Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning

Automated driving systems have become a potential approach to mitigating collisions, emissions, and human errors in mixed-traffic environments. This study proposes the use of deep reinforcement learning method verify effects comprehensive automated vehicle movements at non-signalized intersection according training policy measures effectiveness. integrates multilayer perceptron partially observable Markov decision process algorithms generate proper decision-making algorithm for vehicles. also evaluates efficiency proximal optimization hyperparameters performance process. Firstly, we set initial parameters create simulation scenarios. Secondly, SUMO simulator executes exports observations. Thirdly, Flow tool transfers these observations into states agents. Next, trains input data updates policies actions. Finally, this checks termination iteration These proposed experiments not only increase speeds vehicles but decrease emissions higher market penetration rate lower traffic volume. We demonstrate that fully autonomous condition increased average speed 1.49 times compared entirely human-driven experiment.