Ford Motor Company and the Massachusetts Institute of Technology are collaborating on a new research project that measures how pedestrians move in urban areas to improve certain public transportation services, such as ride-hailing and point-to-point shuttles services.
The project will introduce a fleet of on-demand electric vehicle shuttles that operate on both city roads and campus walkways on the university’s Cambridge, Massachusetts, campus. The vehicles use LiDAR sensors and cameras to measure pedestrian flow, which ultimately helps predict demand for the shuttles. This, in turn, helps researchers and drivers route shuttles toward areas with the highest demand to better accommodate riders.
“The onboard sensors and cameras gather pedestrian data to estimate the flow of foot traffic,” said Ken Washington, vice president of Research and Advanced Engineering at Ford. “This helps us develop efficient algorithms that bring together relevant data. It improves mobility-on-demand services, and aids ongoing pedestrian detection and mapping efforts for autonomous vehicle research.”
Using a high-tech lab
The MIT research is being conducted by the Aeronautics and Astronautics Department’s Aerospace Controls Lab. ACL researches topics related to autonomous systems and control design for aircraft, spacecraft, and ground vehicles. Theoretical and experimental research is pursued in such areas as estimation and navigation, planning and learning under uncertainty, and vehicle autonomy.
“Through the mobility-on-demand system being developed for MIT’s campus, ACL can investigate new planning and prediction algorithms in a complex, but controlled, environment, while simultaneously providing a testbed framework for researchers and a service to the MIT community,” said ACL director Professor Jonathan How.
Hailing a ride
Ford and MIT researchers plan to introduce the service to a group of students and faculty beginning in September. This group will use a mobile application to hail one of three electric urban vehicles to their location and request to be dropped off at another destination on campus.
The electric vehicles are small enough to be able to navigate the campus’s sidewalks, while still leaving plenty of room for traditional pedestrian traffic. Each is outfitted with weatherproof enclosures that shield out inclement weather – a feature particularly useful for New England’s punishing winters.
After requesting the shuttles via a smartphone app, MIT students and faculty won’t be waiting long for their ride to arrive.
During the past five months, Ford and MIT have used LiDAR sensors and cameras mounted to the vehicles to document pedestrian flow between different points on campus. LiDAR is the most efficient way to detect and localise objects from the environment surrounding the shuttles. The technology is much more accurate than GPS, emitting short pulses of laser light to precisely pinpoint the vehicles’ location on a map and detect the movement of nearby pedestrians and objects.
Using this data, researchers study the overall pattern of how pedestrian traffic moves across campus, which helps the researchers anticipate where the most demand for the shuttles will be at any given moment. This allows the shuttles to be carefully pre-positioned and routed to serve the MIT population as efficiently as possible.
Researchers also take into account other factors that affect pedestrian movement on MIT’s campus, such as varying weather conditions, class schedules, and the dynamic habits of students and professors across different semesters.
Applying learnings to mobility services and beyond
This collaboration further enhances Ford’s Dynamic Shuttle project, which provides point-to-point shuttle rides to employees requesting rides using a mobile application on its Dearborn, Michigan, campus. The collaboration advances the ride-hailing concept to new heights by examining the movement of pedestrians to predict demand and reduce wait times for shuttles.
What’s more, the algorithms and methods learned when navigating densely crowded pedestrian areas using LiDAR will also strengthen Ford’s autonomous and driver assist technologies as the company continues develop autonomous vehicles.
The project is one of more than 30 mobility solutions university research projects between Ford and universities in the U.S., Germany and China aimed at helping the company and academic world better understand how to improve mobility for millions of people globally.
University research partnerships are an important part of Ford’s broader effort to change the way the world moves. Ford Smart Mobility is the company’s plan to be a leader in connectivity, mobility, autonomous vehicles, the customer experience, and data and analytics.
Meet Aston Martin F1’s incredible moving data centre
The Aston Martin Red Bull Racing team faces a great deal more IT challenges than your average enterprise as not many IT teams have to rebuild their data center 21 times each year and get it running it up in a matter of hours. Not many data centers are packed up and transported around the world by air and sea along with 45 tonnes of equipment. Not many IT technicians also have to perform a dual role as pit stop mechanic.
The trackside garage at an F1 race is a tight working environment and a team of only two IT technicians face pressure from both the factory and trackside staff to get the trackside IT up and running very fast. Yet, despite all these pressures, Aston Martin Red Bull Racing do not have a cloud-led strategy. Instead they have chosen to keep all IT in house.
The reason for this is performance. F1 is arguably the ultimate performance sport. A walk round the team’s factory in Milton Keynes, England, makes it abundantly clear that the whole organization is hell bent on maximizing performance. 700 staff at the factory are all essentially dedicated to the creation of just two cars. The level of detail that is demanded in reaching peak performance is truly mind blowing. For example, one machine with a robotic arm that checks the dimensions of the components built at the factory is able to measure accuracy to a scale 10 times thinner than a human hair.
This quest for maximum performance, however, is hampered at every turn by the stringent rules from the F1 governing body – the FIA. Teams face restrictions on testing and technology usage in order to prevent the sport becoming an arms race. So, for example, pre-season track testing is limited to only 8 days. Furthermore, wind tunnel testing is only allowed with 60% scale models and wind tunnel-usage is balanced with the use of Computational Fluid Dynamics (CFD) software, essentially a virtual wind tunnel. Teams that overuse one, lose time with the other.
