Traffic flow harmonization in expressway merging
Steering a vehicle is a task increasingly challenging the driver in terms of mental resources. Reasons for this include the increasing volume of road traffic and a rising quantity of road signs, traffic lights, and other distractions at the roadside (such as billboards), to name a few. The application of Advanced Driver Assistance Systems, in particular if taking advantage of Ambient Intelligence (AmI) technology, can help to increase the perceptivity of a driver, leading as a direct consequence to more relaxed mental stress of the same. One situation where we see potential in the application of such a system are merging areas on the expressway where two or more varying traffic streams converge into a single one. In order to reduce cognitive liabilities (in this work expressed as panic or anger), drivers are exposed to while merging, we have developed two behavioral rules. The first (increased range of perception) enables drivers to change early upstream into a spare lane, allowing the merging traffic to join into mainline traffic at reduced conflicts, the second (inter-car distance management in the broader area of merging) provide drivers with recommendations of when and how to change lanes at the best. From a technical point of view, the VibraSeat a in-house developed car seat with integrated tactile actuators, is used for delivering information about perception range and inter-car distances to the driver in a way that does not stress his/her mental capabilities. To figure out possible improvements in its application in real traffic and at a meaningful scale, cellular automatonbased simulation of a specific section of Madrid expressway M30 was performed. Results from the data-driven simulation experiments on the true to scale model indicate that AmI technology has the potential to increase road throughput or average driving speed and furthermore to decrease the panic of drivers while merging into an upper (the main) lane.
expressway merging vibraseat data-driven simulation ami technology driver-vehicle interaction traffic flow optimization cognitive workload drivers mental capabilities