no code implementations • 8 Aug 2023 • Rabbia Asghar, Manuel Diaz-Zapata, Lukas Rummelhard, Anne Spalanzani, Christian Laugier
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.
no code implementations • 11 Jan 2023 • Rabbia Asghar, Lukas Rummelhard, Anne Spalanzani, Christian Laugier
This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'.
no code implementations • 11 Oct 2021 • 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.
no code implementations • 26 Oct 2020 • Niranjan Deshpande, Dominique Vaufreydaz, Anne Spalanzani
In this work, a deep reinforcement learning based decision-making approach for high-level driving behavior is proposed for urban environments in the presence of pedestrians.
no code implementations • 17 Sep 2018 • 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.