Simulation is the key to scaling up validation and verification for robotic systems such as
autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a …

Predicting and planning interactive behaviors in complex traffic situations presents a
challenging task. Especially in scenarios involving multiple traffic participants that interact …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially
accelerate autonomous driving research, especially for perception tasks such as 3D …

Realistic traffic simulation is crucial for developing self-driving software in a safe and
scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to …

Human drivers have complex and individual behavior characteristics which describe how
they act in a specific situation. Accurate behavior models are essential for many applications …

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous
driving systems in simulation. This work introduces a data-driven method called TrafficGen …

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for
planners in a more scalable and safe way compared to real-world road testing. Existing …

Simulation has emerged as an indispensable tool for scaling and accelerating the
development of self-driving systems. A critical aspect of this is simulating realistic and …

Simulation is an integral part in the process of developing autonomous vehicles and
advantageous for training, validation, and verification of driving functions. Even though …