Over the past decade, a lot of researches have been done on the development of advanced driver assistance systems (ADAS). Most of these ADAS are now active and need to be tested and evaluated before large deployment. In these ADAS, the prototyping and the implementation of the control stages are risky stages and not so easy to carry out. Indeed, the prototyping and the test of such reactive algorithms need heavy hardware and software supports (dedicated vehicle, actuators, hardware architecture, software architecture, sensors). To achieve such active devices, additional developments and implementation of numerous expensive embedded devices are required. Therefore, in order to reduce both time and risk, in early design stage, it becomes necessary to have a very realistic simulation environment dedicated to the development and to the evaluation of these ADAS. For such virtual platform, it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, we present a dedicated couple of platforms with their efficient interconnection for the prototyping of such ADAS. Initially, the SiVIC simulation platform has been developed to generate the virtual world (environments, sensors, actuators, vehicles). In order to improve the real time prototyping capabilities of SiVIC, an efficient interconnection of this first platform has been done with RTMaps platform. This second one is mainly dedicated to the multi-sensors data processing (data management, fusion, flow recording and replaying). In this paper we will show the interest of such bi-directionnal interconnected platforms to prototype complex and real time embedded ADAS. This interconnection can be done not only on one computer but also on a distributed and distant computers architecture. The relevance of this approach will be illustrated with an automatic parking application.

In this paper, we present the architecture of an innovative docking station for electric vehicles recharging and a hybrid control scheme for automatic docking of the vehicles. This work is a part of on-going project concerning the development of a smart charging station for electric vehicles equipped with an automated arm, which connect the vehicle to the charging station, and an infrared beacon system for localizing the automatically maneuvering vehicle in the docking area. The proposed control scheme combines time-optimal (bang-bang) control with continuous time-invariant nonlinear control, which stabilizes the vehicle to a small neighborhood of the docking point. Simulation and experimental results illustrate the effectiveness of the proposed controller.

The objective of the CityMobil project is to achieve a more effective organisation of urban transport, resulting in a more rational use of motorised traffic with less congestion and pollution, safer driving, a higher quality of living and an enhanced integration with spatial development. This objective is brought closer by developing integrated traffic solutions: advanced concepts for innovative autonomous and automated road vehicles for passengers and goods, embedded in an advanced spatial setting. This paper presents the automated road vehicles service demonstration to be held in La Rochelle in 2011.

It is obvious to say that perception is necessary to drive. Futhermore we can say that a good visibility is a guaranty for passengers security. The driver will adapt the vehicle speed to the offered visibility. Strong visibility reductions (dense fog for instance) are conditions of risk of accident. We therefore propose here a method to perform an onboard estimation of the visibility distance. Once this estimate is obtained, assistance could be offered to the driver (eg if it runs at a speed not adapted to the current visibility) or to the road infrastructure management so that it can inform other users of risk on his road network. This method uses images acquired by an onboard camera filming the scene and the estimation of vehicle motion. Thus, from this information we will explain how we can achieve a spatial partial structure reconstruction to estimate the visibility distance.

The presence of an area with low visibility conditions is a relevant information for autonomous vehicle as far as environment sensing is important regarding safety. In this aim, we develop a generic sensor of visibility using an onboard camera in a vehicle. Our approach consists in estimating the range to the most distant object belonging to the plane of the road having at least 5% of contrast. The originality of this approach lies in the fact that the depth map of the vehicle environment is obtained by aligning the road plane in successive images. This algorithm exploits the dynamics of the vehicle which is given or observed from proprioceptive sensors classically available on public vehicles. In this paper, we present the principle of our approach in terms of image processing and explain how the vehicle dynamics takes part in it with a sensitivity study.