2016, Enhanced Traffic Intelligence
What determines and signifies typical traffic dynamics? Modeling an interpretation of that in a microscopic environment requires imagination and creativity. Daniel Carballal and Nicolo Campolongo has spent the summer of 2016 working on new features and improving existing ones to mimic real world traffic behavior in the Smart Mobility Lab demonstration environment.
In early 2016 the Smart Mobility Lab restructured its code base and started using the Robot Operating System to enable multiple parallel processes at run-time. ROS processes, so called nodes, can publish and subscribe to information to and from each other. The subsequent adaptation of the SML source code has enabled beneficial modularity, that has the potential to make the lab source code work like a jig-saw puzzle, where pieces can be added and subtracted individually.
Daniel Carballal's and Nicolo Campolongo's respective projects revolving around increased traffic intelligence in the lab has lead to many new features and fine tuning of old ones. Cars now slow down in street corners, overtake other cars, stop for traffic lights and pedestrians on crosswalks, communicate with each other to improve their respective perception. Heavy and light vehicles behave differently when they start and stop and turn around corners based on a bicycle control model. Radars and traffic flow are visualised.
This project is part of the summer 2016 SML projects that together forms a demonstration showcasing how driverless and smart vehicles can improve general traffic dynamics by making smarter and more informed decisions than human drivers.