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Sanjay Gangal
Sanjay Gangal
Sanjay Gangal is the President of IBSystems, the parent company of AECCafe.com, MCADCafe, EDACafe.Com, GISCafe.Com, and ShareCG.Com.

Hexagon Navigating the Road from Assisted to Autonomous Vehicles

 
July 24th, 2019 by Sanjay Gangal

The effort to develop and implement truly autonomous vehicles (AVs) is one whose magnitude (at least from a technology standpoint) hasn’t been matched since the “Space Race” in the 1960s in terms of resources necessary, economic impact and societal implications.

The journey to autonomous vehicles has been undertaken by virtually all traditional vehicle manufacturers, but you may be surprised to learn about a company that has entered the race – Hexagon – a diverse company known primarily for metrology (precise measurement) and production automation, among many other things in widely ranging industries.

Hexagon’s involvement with autonomous vehicles was recently showcased at its annual conference, HxGN Live 2019. During and after the event Hexagon’s Positioning Intelligence (PI) division provided material from presentations and demonstrations that illustrated its progress and future direction of autonomous vehicles with its digital solutions that create Autonomous Connected Ecosystems (ACE). The company stressed, however, that while progress has definitely been made, there is still a long way to go before vehicles can be truly autonomous because of safety and reliability issues.

The Race Is On

The race is on to develop autonomous vehicles (AV) and advanced driver assistance systems (ADAS). Major vehicle OEMs and Tier-1 suppliers, as well as independent newcomers, are accelerating AV and ADAS development efforts to get ahead in this race.

The National Highway Traffic Safety Administration (NHTSA), estimates that 94% of serious car crashes are due to human error. Knowing this statistic, if we remove humans from the driving task, will we save that many people? The answer to that question is one that Hexagon PI is attempting to address and hopes to definitively answer, knowing that autonomous vehicles will fail in different ways than humans. However, many things that are considered “no-brainers” for humans can be a challenge for autonomous vehicle systems.

Luca Castignani, Autonomous Driving Strategist, MSC Software (a Hexagon company), posed the question; is it really safety that is driving investments in AVs? He contends that it is not. Rather, he thinks that we are excited about AVs is motivated more to get more free time by riding, not driving, but of course we expect them to be safe. He thinks that Hexagon is in a unique position for helping develop AVs because it can combine simulation, reality, measurement, validation and assessment for creating innovative and safe vehicles.

Artificial Intelligence (AI) is a vital technology and solves problems for AVs, but it also introduces new ones that can challenge humans because autonomous driving technology is drastically different from traditional automotive technology. Also, it is safety critical, so any errors introduced during autonomous operation could quickly lead to fatal consequences.

Ideally, companies need to road test autonomous vehicles for billions of miles to meet safety standards, but that would require many years of development time. AV and ADAS development can only be achieved with the speed, economy and precision of computer simulation. Simulation is centrally important in developing autonomous vehicles.

Using simulation, engineers can rapidly test thousands of driving scenarios. They can also optimize the performance of sensors and algorithms, accelerating time to market.

Levels of Automation

According to the Society of Automotive Engineers (SAE), fully autonomous cars and trucks that drive us instead of us driving them will eventually become a reality. These self-driving vehicles ultimately will integrate onto U.S. roadways by progressing through six levels of driver assistance technology advancements in the coming years, and includes everything from no automation (where a fully engaged driver is required at all times), to full autonomy (where an automated vehicle operates independently, without a human driver).

SAE's Levels of Automation

Below are SAE’s levels of vehicle automation and who does what, when:

Level 0 – The human driver does all the driving.

Level 1 – An advanced driver assistance system (ADAS) on the vehicle can sometimes assist the human driver with either steering or braking/accelerating, but not both simultaneously.

Level 2 – An advanced driver assistance system (ADAS) on the vehicle can itself actually control both steering and braking/accelerating simultaneously under some circumstances. The human driver must continue to pay full attention (“monitor the driving environment”) at all times and perform the rest of the driving task.

Level 3 – An Automated Driving System (ADS) on the vehicle can itself perform all aspects of the driving task under some circumstances. In those circumstances, the human driver must be ready to take back control at any time when the ADS requests the human driver to do so. In all other circumstances, the human driver performs the driving task.

Level 4 – An Automated Driving System (ADS) on the vehicle can itself perform all driving tasks and monitor the driving environment – essentially, do all the driving – in certain circumstances.  The human need not pay attention in those circumstances.

Level 5 – An Automated Driving System (ADS) on the vehicle can do all the driving in all circumstances. The human occupants are just passengers and need never be involved in driving.

Industry pundits say that at this time, Level 3 is approximately where we’re at on a good day under good conditions. Today, no one will predict exactly when Levels 4 and 5 will be achieved because of the many levels of complexities involved (although 2025 is touted by some major car manufacturers). This point was driven home in a recent story in the Wall Street Journalwith the headline: “Driverless Cars Are 90% Here. Another 90% Is Left to Go.” The crux of the article is that if building an AV was just about putting the parts together, we’d be living in the future already. Point well taken and testament to how difficult it is to develop a fully functional AV that works in virtually all possible environments and circumstances.

Hexagon PI's Take on Levels of Automation

Collaboration Is Key to Success

Hexagon’s Positioning Intelligence division is tasked with creating end-to-end positioning solutions for positioning for land, sea, and air systems. Especially prominent is its advancement of Autonomous X (cars, UAVs, industrial and agricultural vehicles, trains, and other vessels). The division includes the brands NovAteland Veripos.

