101 citations · 146 across the 8 of their papers we have counts for
9 papers
Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps
Rabbia Asghar, Lukas Rummelhard, Wenqian Liu +2
Accurate prediction of driving scene is a challenging task due to uncertainty in sensor data, the complex behaviors of agents, and the possibility of multiple feasible futures. Exi…
Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps
Rabbia Asghar, Wenqian Liu, Lukas Rummelhard +2
Accurate prediction of driving scenes is essential for road safety and autonomous driving. Occupancy Grid Maps (OGMs) are commonly employed for scene prediction due to their struct…
A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges
Phani Teja Singamaneni, Pilar Bachiller-Burgos, Luis J. Manso +4
Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation ski…
Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps
Rabbia Asghar, Manuel Diaz-Zapata, Lukas Rummelhard +2
Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In th…
Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks
Rabbia Asghar, Lukas Rummelhard, Anne Spalanzani +1
Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions…
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
Niranjan Deshpande, Dominique Vaufreydaz, Anne Spalanzani
Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem. This work formulates navigation in urban…
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
Niranjan Deshpande, Dominique Vaufreydaz, Anne Spalanzani
Decision making for autonomous driving in urban environments is challenging due to the complexity of the road structure and the uncertainty in the behavior of diverse road users. T…
Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data
Pavan Vasishta, Dominique Vaufreydaz, Anne Spalanzani
Autonomous Vehicles navigating in urban areas have a need to understand and predict future pedestrian behavior for safer navigation. This high level of situational awareness requir…
Personal space of autonomous car's passengers sitting in the driver's seat
Eleonore Ferrier-Barbut, Dominique Vaufreydaz, Jean-Alix David +2
This article deals with the specific context of an autonomous car navigating in an urban center within a shared space between pedestrians and cars. The driver delegates the control…