E2SUMO

E2SUMO is a term encountered in studies of intelligent transportation systems that describes frameworks or pipelines that couple end-to-end learning approaches with the SUMO (Simulation of Urban MObility) traffic simulation platform. The exact usage of the acronym is not standardized; different projects may use E2SUMO to denote end-to-end learning integrated with SUMO for training, evaluating, or deploying autonomous driving, traffic control, or routing policies within a realistic urban environment.

Typically, E2SUMO implementations use SUMO together with the TraCI (Traffic Control Interface) protocol to exchange state information (vehicle trajectories, speeds, signals) and control commands (vehicle acceleration, routing decisions, traffic signal timings) between a simulation and an external learning model. The common architecture comprises a SUMO instance running a defined network and demand, a machine learning component (such as a neural network or reinforcement learning agent), and a bridge or middleware to translate data between the two.

Applications include training autonomous driving policies in simulated cities, optimizing traffic signals, and evaluating end-to-end routing or control strategies under varied traffic conditions. Benefits of the E2SUMO pattern include accelerated experimentation, reproducibility, and the ability to study perception-action loops in a controlled setting.

Challenges include the sim-to-real gap when transferring to real vehicles, synchronization and latency issues, and the need for robust domain randomization. See also SUMO, TraCI, end-to-end learning, reinforcement learning, traffic simulation.