Simulating transformation
Pieter Fourie’s “laboratory” is located in the western part of Singapore. He works in a sunlit office on the sixth floor of the CREATE Tower, a building encased in vertical foliage at the National University of Singapore (NUS). Here, he conducts research into the cities of the future on behalf of ETH Zurich’s Future Cities Laboratory. Fourie heads up the Engaging Mobility project, which brought together government authorities and universities at a preliminary workshop in July 2017. The goal was to define the basic conditions required to implement city-wide, on-demand mobility using autonomous cars and buses.
The researchers used the results of the workshop to formulate key research questions such as: What do we do with the current supply of parking spaces if the majority of vehicles are constantly on the road? Do we need to redefine the layout of our roads? And what effect will auto-
mated, electrified transport have on existing public transport, energy requirements and safety?
Fourie explores these and similar issues using the MATSim simulation platform developed by a group led by Professor Kay Axhausen at ETH Zurich’s Institute for Transport Planning and Systems. MATSim is agent-based, which means the simulation is driven by the behaviour of individual agents rather than by overarching rules. “On the basis of Singapore’s most recent demographics, we are modelling a synthetic population that is as close as possible to the real one,” Fourie says.
Within this population, each individual agent exhibits a certain mobility behaviour and has a specific destination based on real-life traffic data. Fourie is now at the stage of tinkering with the underlying conditions, including the number of vehicles employed, their size, the maximum permissible waiting times for passengers, the availability of parking spaces and a variety of different traffic flows. He then lets the synthetic population loose on the simulation for 24 hours. The system automatically evaluates how efficiently the individual agents were able to reach their destinations in each scenario.
Right now, Fourie’s team is programming these kinds of simulations for the waterfront area of Tanjong Pagar, a district of some 2 square kilometres in the western part of Singapore. This site is currently being converted from a container terminal into a residential and commercial area. Fourie has already simulated more than 200,000 trips involving 60,000 individual agents. This included calculating how big the fleet of autonomous vehicles would need to be and how many kilometres the vehicles would have to cover to achieve an equivalent level of service in three different street typologies.
The researchers also simulated four different parking strategies for a fleet of 4-, 10- and 20-seater vehicles. Preliminary results suggest that the transport system is at its most efficient if the shared vehicles are allowed to park in the street when they stop receiving requests for pick-ups. That holds true even if it means temporarily reducing the roadway capacity by one lane. The researchers’ findings also suggest that having fewer, but correspondingly larger, pick-up and drop-off stations has a favourable impact on traffic flow by reducing the detours cars have to take to collect passengers. The stations also need to be big enough to accommodate different vehicle sizes. Fourie is hoping to have these kinds of simulations up and running for the entire island as early as next year.
Decision-making dilemmas
Despite these rapid developments in Singapore and the fledgling services coming online in Las Vegas, Emilio Frazzoli still sees plenty of challenges ahead, especially when it comes to dealing with chaotic environments. “We still don’t know exactly how autonomous vehicles should behave in traffic,” he says, explaining that this involves dozens of decision-making dilemmas that are an integral part of everyday traffic situations. For example, should a self-driving car cross a double line in order to avoid a potential collision? And what if an innocent road user is injured as a result of a manoeuvre designed to save a culpable driver from a fatal crash? These are the kinds of decisions that have to be defined when programming control algorithms. One key focus of Frazzoli’s current research is therefore the “rulebooks” that should be used to prioritise these various decision-
making criteria in control algorithms. At the top of the hierarchy are rules designed to ensure road users’ safety. At the bottom are rules designed to enhance passenger comfort.
In a recent article, Frazzoli and his team estimated that it would take 200 rules in 12 hierarchy groups to prepare vehicles for every possible scenario, including low-priority rules such as not frightening animals on the edge of the road. Frazzoli feels the time has come for a broader public debate on autonomous driving: “The coding of safety and liability rules is not something we should simply leave in the hands of engineers working for private companies”. Ultimately, he argues, it is in everyone’s interest to incorporate our new, virtual drivers into urban traffic as smoothly as possible – much like we do with new human drivers, but with the greater levels of safety, predictability and efficiency that autonomous vehicles offer.