Navigant Research Blog

Most Developed Automated Vehicle Tech Doesn’t Equate to a Best Solution Today

— June 21, 2018

An interesting Twitter thread sprung up recently discussing the merits of Tesla’s AutoPilot partially automated driving system relative to competitors. This came in the wake of a preliminary report from the National Transportation Safety Board that was examining a March 2018 crash that killed an Apple engineer in a Tesla Model X. One of the commenters raised the concept of which automated driving systems are most developed, citing how far back Tesla ranked in Navigant Research’s Automated Driving Leaderboard report.

How to Interpret Leaderboard Rankings

While it is true that Tesla had the lowest score in this year’s ranking, it’s important to understand both what is being ranked and how most developed may relate to best developed for automated driving.

This Navigant Research Leaderboard is intended to provide a snapshot of where analysts believe companies rank in their ability to successfully commercialize automated driving technology. To do that, the Leaderboard goes well beyond just the core automation technology, with individual subscores assigned for 10 different criteria including vision, go-to market strategies, partnerships, manufacturing capability, product quality, and financial strength. While Tesla has always ranked high on vision, it often lags in many other areas, leading to its low overall score.

Scoring for Vision

The vision score is based in part on where a company sees its business going in the coming decade as well as what it hopes to achieve. In Tesla’s case, its score was boosted by its belief that automated driving technology should be deployed as far and wide as possible as soon as possible.

That is a fantastic idea. In an ideal world, it would save many of the tens of thousands of lives lost annually in American traffic accidents and the more than one million lost globally. Unfortunately, we don’t live in an ideal world. Putting insufficiently developed technology in the hands of untrained consumers on public roads can be a recipe for disaster.

The Gap between Most and Best

AutoPilot may indeed be the most developed system on the market today in terms of the manufacturer’s willingness to stretch its capabilities and extract all it can from the available sensor suite. That does not necessarily make it the best developed system in terms of what should be in production based on safety requirements and consumers’ current abilities and understanding of the technology.

Humans Like Consistency

Early in my engineering career, working on anti-lock braking systems, I learned the value of ensuring that the technology performed with as much consistency and predictability as possible. For all our flaws, humans are remarkably adaptable if we understand the conditions. Most people tend to drive the same vehicle every day for years. Even if a vehicle has limitations or quirks, as long as they are consistent, drivers will adjust how they use the vehicle. If a vehicle has a longer stopping distance, drivers will brake sooner. If the steering response is slower, a larger angle will be used.

The problem comes when the system responds differently every time you use it, as many voice control systems have done. That’s when people stop using it or get caught unawares when they do. That likely contributed to Walter Huang ignoring warnings to hold the steering wheel in his Tesla. There are other systems on the market, that while not as capable as they could be in certain conditions, are indeed better developed for the real world.

Acknowledging Room for Improvement

As we move to higher levels of automated driving in the next several years, we need to encourage manufacturers to acknowledge what their products can’t do yet, while working to make available functions the best they can be.

 

Driverless Shuttles Look to Move EV Market

— June 5, 2018

While partially automated passenger vehicles are becoming notorious due to a recent string of accidents, slower driverless shuttles are starting to safely (so far) address the last-mile transportation conundrum. Fully automated vehicles are moving people for short distances in many cities globally, collecting data and learning lessons that could benefit the larger automated driving market.

EasyMile, a Toulouse, France-based company that specializes in automated vehicle technology, is one of numerous companies that has developed or is currently working on electric automated shuttles. EasyMile driverless shuttles are transporting passengers in cities across Europe and North America. The company has projects underway or in development in Norway; France; Gainesville, Florida; San Ramon, California; Arlington, Texas; and Denver, Colorado. In May, EasyMile received approval to operate its vehicles on public roads in mixed traffic in California. This is the company’s first such pilot project in the state, according to Lauren Isaac, director of Business Initiatives at EasyMile.

