Traffic Agent

Traffic agents, simulated representations of vehicles and pedestrians, are crucial for developing and validating autonomous driving systems and optimizing traffic flow. Current research focuses on creating more realistic and reactive agents using techniques like reinforcement learning, generative models (e.g., conditional VAEs, transformers), and graph neural networks to capture complex interactions between agents and their environment. These advancements aim to improve the accuracy and generalizability of traffic simulations, ultimately leading to safer and more efficient transportation systems. The development of generalized models capable of handling diverse scenarios and heterogeneous agents is a key area of ongoing investigation.