PTV VISSIM, Python & Deep Reinforcement Learning (DQN) Developer for Autonomous Intersection Control

Project Title PTV VISSIM, Python & Deep Reinforcement Learning (DQN) Developer for Autonomous Intersection Control Brief Project Goal I am seeking an experienced PTV VISSIM, Python, and Deep Reinforcement Learning (DQN) developer to complete the remaining technical development and evaluation of an existing research project for autonomous vehicle intersection control. The project aims to develop and evaluate a collision-free, signal-free intersection control system (IC-PLT) for autonomous vehicles using PTV VISSIM 2025, Python, EDM/COM, and DQN-based reinforcement learning. A substantial part of the project has already been developed, including the calibrated and validated VISSIM model, deterministic IC-PLT controller, fixed-data generation, simulation logging, and offline DQN framework. The selected developer will be required to review and complete the existing code, validate the deterministic controller, integrate the online DRL/DQN layer, train and validate the model, run the required simulation scenarios, and perform the final comparative performance evaluation between the calibrated signalized model, deterministic IC-PLT, and DRL-enhanced IC-PLT. Experience with PTV VISSIM, Python, COM/EDM, PyTorch/DQN, traffic simulation, and preferably autonomous vehicles or Intelligent Transportation Systems (ITS) is highly preferred. Existing code, VISSIM model, methodology, roadmap, and detailed project presentation will be provided to shortlisted candidates.