Christian Laugier

Learn More
Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges for driver assistance systems. Most of today’s systems use target tracking algorithms based on object models. They work quite well in simple environments such as freeways, where few potential obstacles have to be considered. However,(More)
It may come as a surprise that there is not a large literature on the use of probabilistic methods for robot control. After all, in the last 20 years there has been a huge amount of research into probabilistic methods in many areas of artificial intelligence, even in communities that are more neurally-focused than mainstream AI. However, almost all of this(More)
Vehicle navigation in dynamic environments is a challenging task, especially when the motion of the obstacles populating the environment is unknown beforehand and is updated at runtime. Traditional motion planning approaches are too slow to be applied in real-time to this problem, whereas reactive navigation methods have generally a too short look-ahead(More)
The Bayesian occupancy filter (BOF) (Coue et al., 2002) has achieved promising results in the object tracking applications. This paper presents a new development of BOF which inherits original BOF's advantages. Meanwhile, the new formulation has significantly reduced original BOF's complexities and can be run in realtime. In Bayesian occupancy filter, the(More)
Most of present work for autonomous navigation in dynamic environment doesn't take into account the dynamics of the obstacles or the limits of the perception system. To face these problems we applied the probabilistic velocity obstacle (PVO) approach (Kluge and Prassler, 2004) to a dynamic occupancy grid. The paper presents a method to estimate the(More)
The paper describes a navigation algorithm for dynamic, uncertain environment. Moving obstacles are supposed to move on typical patterns which are pre-learned and are represented by Gaussian processes. The planning algorithm is based on an extension of the rapidly-exploring random tree algorithm, where the likelihood of the obstacles trajectory and the(More)
It has been shown that the dynamic environment around the mobile robot can be efficiently and robustly represented by the Bayesian occupancy filter (BOF) [1]. In the BOF framework, the environment is decomposed into a gridbased representation in which both the occupancy and the velocity distributions are estimated for each grid cell. In such a(More)
Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead(More)
Amyotrophic lateral sclerosis, or ALS, is a degenerative disease of the motor neurons that eventually leads to complete paralysis. We are developing a wheelchair system that can help ALS patients, and others who can't use physical interfaces such as joysticks or gaze tracking, regain some autonomy. The system must be usable in hospitals and homes with(More)
This paper presents the first working prototype of a brain controlled wheelchair able to navigate inside a typical office or hospital environment. This brain controlled wheelchair (BCW) is based on a slow but safe P300 interface. To circumvent the problem caused by the low information rate of the EEG signal, we propose a motion guidance strategy providing(More)