Campuses Are Ideal Test Beds

Isaac said campuses with private roads, such as business parks, hospitals, and universities, are ideal operating sites for these driverless shuttles because they don’t require navigating the ever-changing regulatory process needed for public roads. EasyMile’s EZ10 shuttles operate on fixed or on-demand routes and do not require human drivers. In some instances, operators are present to monitor the vehicles, talk to customers, and comply with local regulations—even though the vehicles don’t have a steering wheel or brakes.

The all-electric EV10 shuttles can be programmed to open the doors at every stop and complete the route. They can also be programed to only stop when passengers are detected or provide point-to-point delivery based on passenger requests via a mobile phone app. The vehicles’ average speed is around 15 mph; however, they can reach maximum speeds of 25 miles per hour depending on the application.

EasyMile’s US headquarters are within Panasonic’s Peña Station campus, located near the Denver International Airport. Isaac expects that the planned shuttle service there will be transporting people between nearby bus and train station stops sometime this summer. “EasyMile is excited to provide the first automated shuttle operation transporting people between a bus and rail station,” said Isaac.

Growing Crowd Pursuing This Niche

EasyMile is part of a growing crowd of companies pursuing the burgeoning automated shuttle niche. Ann Arbor, Michigan-based May Mobility operated a pilot with automated vehicles delivering Bedrock employees around a section of downtown Detroit in fall 2017 and plans additional tests in 2018. Apple, which has invested significantly in automated vehicle technology development, recently was rumored to be in a partnership with Volkswagen to convert shuttles to automated at the company’s headquarters. Luxury EV maker Fisker also has an automated electric shuttle in development known as the Orbit.

Shuttles Act as a Linchpin

A growing number of cities see emissions-free shuttles as a linchpin in improving urban air quality and reducing congestion while also expanding the use of public transit by filling the last-mile gaps and transit deserts. The cities of Houston, Texas, Peachtree Corners, Georgia, and Providence, Rhode Island have announced intentions to pilot automated shuttles. By focusing on low speed, fixed loop trials, automated shuttles are gaining valuable experience while avoiding being in the headlines for the wrong reasons.

 

Toronto Is Getting Its Master’s Degree in Cities

— May 29, 2018

Toronto is going back to school. In March, the University of Toronto (U of T) approved its newest discipline, the School of Cities—which is aimed at addressing complex urban challenges such as traffic congestion and affordable housing. The program is one of the first of its kind in the world, and it will serve as a hub for innovative interdisciplinary urban research, education, and engagement. More than 220 faculty members at U of T conduct urban-focused research, representing over 40 academic departments such as engineering, architecture, urban planning, and public health.

Headlined by speakers such as Dan Doctoroff, CEO of Sidewalk Labs (an Alphabet company), U of T’s School of Cities program hosted an inaugural session on May 15 called Toronto: Towards a Smart and Inclusive City-Region. The session brought together urban thought leaders, policymakers, planners, business leaders, and entrepreneurs to exchange ideas on ways to meet the challenges of city building while ensuring that smart cities are also inclusive cities.

Inclusivity a Major Focus

Several speakers at the session noted how strong city leadership and vision are crucial to ensuring that smart city development reflects the socioeconomic conditions of the city. Sidewalk Labs touted its deep commitment to inclusive cities, referencing its spinoff company Cityblock Health—which aims to help low income Americans access health services. The company also outlined how automated vehicles (AVs) tie into its broader vision for inclusivity. Sidewalk Labs is targeting a 40%-50% reduction in annual family income expenditure on vehicles by offering shared AV services—providing citizens with increased opportunity to access transportation. More public and open space would also be enabled through AVs, with a significant reduction of land needed for parking.

An Ambitious Project

Sidewalk Labs’ ambitious project in Toronto has brought considerable attention and excitement to the city, as well as skepticism. Citizen concerns around data, privacy, and business models have been well documented. Doctoroff shared more details on how data will likely be used, Sidewalk Labs’ support for open data, and its commitment to data security. Information was also provided on the company’s projected business model and areas where it expects to make a return on its investment:

  • Property: Real estate value in Quayside is expected to increase over time.
  • Technology: The technology it develops is expected to scale to the larger Toronto area and other cities in Canada and around the world.
  • Next-generation infrastructure: Sidewalk Labs will potentially arrange and manage next-generation infrastructure services.

