Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …

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 …

Development and safety validation of highly automated vehicles increasingly relies on data
and data-driven methods. In processing sensor datasets for environment perception, it is …

Analyzing and predicting the traffic scene around the ego vehicle has been one of the key
challenges in autonomous driving. Datasets including the trajectories of all road users …

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 …

The development of safety-oriented research and applications requires fine-grain vehicle
trajectories that not only have high accuracy, but also capture substantial safety-critical …

Scenario-based testing for the safety validation of highly automated vehicles is a promising
approach that is being examined in research and industry. This approach heavily relies on …

Real-world datasets facilitate the development of autonomous vehicles, especially when
they are accessible, diverse, and provide a measure of accuracy. While existing datasets …

The safety of road construction is one of the most important concerns of construction
managers for the following reasons: long-span construction operation, no fixed monitoring …

This paper presents an automated traffic safety diagnostics solution named “Automated
Roadway Conflict Identification System”(ARCIS) that uses deep learning techniques to …