Diverse Driving

Diverse driving research focuses on enabling autonomous vehicles (AVs) to safely and consistently navigate complex environments populated by drivers exhibiting varied behaviors. Current research employs machine learning techniques, including reinforcement learning and Monte Carlo Tree Search, to model and predict diverse driving styles, often incorporating constraint satisfaction and parallel optimization methods to ensure safety. This work is crucial for improving AV safety and reliability, ultimately leading to wider acceptance and deployment of autonomous driving technology.