Abstract

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 [1] 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 [1] 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.

Abstract

While road lane markings detection was extensively studied, in particular for intelligent vehicle applications, the detection and recognition of all kind of marking such as arrows, crosswalks, zebras, words, pictograms, continuous and discontinuous lane markings was drastically less studied. However, it has many potential applications in the design of advanced driver assistance systems, as well as for asset management along itineraries. An algorithm is proposed which is based on the following processing steps: marking pixel extraction, detection using connected components before Inverse Perspective Mapping and recognition based on the comparison with a single pattern or with repetitive rectangular patterns. The proposed algorithm is able to detect and recognize repetitive markings (such as crosswalks) as well as single patterns (such as arrows). We believe that the proposed algorithm can be extended easily to solve the problem of the identification of all types of markings.

Abstract

International audience; The networks used in the Railway domain are usually heterogeneous, not enough protected and not fitted to the usual Cyber Security requirements in terms of sustainability, protection and attack detection. Furthermore, the quick evolution of the telecommunication means, the threats and the sustainability aspects have to be taken into account in order to protect the Railway system. The paper presents the first contributions on Cyber Security for railways that can be divided into three main aspects dealing with the Cyber Security of the wireless part of the railway communication system: detection, decision and Human-in-the-Loop. Part of the work will be devoted to the development of an Open Pluggable Framework (OPF). The OPF is a software framework based on automation principles. It monitors the environment, then some algorithms detect abnormal behaviours, and next, OPF decides which reaction to take and finally apply this action (e.g. an alarm or a reconfiguration). The last part " human in the loop " aims at answering the questions: what happens if the automatic countermeasures fail and how the driver can cope with the attack consequences. It consists in placing professional drivers and OCC operators (Operational Control Center) in a realistic simulator and playing scenarios involving attacks. Scenarios are deduced of a vulnerability analysis and human-system resilience is studied experimentally in observing the human abilities to detect, to understand and to react to (simulated) cyber-attacks, or to their consequences. A preliminary methodology is proposed and discussed through a concrete case study. Document type: Conference object