In order to maximize performance within uniquely difficult logistical and regulatory conditions, the Aston Martin Red Bull Racing team has had to deploy a very powerful and agile IT estate.
According to Neil Bailey, Head of IT Infrastructure, Enterprise Architecture and Innovation, their legacy trackside infrastructure was “creaking”. Before choosing hyperconverged infrastructure, their “traditional IT had reached its limits”, says Bailey. “When things reach their limits they break, just like a car,” adds Bailey.
The team’s biggest emphasis for switching to HPE’s hyperconverged infrastructure, SimpliVity, was performance. Now, with “the extra performance of SimpliVity, it means it doesn’t get to its limits,” says Bailey. HPE SimpliVity has helped reduce space, has optimized processing power and brought more agility.
One of the first and most important use cases they switched to hyperconverged infrastructure was post-processing trackside data. During a race weekend each car is typically fitted with over 100 sensors providing key data on things like tyre temperature and downforce multiple times per second. Processing this data and acting on the insights is key to driving performance improvements. With their legacy infrastructure, Bailey says they were “losing valuable track time during free practice waiting for data processing to take place.” Since switching to HPE SimpliVity, data processing has dropped from being more than 15 minutes to less than 5 minutes. Overall, the team has seen a 79% performance boost compared to the legacy architecture. This has allowed for real time race strategy analysis and has improved race strategy decision making.
Data insights helps the team stay one step ahead, as race strategy decisions are data driven. For example, real time tyre temperature data helps the team judge tyre wear and make pit stop decisions. Real time access to tyre data helped the team to victory at the 2018 Chinese Grand Prix as the Aston Martin Red Bull cars pitted ahead of the rest of the field and Daniel Ricciardo swept to a memorable victory.
Hyperconverged infrastructure is also well suited to the “hostile” trackside environment, according to Bailey. With hyperconverged infrastructure, only two racks are needed at each race of which SimpliVity only takes up about 20% of the space, thus freeing up key space in very restricted trackside garages. Furthermore, with the team limited to 60 staff at each race, only two of Bailey’s team can travel. The reduction in equipment and closer integration of HPE SimpliVity means engineers can get the trackside data center up and running quickly and allow trackside staff to start work as soon as they arrive.
Since seeing the notable performance gains from using hyperconverged infrastructure for trackside data processing, the team has also transitioned some of the factory’s IT estate over to HPE SimpliVity. This includes: Aerodynamic metrics, ERP system, SQL server, exchange server and the team’s software house, the Team Foundation Server.
As well as seeing huge performance benefits, HPE SimpliVity has significantly impacted the work patterns of Bailey’s team of just ten. According to Bailey, the biggest operational win from hyperconverged infrastructure is “freeing up engineers’ time from focusing on ‘business as usual’ to innovation.” Traditional IT took up too much of the engineers’ time monitoring systems and just keeping things running. Now with HPE SimpliVity, Bailey’s team can “give the business more and quicker” and “be more creative with how they use technology.”
Hyperconverged infrastructure has given Aston Martin Red Bull Racing the speed, scalability and agility they require without any need to turn to the cloud. It allows them to deliver more and more resources to trackside staff in an increasingly responsive manner. However, even with all these performance gains, Aston Martin Red Bull Racing has been able to reduce IT costs. So, the users are happy, the finance director is happy and the IT team are happy because their jobs are easier. Hyperconvergence is clearly the right choice for the unique challenges of Formula 1 racing.
Body-tracking tech moves to assembly line
Technology typically used by the world’s top sport stars to raise their game, or ensure their signature skills are accurately replicated in leading video games, is now being used on an auto assembly line.
Employees at Ford’s Valencia Engine Assembly Plant, in Spain, are using a special suit equipped with advanced body tracking technology. The pilot system, created by Ford and the Instituto Biomecánica de Valencia, has involved 70 employees in 21 work areas.
Player motion technology usually records how athletes sprint or turn, enabling sport coaches or game developers to unlock the potential of sport stars in the real world or on screen. Ford is using it to design less physically stressful workstations for enhanced manufacturing quality.
“It’s been proven on the sports field that with motion tracking technology, tiny adjustments to the way you move can have a huge benefit,” said Javier Gisbert, production area manager, Ford Valencia Engine Assembly Plant. “For our employees, changes made to work areas using similar technology can ultimately ensure that, even on a long day, they are able to work comfortably.”
Engineers took inspiration from a suit they saw at a trade fair that demonstrated how robots could replicate human movement and then applied it to their workplace, where production of the new Ford Transit Connect and 2.0-litre EcoBoost Duratec engines began this month.
The skin-tight suit consists of 15 tiny movement tracking light sensors connected to a wireless detection unit. The system tracks how the person moves at work, highlighting head, neck, shoulder and limb movements. Movement is recorded by four specialised motion-tracking cameras – similar to those usually paired with computer game consoles – placed near the worker and captured as a 3D skeletal character animation of the user.
Specially trained ergonomists then use the data to help employees align their posture correctly. Measurements captured by the system, such as an employee’s height or arm length, are used to design workstations, so they better fit employees.