Semantic understanding of multi-vehicle interaction patterns at intersections play a pivotal
role in proper decision-making of autonomous vehicles. This paper presents a flexible …

Understanding driver interaction behavioral semantics has potential benefits to autonomous
car's decision-making design. This article presents a framework of analyzing various …

Interpretation of common-yet-challenging inter-action scenarios can benefit well-founded
decisions for autonomous vehicles. Previous research achieved this using their prior …

Understanding how drivers behave at stop-controlled intersection is of critical importance for
the control and management of an urban traffic system. It is also a critical element of …

Robust unsupervised learning of temporal dynamic interactions is an important problem in
robotic learning in general and automated unsupervised learning in particular. Temporal …

Generating multi-vehicle interaction scenarios can benefit motion planning and decision
making of autonomous vehicles when on-road data is insufficient. This paper presents an …

Safely passing through unsignalized intersections (USI) in urban area is challenging for
autonomous vehicles due to high uncertainties of surrounding engaged human-driven …

In this paper we propose an unsupervised learning framework to predict risky driving at
intersections in a connected vehicle environment. The proposed framework uses time series …

Classification and analysis of driving behaviors offer in-depth knowledge to make an
efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving …

The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving
Systems (ADS) is a major challenge for the automotive industry. Scenario based testing is a …