In cooperative, connected, and automated mobility (CCAM), the more automated vehicles
can perceive, model, and analyze the surrounding environment, the more they become …

Autonomous driving has been among the most popular and challenging topics in the past
few years. Among all modules in autonomous driving, High-definition (HD) map has drawn …

In this paper, an automated driving system (ADS) data acquisition and analytics platform for
vehicle trajectory extraction, reconstruction, and evaluation based on connected automated …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and
many researchers and developers in the field cannot afford a real car and the corresponding …

Behavior-related research areas such as motion prediction/planning, representation/
imitation learning, behavior modeling/generation, and algorithm testing, require support from …

Automated vehicles rely heavily on data-driven methods, especially for complex urban
environments. Large datasets of real world measurement data in the form of road user …

The development and validation of automated vehicles involves a large number of
challenges to be overcome. Due to the high complexity, many classic approaches quickly …

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative
perception, planning, and action operations. One of the foundational layers of such stacks is …

Provably safe motion planning for automated road vehicles must ensure that planned
motions do not result in a collision with other traffic participants. This is a major challenge in …