We present MBAPPE, a novel approach to motion planning for autonomous dr...
Predicting future motions of nearby agents is essential for an autonomou...
Vision research showed remarkable success in understanding our world,
pr...
While a lot of work has been done on developing trajectory prediction
me...
Pedestrian crossing prediction has been a topic of active research, resu...
During recent years, deep reinforcement learning (DRL) has made successf...
Deep reinforcement learning (DRL) has been demonstrated to be effective ...
During recent years, deep reinforcement learning (DRL) has made successf...
In this paper, we propose THOMAS, a joint multi-agent trajectory predict...
In this paper, we propose GOHOME, a method leveraging graph representati...
Understanding the behaviors and intentions of pedestrians is still one o...
In this paper, we propose HOME, a framework tackling the motion forecast...
Reinforcement Learning (RL) aims at learning an optimal behavior policy ...
Consistent and reproducible evaluation of Deep Reinforcement Learning (D...
Deep learning within the context of point clouds has gained much researc...
This paper explores the capability of deep neural networks to capture ke...
Convolutional neural networks are commonly used to control the steering ...
We present here a first prototype of a "Speed Limit Support" Advance Dri...
We present a new modular traffic signs recognition system, successfully
...
We present promising results for visual object categorization, obtained ...
This paper shows how to improve the real-time object detection in comple...
We present promising results for real-time vehicle visual detection, obt...