The inter-vehicle co-operation is meant not only among automated vehicles, but extends also to manually driven vehicles. Human drivers receive maneuvering instructions on their HMI; the ergonomy and non-distraction of this new user interface needs to be validated.

The AutoNet2030 researchers showed that it is possible for vehicles with or without drivers to operate in high-speed, multi-lane traffic autonomously under real-life conditions. This is a key step in the ongoing shift towards autonomous driving. EPFL’s contribution to this project came in the form of the cooperative maneuvering control algorithm.

One option for integrating automated vehicles with legacy vehicles is the use of automated vehicle platoons. For example, a manually-driven truck could lead a platoon of autonomous tractor trailers moving at a constant speed and at an equal distance from each other. This approach has been successfully tested over hundreds of kilometers. A problem with this approach is that this type of convoy behaves as a discrete block which, above a given number of vehicles, becomes increasingly difficult to manage.

Under the AutoNet2030 cooperative and distributed system, there is no leader; each connected vehicle communicates directly with other vehicles in the immediate vicinity. They then adjust their speed and position independently of each other. The convoy has no trouble driving on one or more lanes on a highway or reconfiguring when another vehicle joins the group.

Each vehicle also benefits from its neighbors’ “eyes”, effectively enjoying 360 degree perception. There is no upper limit to the size of the convoy in theory, since each member positions itself independently.

The convoys are managed using control software based on an algorithm developed by EPFL’s Distributed Intelligent Systems and Algorithms Laboratory (DISAL).

We have been working on this type of distributed control algorithm for around ten years. Simply put, the idea is to find a way for agents that are not particularly clever—robots or cars—to work together and achieve complex group behavior.

—Alcherio Martinoli, the head of DISAL

In mathematical terms, this means that the algorithm uses information that it receives from the agents’ sensors to guide the convoy’s movements in real time. The convoy automatically and constantly reorganizes when, for example, another vehicle joins or leaves it, it changes lanes, or it adapts to target speeds. The DISAL researchers began by managing robots on simulators before moving on to real miniature robots and then to cars on simulators. Finally, as part of the AutoNet2030 project, they managed to get to real vehicles on the road.

The final demonstration took place at the end of October 2016 in Sweden, on the AstaZero test track. Three vehicles were used: an automated truck and car and—a key aspect of the project—a networked though manually-driven car. The researchers equipped the non-automated car with GPS and laser sensors and a human-machine interface allowing the driver to follow instructions on joining the convoy.

It may not seem so impressive with only three cars, but for the first time we were able to validate what we had achieved in the simulation. And the number of vehicles in the convoy has no impact on the complexity of the control mechanism.

—Alcherio Matinoli

This is a proof of concept. We are hoping that, with a rise in demand, carmakers will come up with ever cheaper solutions for converting legacy vehicles, that they will coordinate their efforts with the community working on the Internet of things, and that we will be able to deploy and improve this multi-lane convoy system for heterogeneous vehicles.

—Guillaume Jornod, the EPFL scientist who ran the trials

In addition to EPFL, the AutoNet2030 project involved: Institute of Communications and Computer Systems (Greece, coordinator); Broadbit (Hungary); Baselabs (Germany); Centro Ricerche Fiat (Italy); ARMINES – Mines ParisTech – INRIA (France); Scania (Sweden); Hitachi Europe; and the Technische Universitaet Dresden (Germany). This project received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement Nº 610542.

Resources

• Marcus Obst; Ali Marjovi; Milos Vasic; Iñaki Navarro; Alcherio Martinoli; Angelos Amditis; Panagiotis Pantazopoulos; Ignacio Llatser; Xiangjun Qian, “Challenges for Automated Cooperative Driving – The AutoNet2030 Approach”, Automated Driving book chapter (2017, Pages 561-570), ISBN 978-3-319-31895-0 / doi: 10.1007/978-3-319-31895-0