that if autonomy was only destined for the L.A. freeways, you don’t have to deal with dogs and baseballs running across them, and no need to recognize that,” said Hackett. He added, “We’ve opted into some really difficult settings to prove this capability.”

Earlier this month, Hackett — who formerly led Ford’s autonomous vehicle division — admitted during a speech at the Detroit Economic Club that the carmaker had been overly ambitious in its plans to rapidly scale its self-driving efforts. He reiterated that Ford is on track to launch a driverless car fleet in 2021 but said that it’ll likely be geofenced and that the applications “will be narrow” because the problem of self-driving “is still too complex.”

Ford has a close relationship with Pittsburgh-based driverless technologies startup Argo AI, which it pledged to invest $ 1 million in over the next five years. Argo is currently testing autonomous cars in Miami and Detroit, with plans to expand tests to Washington, D.C. in the coming months, and it has obtained a permit to drive its self-driving cars on California roads.

Argo’s system — which comprises sensors like cameras, radar, and lidar, in addition to software and a bespoke compute platform — is one of its two core projects; the other is creating and maintaining high-definition maps of the roads on which its cars will drive.

In November, Ford unveiled a collaboration with Postmates to deliver food, personal care items, and other everyday goods from Walmart stores in Miami-Dade County. This came months after the company launched a delivery program — albeit a non-autonomous one — with Postmates in Miami and Miami Beach, Florida. The latter pilot lets customers order goods from local businesses. Orders are delivered via Ford Transit Connect cars sporting multiple storage lockers, speakers, and touchscreens.

As part of a $ 900 million investment in its Michigan manufacturing footprint announced two years ago, the automaker said in March that it would build a new factory dedicated to the production of autonomous vehicles. In July, Ford revealed that it would create a separate $ 4 billion Detroit-based unit to house the research, engineering, systems integration, business strategy, and development operations for its self-driving vehicle fleet. Separately, it has entered “exploratory talks” with Volkswagen to co-develop autonomous and electric cars.

Let’s block ads! (Why?)

Big Data – VentureBeat

AI Weekly: How self-driving cars could reduce emissions, eliminate parking spots, and add $1.3 trillion to the U.S. economy

April 26, 2019 Big Data

During Tesla’s inaugural Autonomy Day on Monday, CEO Elon Musk spoke about an idea first articulated in a document published three years ago — an autonomous taxi service built on the back of Tesla’s growing Model 3 and Model S network. It would be powered by the company’s Autopilot software and would allow any owner to add their car if they wish to earn revenue while they weren’t using the vehicle (Tesla would take a 25% to 30% cut), or remove them at will. Rides would cost an estimated $ 0.18 cents a mile, compared to the $ 2 to $ 3 charged by traditional ride-sharing platforms, and on the vehicle owner side of the equation, gross profit would hover around $ 0.65 per mile for a total of $ 30,000 per car per year on average.

It’s an attractive business model, and Musk isn’t the first to propose something like it. Jaime Moreno — CEO of Mormedi, a strategic design consultancy that works in the transport sector — predicts that 80% of car sales will be to fleet management companies rather than consumers in the next decade. And market research firm ReThinkX expects that by 2030 consumer demand for new vehicles will decline by 70%, accelerated by a projected uptick in the availability of on-demand autonomous rides.

Skeptical? Consider this: Cars, which cost an average of $ 35,000 apiece in the U.S., are used only 4% of the time, according to the Department of Transportation’s Bureau of Transportation Statistics. Beyond the base price tag, there’s insurance, taxes, fuel, and maintenance to cover, not to mention interest paid on car loans. Cars require parking spaces. They sit idly on congested freeways. Moreover, they’re outsized contributors of carbon emissions; a typical passenger car emits about 4.6 metric tons of carbon dioxide per year.

Worst of all, they’re demonstrably dangerous. About 94% of car crashes are caused by human error. In 2016, the top three causes of traffic fatalities were distracted driving, drunk driving, and speeding, and the National Safety Council pegs Americans’ odds of dying in a car crash at one in 114.

But what about the car’s cultural and economic significance, you might argue? The U.S. is the birthplace of the Model T, after all, and in 2017 employment in Detroit — a city that has long been synonymous with America’s car industry — grew by 31%. (Ford says it assembles about 80% of the vehicles it sells in the U.S. at stateside factories.)

And to be fair, depending on the circumstances, owning a car might make more fiscal sense than opting for a ride-sharing alternative. According to Ride or Drive, a calculator that compares the cost of car ownership to that of ride-sharing and other modalities, a $ 25,000 car that gets 25 miles to the gallon purchased on a 60-month loan (at a 4.35% interest rate) would cost $ 180,890 over 10 years versus $ 245,640 in ride-sharing fees, assuming at least four trips per day of 15 minutes in length (or about 12,000 miles per year).

Try explaining that to millennials. Only about half obtain a driver’s licenses by the age of 18, a trend that’s more or less inversely proportional to the uptick in public transit usage and the migration to dense urban areas. The American Public Transportation Association reported that public bus and train use in the United States rose to 10.7 billion trips — the highest number in 57 years — in 2013. And from 2010 to 2017, cities across the U.S. (including Odessa, Texas; Boise City, Idaho; and Charleston, South Carolina) grew by 15%.

A recent study by the International Transportation Forum in Portugal offers a glimpse into the impending shift. It showed that with a combination of driverless car fleets and high-capacity rail, residents’ transportation needs in cities the size of Lisbon can be delivered with 35% of the vehicles currently on the road during peak hours, and that over 24 hours 10% would be sufficient to meet the city’s needs. It also posited that shared self-driving taxi services would yield other benefits, like the elimination of on-street parking.

That’s not to suggest that driverless cars are a silver bullet to the globe’s transportation challenges. For one thing, serious concerns about their safety linger. In a pair of surveys published by the American Automobile Association last January and Gallup this past May, 63% of people reported feeling afraid to ride in a fully self-driving vehicle and more than half said they’d never choose to ride in one. And key questions about consumer privacy protections remain unanswered.

But perhaps the benefits outweigh the rewards. Morgan Stanley certainly thinks so — it’s predicting that the widespread adoption of autonomous vehicles would contribute $ 1.3 trillion to the U.S. economy through cost savings from reduced fuel consumption, fewer accidents, and productivity gains.

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to bookmark our AI Channel and subscribe to the AI Weekly newsletter.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: cars

AI Weekly: Autonomous cars need better safety metrics to move the industry forward

Baidu secures licenses to test self-driving cars in Beijing

Ongoing partnerships

Apollo Go and Apollo 5.0

MIT CSAIL teaches autonomous cars to navigate occluded intersections safely

Tier IV raises over $100 million to develop open source software for driverless cars

Here’s how Cruise’s autonomous cars navigate double-parked cars

Zoox CEO explains what makes the company’s self-driving cars better: four-wheel steering, big batteries, AI

Wind-tunnels and Aerodynamics: How Mercedes-AMG Petronas Motorsport Designs Some of the Fastest F1 Cars

CarGurus: 32% of consumers are ‘excited’ about self-driving cars

Ford to deploy up to 100 autonomous cars by the end of 2019, expand testing to third city

AI Weekly: How self-driving cars could reduce emissions, eliminate parking spots, and add $1.3 trillion to the U.S. economy

Recent Posts

Categories

Archives