Last year Hexagon PI acquired AutonomouStuff, one of the world’s leading suppliers of integrated autonomous vehicle solutions. AutonomouStuff being part of Hexagon PI has boosted collaboration between the organizations to provide solutions for autonomous vehicle development.

“Combined with Hexagon PI’s leadership in high accuracy, functionally-safe and high-integrity positioning technology, the addition of AutonomouStuff and their offerings is helping our customers to accelerate the development of more comprehensive Autonomous X solutions,” said Michael Ritter, President and CEO of Hexagon PI. “Our expanded capabilities will allow Hexagon PI to meet the industry’s ever growing demand for more robust autonomy solutions.”

Prior to the acquisition, Hexagon PI had been an important technology provider to AutonomouStuff for several years, and the two organizations worked closely together to serve common customers and collaborate. As the division grows, AutonomouStuff will continue to function as an independent brand within Hexagon PI.

Hexagon PI and AutonomouStuff Collaborative Autonomous Vehicle Demonstrator Project

“The acquisition of AutonomouStuff accelerates Hexagon’s ability to move our customers beyond the data impasse of IoT,” said Ola Rollén, Hexagon President and CEO. “We’re particularly interested in technologies that are the most disruptive – those capable of leveraging the vast potential of data being generated by connected things, integrating AI, edge-cloud computing orchestration, mobility, and data visualization into autonomous connected ecosystems.”

“When combined with our positioning intelligence, mapping and sensing technology leadership, this acquisition creates a nexus of domain expertise that will lead the autonomous mobility industry for years to come.”

Pieces and Parts Make It Work

It should come as no surprise that autonomous vehicles rely heavily on integrated hardware and software components that must work continuously and seamlessly.

As an example, a Synchronous Positioning, Attitude and Navigation (SPAN) technology (from Hexagon company, NovAtel) couples Global Navigation Satellite System (GNSS) dual-frequency receivers with an Inertial Measurement Units (INUs) to provide continuously available 3D position, velocity and attitude (pitch, roll and heading). All of this combined data provides safe and reliable navigation in challenging environments such as under overpasses and in dense urban areas that can adversely affect GNSS reception. SPAN technology helps in urban environments by providing reliable position information even when satellite reception and information is poor.  In a sense, the IMUs “feel” the motion of a vehicle to provide continuously available position, velocity and attitude, even over short periods of time when satellite signals are completely blocked (in tunnels, for example) or otherwise unavailable

Collectively, GNSS is used to describe all of the world's satellite constellations, operating or planned. It currently incorporates the American GPS, Russia's GLONASS, China's BeiDou and the European Economic Union's Galileo satellite systems. The ability to track all constellations is significant to our customers as the more constellations tracked, the better the reliability and availability of the positioning and navigation solutions, especially in partially obstructed environments.

There is now universal agreement that dual-frequency GNSS delivers better positioning and navigation data in urban and other challenging environments. This is because single GNSS signals require line of sight contact between satellites and receivers, and signals can be easily blocked by tall buildings. Compounding this problem is the fact that the signals that do manage to reach the floors of “urban canyons” are often reflected off buildings, resulting in multiple and confusing signals. This so-called multipath effect is a considerable source of position and navigation error in dense cities.

Hexagon PI’s autonomous vehicle system includes the following hardware and software components.

Hardware

  • GNSS Receiver Module provides precise dual-frequency positioning and is capable of tracking with the various GPS satellite constellations.
  • Ground Truth Kit ensures accurate autonomous vehicle position. Ground truth refers to information collected on location and allows image data to be related to real features and materials on the ground. The collection of ground truth data enables calibration of remote-sensing data, and aids in the interpretation and analysis of what is being sensed.
  • GNSS Antenna for positioning satellite reception and correction.

Software

  • Positioning Engine provides multi-constellation and multi-frequency GNSS positioning with precise Precision Point Positioning (PPP) algorithms.
  • Waypoint Post-Processing that maximizes accuracy by processing forward and backward in time, backsmoothing and combining the results.
  • Interference Toolkit (ITK) monitors, filters, quantifies, and removes unintentional interference sources that can impact GNSS receiver performance, as well as eliminate interference sources from vehicle sensors.

Down on the Farm

Although automobiles get the lion’s share of publicity and notoriety for becoming autonomous, several other types of vehicles are also part of the mix, including agricultural equipment in what has been coined as a “Farm to Tablet” movement that is transforming precision farming.

Similar to a factory of manufacturing facility, today’s farmers are focused on total cost of operations. Farmers want to understand in much greater detail where efficiencies can be improved with so-called digital farming techniques. This is where intelligent connected autonomous machines come in, as they can supply data about resource (seed and fertilizer) application and machine performance in what could be called a farm ERP system. Efforts like autonomous agricultural equipment lets farmers better manage food production from farm-to-tablet more efficiently by running core business processes along the entire agri-business value chain.

Still Under Construction: The Road to the Future

Although we are at the dawn of mass driverless mobility, autonomous driving and vehicles are developing rapidly. More and more semi-autonomous systems are already in service and there is no doubt that we will see fully autonomous vehicles on our highways sooner rather than later. The impetus behind autonomous vehicles is largely economic, but other good reasons for transportation autonomy also include the opportunity for greatly improved safety for human passengers.

It’s pretty obvious that Hexagon PI thinks that a fully autonomous future is well within its capabilities, is betting big on it coming together, and realizes that the next few years will continue to set the foundation for it. There are still and will continue to be a lot of questions – technical, ethical, and others – but Hexagon PI is confident that it can answer these questions as it charges ahead to a safe and reliable future for autonomous vehicles.

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