October 21, 2021
Digital twin technology will be key to planning the future of efficient, safe mobility. And it starts with having precise, detailed data. Researchers in Aachen, Germany are developing digital twins with ITS America member Ouster digital lidar in order to run V2X simulations with autonomous vehicles.
During NASA’s Apollo 13 mission in the 1970s, three onboard astronauts suddenly heard a loud explosion that shook their voyaging spacecraft and severely damaged their vehicle. Meanwhile, the spacecraft continued to evolve due to the harsh, extreme conditions of space. All the failure scenarios and simulations the team had prepared for using the original digital model did not prepare them for this.
200,000 miles away, NASA’s mission controllers had to quickly adapt the digital model in order to run new simulations and bring the crew safely home. To match the real-time state of the spacecraft, the team used a continuous live stream of data and a combination of mathematical and physical models. With this “digital twin”, they ran simulations and tested new strategies with the onboard crew. Fuel was limited, oxygen was limited, time was limited – but the “digital twin” allowed NASA to quickly learn, adapt, and safely bring the three astronauts back home.
This concept of “digital twins” has revolutionized other industries as well, including manufacturing and engineering. Access to real-time representations allows for continuous innovation, quantifiable decision making, and cost savings of up to billions per year.
In this post, we’ll cover the potential smart cities applications of digital twin technology and discuss how the City of Aachen is building digital twins with Ouster digital lidar as it prepares for V2X deployments.
Digital twin vs. 3D model
A digital twin is not just a precise, high-fidelity 3D model, though a 3D model is certainly an important component of a digital twin.
What differentiates a digital twin from a model is the bidirectional flow of data between the physical and the digital world. This continuous synchronization of data enables digital twin models to be continuously updated based on real-time conditions, making it an effective tool to run virtual simulations and scenario planning.
As defined in this research study “Digital Twin: Origin to Future”, a digital twin has a few common characteristics:
- Ultra high-fidelity model of the physical object, often pulling from multiple data sources. The higher level of the detail and precision, the more it can be used as an effective and reliable tool. For example, smart cities may integrate infrastructure, vehicle movement, pollution, and demographics data.
- Dynamic, bi-directional mapping between the physical and digital world. A digital twin is essentially a living model in 3D and must account for both real-time and historical data.
- Self-evolving and self-adapting. The real world doesn’t stop evolving, and neither does a digital twin. A digital twin must self-optimize with the help of new sources of data collected in the physical world.
Today, “digital twin” technology is an increasingly important ingredient of smart cities. The digitization and bi-directional mapping of the real and the digital worlds are key to planning the future of efficient and safe mobility. And it starts with having precise, detailed data.
Digital twin technology is a key foundation for smart cities
Cities around the world have started to explore digital twins to accelerate applications, including:
- Urban planning. Running “what if” scenarios on digital twins enables city planners to evaluate the impact of proposed infrastructure developments and policy changes, aiding in the decision making process. For example, researchers could simulate how the construction of new bike lanes could impact bicyclist safety, traffic flow, curb parking, and even air pollution. Using a digital twin, planning can be done more effectively to optimize for safety, accessibility, cost, or other factors.
- Traffic management, including the integration of connected and autonomous vehicles. Similar to urban planning, digital twins can be used to run traffic simulations aimed at improving safety, traffic efficiency, and congestion. By combining digital twin models with real-time data, traffic operators can predict, optimize, and control traffic measures. And with the introduction of connected and autonomous vehicles, researchers can run simulations to understand how these would impact traffic and road users and test V2X functionalities.
- Infrastructure monitoring of assets such as roads, bridges, and other structures. Infrastructure data on structures’ wear and tear can be analyzed in tandem with real-time usage data. Algorithms can then analyse infrastructure’s real-time condition and alert engineers of potential problems and predict how infrastructure would withstand different weather conditions, traffic, vibrations, and other events. Such proactive maintenance measures could preserve infrastructure by decades and save billions of dollars. In one report, the estimated cost of deferred maintenance of U.S. infrastructure is over $1 trillion.
- Others including natural disaster monitoring, smart grid monitoring, and more
Want to learn more? Read the full article at the Ouster website.
Samantha Chan is Product Marketing Manager at Ouster.
