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 …

We consider the problem of interaction-aware motion planning for automated vehicles in
general traffic situations. We model the interaction between the controlled vehicle and …

We propose a Stochastic MPC (SMPC) approach for autonomous driving which incorporates
multi-modal, interaction-aware predictions of surrounding vehicles. For each mode, vehicle …

Autonomous driving has been gaining more and more attention in the last decades, thanks
to its positive social-economic impacts including the enhancement of traffic efficiency and …

The automation of road intersections has significant potential to improve traffic throughput
and efficiency. While the related control problem is usually addressed assuming fully …

We propose a Stochastic MPC (SMPC) formu-lation for autonomous driving at traffic
intersections which incorporates multi-modal predictions of surrounding vehicles given by …

Safe and reliable autonomy solutions are a critical component of next-generation intelligent
transportation systems. Autonomous vehicles in such systems must reason about complex …

This paper presents a real-time lane change control framework of autonomous driving in
dense Traffic, which exploits cooperative behaviors of other drivers. This paper focuses on …

Safe autonomous passing of intersections with mixed traffic, including human drivers and
autonomous vehicles, is challenging. We propose a tailored approach that provides …

We propose a Stochastic MPC (SMPC) formulation for path planning with autonomous
vehicles in scenarios involving multiple agents with multi-modal predictions. The multi …