Interaction Aware

Interaction-aware systems aim to design autonomous agents, particularly autonomous vehicles, that can safely and efficiently navigate environments shared with other agents (humans, other vehicles). Current research focuses on developing models that predict the behavior of other agents, often using neural networks integrated with optimization techniques like Model Predictive Control or reinforcement learning, to generate interaction-aware trajectories. This research is crucial for improving the safety and efficiency of autonomous systems in real-world scenarios, impacting fields like autonomous driving, robotics, and human-computer interaction.