This paper presents the architecture of a high- density parking solution based on car-like robots specifically designed to move cars. The main difficulty is to park the vehicles close to one another which implies hard constraints on the robot motion and localization. In particular, this paper focuses on navigation in narrow lanes. We propose a Lyapunov- based control strategy that has been derived after expressing the problem in a Configuration Space formulation. The current solution has been implemented and tested on Stanley Robotics' robots and has been running in production for several months. Thanks to the Configuration Space formulation, we are able to guarantee the obstacles’ integrity. Moreover, a method for calibrating the GPS orientation with a high-precision is derived from the present control strategy.

Advanced Driver Assistance Systems (ADAS) based on video camera are increasingly invasive in todays car. However, if most of these systems work properly under clear weather, their performances drastically fall in case of adverse weather or bad lighting conditions. In this paper we study how to predict the road state: wet or dry. We simulate realistic embedded images relying on scene's physical data: road's Bidirectional Reflectance Distribution Function (BRDF), vehicle direction, sun position and daylight model of the sky. These data are used to produce a database of 640 synthetic images of wet and dry road scene, under different conditions (weather, date, direction). This database allows us to evaluate the relationship between conditions and road state, in order to determine from a given condition if the road state could be predictable. Finally, an optimization method is used to estimate road surfaces' BRDF parameters.

Compared with daytime, a larger proportion of road accidents happens during nighttime. The altered visibility for drivers partially explains this situation. It becomes worse when dense fog is present. In this paper, we first define a standard night visibility index, which allows specifying the type of fog that an advanced driver assistance system should recognize. A methodology to detect the presence of night fog and characterize its density in images grabbed by an in-vehicle camera is then proposed. The detection method relies on the visual effects of night fog. A first approach evaluates the presence of fog around a vehicle due to the detection of the backscattered veil created by the headlamps. In this aim, a correlation index is computed between the current image and a reference image where the fog density is known. It works when the vehicle is alone on a highway without external light sources. A second approach evaluates the presence of fog due to the detection of halos around light sources ahead of the vehicle. It works with oncoming traffic and public lighting. Both approaches are illustrated with actual images of fog. Their complementarity makes it possible to envision a complete night-fog detection system. If fog is detected, its characterization is achieved by fitting the different correlation indexes with an empirical model. Experimental results show the efficiency of the proposed method. The main applications for such a system are, for instance, automation or adaptation of vehicle lights, contextual speed computation, and reliability improvement for camera-based systems.

This paper presents a new approach to fuse 3D and 2D information in a driver assistance setup, in particular to perform obstacle detection and tracking. We propose a new cooperative fusion method between two exteroceptive sensors: it is able to address highly non linear dynamic configuration without any assumption on the driving maneuver. Information are provided by a mono-layer laser scanner and a monocular camera which are unsynchronized. The initial detection stage is performed using the 1D laser data, which computes clusters of points which might correspond to vehicles present on the road. These clusters are projected to the image to define targets, which will be tracked using image registration techniques. This multi-object association and tracking scheme is implemented using belief theory integrating temporal and spatial information, which allows the estimation of the dynamic state of the tracks and to monitor appearance and disappearance of obstacles. Accuracy of the method is evaluated on a database made publicly available, focus is cast on the relative localization of the vehicle ahead: estimations of its longitudinal and lateral distances are analysed.

Overspeeding is both a cause and an aggravation factor of traffic accidents. Consequently, much effort is devoted to limiting overspeeding and, consequently, to increasing the safety of road networks. In this paper, a novel approach to computing a safe speed profile to be used in an adaptive intelligent speed adaptation (ISA) system is proposed. The method presents two main novelties. First, the 85th percentile of observed speeds (V85), estimated along a road section, is used as a reference speed, which is practiced and practicable in ideal conditions. Second, this reference speed is modulated in adverse weather conditions to account for reduced friction and reduced visibility distance. The risk is thus mitigated by modulating the potential severity of crashes by means of a generic scenario of accidents. Within this scenario, the difference in speed that should be applied in adverse conditions is estimated so that the highway risk is the same as in ideal conditions. The system has been tested on actual data collected on a French secondary road and implemented on a test track and a fleet of vehicles. The performed tests and the experiments of acceptability show a great interest for the deployment of such a system.

Advanced Driver Assistance Systems (ADAS) based on video camera tends to be generalized in today's automotive. However, if most of these systems perform nicely in good weather conditions, they perform very poorly under adverse weather particularly under rain. We present a novel approach that aims at detecting raindrops on a car windshield using only images from an in-vehicle camera. Based on the photometric properties of raindrops, the algorithm relies on image processing technics to highlight raindrops. Its results can be further used for image restoration and vision enhancement and hence it is a valuable tool for ADAS.

Compared to daytime, a larger proportion of accident happens during nighttime. The altered visibility of the road scene for the drivers may partially explain this situation. The latter becomes worse in fog presence. In this paper, two camera-based methods are proposed to detect the presence of night fog in images grabbed by in-vehicle multipurpose cameras. They rely on the visual effects of night fog. A first approach can assess the presence of fog around the vehicle thanks to the detection of the backscattered veil induced by the vehicle ego lights. It aims at detecting fog when the vehicle is alone in absence of exterior public lighting. A second approach can assess the presence of fog thanks to the detection of halos around light sources in the vehicle environment. It aims at detecting fog in presence of road traffic or public lighting. Both methods are presented and illustrated with actual images of fog. Their complementarity makes it possible to envision a complete night fog detection system. There are numerous applications for such a system: automation or adaptation of vehicle lights, contextual speed computation and reliability improvement for camera-based systems.

One source of accidents when driving a vehicle is the presence of homogeneous and heterogeneous fog. Fog fades the colors and reduces the contrast of the observed objects with respect to their distances. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement of road images. The visibility enhancement algorithm proposed in is not dedicated to road images and thus it leads to limited quality results on images of this kind. In this paper, we interpret the algorithm in as the inference of the local atmospheric veil subject to two constraints. From this interpretation, we propose an extended algorithm which better handles road images by taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are its speed, the possibility to handle both color images or gray-level images, and its small number of parameters. A comparative study and quantitative evaluation with other state-of-the-art algorithms is proposed on synthetic images with several types of generated fog. This evaluation demonstrates that the new algorithm produces similar quality results with homogeneous fog and that it is able to better deal with the presence of heterogeneous fog.