Analysis of AODV, DSDR, LAR, DSR and ZRP using OSM, SUMO, and NS2
An Intelligent Traffic Light Control system using Reinforcement Learning. Compares Deep Q-Network (DQN) and Tabular Q-Learning to optimize traffic flow in SUMO simulator.
Repository to compare the quality of data generated from CARLA and SUMO simulators against real data from the UAH-DRIVESET-v1
Evaluating quality of data augmentation using CARLA and SUMO driving simulators for classifying normal and aggressive behaviors of UAH-driveset.
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