Interaction is fundamental in autonomous driving (AD). Despite more than a decade of
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …

Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …

Simulation with realistic, interactive agents represents a key task for autonomous vehicle
software development. In this work, we introduce the Waymo Open Sim Agents Challenge …

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …

Recent advances in high-fidelity simulators [,,] have enabled closed-loop training of
autonomous driving agents, potentially solving the distribution shift in training vs deployment …

Abstract We introduce\textit {Nocturne}, a new 2D driving simulator for investigating multi-
agent coordination under partial observability. The focus of Nocturne is to enable research …

Autonomous vehicles need to be designed to abide by the same rules that humans follow.
This is challenging, because traffic rules are fuzzy and not well defined, making them …

Many scenarios in mobility and traffic involve multiple different agents that need to cooperate
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can
be used for validation and verification of self-driving system performance without relying on …

Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …