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

Autonomous vehicles need to face human-driving vehicles with their uncertain intentions in
dynamic urban environment. Thus it leads to a challenging decision-making problem. In this …

We consider the problem of designing an algorithm to allow a car to autonomously merge on
to a highway from an on-ramp. Two broad classes of techniques have been proposed to …

Short-term future of automated driving can be imagined as a hybrid scenario in which both
automated and human-driven vehicles co-exist in the same environment. In order to address …

Accurate prediction of whether pedestrians will cross the street is prevalently recognized as
an indispensable function of autonomous driving systems, especially in urban environments …

Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …

Car-following is the most general behavior in highway driving. It is crucial to recognize the
cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this …

The push towards sustainable transportation emphasizes vehicular energy efficiency in
mixed traffic scenarios. A research hotspot is the cooperative control of connected and …

Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …

We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …