simulation and characterization of traffic on drive me route around gothenburg using sumo

As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. In this work, the CRI was first used to assess the risk during the merging manoeuvre. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. This code ignores that the connections or edges are maybe not usable by the vehicle type you use but I hope you get the idea.In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. Route.append(random.choice(route.getToNode().getOutgoing())) In Python something like the following would do: import random, sumolib Alternatively you could write a simple script yourself which parses the network and the connections and tosses a coin at each junction where to drive next. If you really want random routes you can try to give intermediate points (option -i) to randomTrips.py but it will still give shortest paths between those. So if you have a trip file (maybe from randomTrips.py) just call duaIterate.py -n net.xml -t trips.xml The usual way of preventing this is to use dynamic assignment, that is using SUMO's duaIterate.py script to calculate route distributions based on the travel time in a previous iteration of the simulation.

Simulation and characterization of traffic on drive me route around gothenburg using sumo