Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Nashashibi, Fawzi"'
Decision-making for automated driving remains a challenging task. For their integration into real platforms, these algorithms must guarantee passenger safety and comfort while ensuring interpretability and an appropriate computational time. To model
Externí odkaz:
http://arxiv.org/abs/2410.19510
Multiple approaches have already been proposed to mimic real driver behaviors in simulation. This article proposes a new one, based solely on the exploration of undisturbed observation of intersections. From them, the behavior profiles for each macro
Externí odkaz:
http://arxiv.org/abs/2409.15786
Publikováno v:
2024 IEEE Intelligent Vehicles Symposium (IV), ITS-IEEE, Jun 2024, Jeju, South Korea
The implementation of road user models that realistically reproduce a credible behavior in a multi-agentsimulation is still an open problem. A data-driven approach consists on to deduce behaviors that may exist in real situation to obtain different t
Externí odkaz:
http://arxiv.org/abs/2407.02863
Publikováno v:
ITSC, Sep 2023, Bilbao, Spain
Predicting the future trajectories of dynamic agents in complex environments is crucial for a variety of applications, including autonomous driving, robotics, and human-computer interaction. It is a challenging task as the behavior of the agent is un
Externí odkaz:
http://arxiv.org/abs/2312.06219
Autor:
Melbouci, Kathia, Nashashibi, Fawzi
The most commonly used method for addressing 3D geometric registration is the iterative closet-point algorithm, this approach is incremental and prone to drift over multiple consecutive frames. The Common strategy to address the drift is the pose gra
Externí odkaz:
http://arxiv.org/abs/2309.09934
The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain be
Externí odkaz:
http://arxiv.org/abs/2308.04312
Predicting the trajectories of surrounding agents is an essential ability for autonomous vehicles navigating through complex traffic scenes. The future trajectories of agents can be inferred using two important cues: the locations and past motion of
Externí odkaz:
http://arxiv.org/abs/2005.02545
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth data with optional dense RGB, and accomplish depth completion and se
Externí odkaz:
http://arxiv.org/abs/1808.00769
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster converge
Externí odkaz:
http://arxiv.org/abs/1807.02371
Publikováno v:
In Annual Reviews in Control 2020 49:81-94
