Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

Autonomous vehicles must often contend with conflicting planning requirements, eg, safety
and comfort could be at odds with each other if avoiding a collision calls for slamming the …

Predicting the future behavior of agents is a fundamental task in autonomous vehicle
domains. Accurate prediction relies on comprehending the surrounding map, which …

Route planning for navigation under partial observability plays a crucial role in modern
robotics and autonomous driving. Existing route planning approaches can be categorized …

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A
major challenge is to efficiently learn a representation that approximates the true joint …

Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …

Generating diverse and comprehensive interacting agents to evaluate the decision-making
modules is essential for the safe and robust planning of autonomous vehicles (AV). Due to …

Multi-robot cooperative navigation is an important task, which has been widely studied in
many fields like logistics, transportation, and disaster rescue. However, most of the existing …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

In this article, we present an efficient planning system for automated vehicles in highly
interactive environments (EPSILON). EPSILON is an efficient interaction-aware planning …