A Welcome Development, but Some Concerns

U of T’s new School of Cities is a welcome development that helps fills a void in contemporary academia. Cutting-edge research and collaboration will be needed to help solve the world’s most complex urban issues. Look for more universities to follow suit with similar programs in the coming years.

Sidewalk Labs utilized U of T’s inaugural session to continue its concerted effort to address data and privacy concerns. The company recently released its Responsible Data Use Policy Framework, which builds on Canadian privacy laws and recent recommendations by national and provincial Canadian privacy regulators. To win the support of Torontonians, Sidewalk Labs will need to continue to demonstrate and more effectively communicate to citizens that data from the project will be anonymized, open, secure, and not used for advertising.

 

HD Maps Might Help Teslas Stop Running into Fire Trucks

— May 24, 2018

Recently, a Tesla in Utah ran into the back of a stationary fire truck at high speed. This is the second such incident this year and the National Transportation Safety Board is already investigating the earlier incident. Incidents involving Teslas get news coverage because of the strident safety claims made by Elon Musk for his company’s AutoPilot driver assist system, but such accidents can happen with many vehicle brands. Relying on a single sensor for active safety control is often inadequate, but high definition (HD) maps may actually turn out to be part of the solution.

Teslas, and many millions of other vehicles, are equipped with forward-looking radar sensors that are used for adaptive cruise control (ACC). The radar is used to detect a vehicle moving ahead while ACC is active and measures the gap to that vehicle. If the lead vehicle slows down, the ACC vehicle will automatically slow to maintain a safe gap.

Forward-Looking Sensors Not Seeing Everything

You might think that if ACC detects a stopped vehicle it would automatically slow to a stop, but as the two recent crashes indicate, this isn’t always true. When ACC is used at highway speed, the assumption is that the other vehicles on the road will also be moving. To prevent false positives that would cause the brakes to erroneously engage, these systems are designed to ignore static objects like road signs, light poles, etc.

When another static vehicle that was outside of the radar range comes within view of the sensor while moving at highway speeds (as both vehicles in these crashes were), it is not assumed to be a vehicle and thus it is ignored. Some vehicles also include a combination of automatic emergency braking and/or forward collision warning safety systems to prevent crashes, but these systems are not optimized for identifying stationary vehicles in the roadway when the vehicles are traveling at highway speeds. Refinements in the coordination between these systems will continue.

How Does Mapping Fit into This?

Today, increasingly detailed maps are being used not just for routing but also as inputs to hybrid propulsion systems and long-range sensors in partially automated vehicles from GM and Mercedes-Benz. In the coming years, HD maps with detailed locations of static objects will be used for precision localization. If a vehicle has HD maps with the locations of fixed roadside objects, it may be possible to fuse this with the real-time radar data to better understand which objects can safely be ignored. The addition of image data from the camera used for lane keeping assist and it should be possible to recognize legitimately stopped vehicles and respond accordingly.

Companies such as San Francisco startup Mapper and incumbent map providers like HERE and TomTom have begun building HD maps. Mapper has developed a low cost, multi-camera-based data collection system that can be installed in vehicles used for ride-hailing providers or in other fleets. By the end of 2018, up to 2 million vehicles from Volkswagen, BMW, and Nissan are expected to be on the road globally with Mobileye’s latest EyeQ4 image processor. These vehicles will also be collecting data that feeds into Mobileye’s Road Experience Management system and then into maps from providers including HERE.

The sooner we start augmenting existing driver assist systems with new data sources such as HD maps or fusion of other sensors in the vehicle, the sooner object classification should improve to help prevent more crashes. The Tesla crashes are getting the attention, but these are problems that afflict virtually every manufacturer and the technology needs to be improved in order to save more lives.

 

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