Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Moutarde, Fabien"'
Autor:
de Moreau, Simon, Almehio, Yasser, Bursuc, Andrei, El-Idrissi, Hafid, Stanciulescu, Bogdan, Moutarde, Fabien
Nighttime camera-based depth estimation is a highly challenging task, especially for autonomous driving applications, where accurate depth perception is essential for ensuring safe navigation. We aim to improve the reliability of perception systems a
Externí odkaz:
http://arxiv.org/abs/2409.08031
Sound plays a major role in human perception. Along with vision, it provides essential information for understanding our surroundings. Despite advances in neural implicit representations, learning acoustics that align with visual scenes remains a cha
Externí odkaz:
http://arxiv.org/abs/2405.18213
Autor:
Chekroun, Raphael, Wang, Han, Lee, Jonathan, Toromanoff, Marin, Hornauer, Sascha, Moutarde, Fabien, Monache, Maria Laura Delle
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to mitigate the impact of data source delays. After the success of the Meg
Externí odkaz:
http://arxiv.org/abs/2402.05663
Autor:
Horváth, Dániel, Martín, Jesús Bujalance, Erdős, Ferenc Gábor, Istenes, Zoltán, Moutarde, Fabien
Even though reinforcement-learning-based algorithms achieved superhuman performance in many domains, the field of robotics poses significant challenges as the state and action spaces are continuous, and the reward function is predominantly sparse. Fu
Externí odkaz:
http://arxiv.org/abs/2312.09394
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent explainable exploration and optimization capabilities of the Monte-Carlo Se
Externí odkaz:
http://arxiv.org/abs/2309.08452
Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Scene Graph Neural Networks with projected vectorized representations for
Externí odkaz:
http://arxiv.org/abs/2305.08190
Publikováno v:
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Vision research showed remarkable success in understanding our world, propelled by datasets of images and videos. Sensor data from radar, LiDAR and cameras supports research in robotics and autonomous driving for at least a decade. However, while vis
Externí odkaz:
http://arxiv.org/abs/2303.07257
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods a
Externí odkaz:
http://arxiv.org/abs/2205.07310
Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions. While measuring the overall progress of those solutions over time tends to be more and more established due to the new publicly available
Externí odkaz:
http://arxiv.org/abs/2201.12626
In the search for more sample-efficient reinforcement-learning (RL) algorithms, a promising direction is to leverage as much external off-policy data as possible. For instance, expert demonstrations. In the past, multiple ideas have been proposed to
Externí odkaz:
http://arxiv.org/abs/2201.03834