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

Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple
surrounding vehicles. The correlations between road users vary over time, the degree of …

Predicting surrounding vehicle behaviors are critical to autonomous vehicles when
negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious …

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 …

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences
of its potential actions by anticipating the uncertain intentions of other traffic participants. This …

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 …

Autonomous vehicles use a variety of sensors and machine-learned models to predict the
behavior of surrounding road users. Most of the machine-learned models in the literature …

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

In complex lane change (LC) scenarios, semantic interpretation and safety analysis of
dynamic interaction pattern are necessary for autonomous vehicles to make appropriate …

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous
vehicles being released on the road in the near future. However, it can be assumed that this …