Learning Interaction-Aware Guidance for Trajectory Optimization in Dense Traffic Scenarios

Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model)...

Safe Navigation in Dense Traffic Scenarios using Reinforcement Learning as Global Guidance for a Model Predictive Controller

The successful integration of autonomous vehicles (AVs) in human environments is highly dependent on their ability to navigate safely and timely through dense traffic conditions. Such conditions involve a diverse range of human behaviors, ranging from cooperative (willing to yield) to non-cooperative human drivers (unwilling to yield) that need...