34 citations · 78 across the 12 of their papers we have counts for
13 papers
Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning
Saeed Rahmani, Gözde Körpe, Zhenlin +4
Automated driving at unsignalized intersections is challenging due to complex multi-vehicle interactions and the need to balance safety and efficiency. Model Predictive Control (MP…
Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments
Marc Heim, Francisco Suarez-Ruiz, Ishraq Bhuiyan +2
Human-level autonomous driving is an ever-elusive goal, with planning and decision making -- the cognitive functions that determine driving behavior -- posing the greatest challeng…
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
Max Lodel, Bruno Brito, Álvaro Serra-Gómez +3
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current met…
Improving Pedestrian Prediction Models with Self-Supervised Continual Learning
Luzia Knoedler, Chadi Salmi, Hai Zhu +2
Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This pap…
Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals
Jitske de Vries, Elia Trevisan, Jules van der Toorn +3
In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations…
Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells
Hai Zhu, Bruno Brito, Javier Alonso-Mora
In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertai…
Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios
Bruno Brito, Achin Agarwal, Javier Alonso-Mora
Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have…
Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments
O. de Groot, B. Brito, L. Ferranti +2
We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the m…
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians
Bruno Brito, Michael Everett, Jonathan P. How +1
Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the envir…
Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments
Hai Zhu, Francisco Martinez Claramunt, Bruno Brito +1
This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each ro…
Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians
Bruno Brito, Hai Zhu, Wei Pan +1
Prediction of human motions is key for safe navigation of autonomous robots among humans. In cluttered environments, several motion hypotheses may exist for a pedestrian, due to it…
Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments
Bruno Brito, Boaz Floor, Laura Ferranti +1
This paper presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimiza…
With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
Álvaro Serra-Gómez, Bruno Brito, Hai Zhu +2
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coo…