Abstract-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.

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.

Crossroads of international issues, maritime domain is facing growing human activities (fishing, transportation, boating…) involving a large spectrum of ships from small sailing boats to super tankers. This increase of maritime mobilities has favored the appearance and generalization of position report systems for keeping track of ships movements. Amongst these systems, cooperative position reports using devices such as the Automatic Identification System (AIS) have been widely deployed and used. Recent works have shown that falsification of AIS messages is possible, and therefore could mask or favor illegal actions, lead to disturbance of monitoring systems and new maritime risks. This paper presents these new threats and risks and introduces a novel methodological approach for modelling, analyzing and detecting such maritime events.

Abstract-Overspeeding is both a cause and an aggravation factor of traffic accidents. Consequently, lots of efforts are devoted so as to limit overspeeding and consequently to increase the safety of road networks. In this article, a novel approach to compute a safe speed profile to be used in an adaptive Intelligent Speed Adaptation system (ISA) is proposed. The method presents two main novelties. First, the 85 th percentile of observed speeds (V85), estimated along a road section, is used as a reference speed, practiced and practicable in ideal conditions. Second, this reference speed is modulated in adverse weather conditions in order to account for a reduced friction and a reduced visibility distance. The risk is thus mitigated by modulating the potential severity of crashes by means of a generic scenario of accident. 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.

Abstract-In this paper, a novel approach to compute advisory speeds to be used in an adaptive Intelligent Speed Adaptation system (ISA) is proposed. This method is designed to be embedded in the vehicles. It estimates an appropriate speed by fusing in real-time the outputs of ego sensors which detect adverse conditions with roadway characteristics transmitted by distant servers. The method presents two major novelties. First, the 85 th percentile of observed speeds (V85) is estimated along a road, this speed profile is considered as a reference speed practised and practicable in ideal conditions for a lonely vehicle. In adverse conditions, this reference speed is modulated in order to account for lowered friction and lowered visibility distance (top-down approach). Second, this method allows us taking into account the potential seriousness of crashes using a generic scenario of accident. Within this scenario, the difference in speed that should be applied in adverse conditions is estimated so that global injury risk is the same as in ideal conditions.