A complete system to determine the speed limit by fusing a GIS and a camera

International audienceDetermining the speed limit on road is a complex task based on the Highway Code and the detection of temporary speed limits. In our system, these two aspects are managed by a GIS (Geographical Information System) and a camera respectively. The vision-based system aims at detecting the roadsigns as well as the subsigns and the lane markings to filter those applicable. The two sources of information are finally fused by using the Belief Theory to select the correct speed limit. The performance of a navigation-based system is increased by 19%

Improving pan-European speed-limit signs recognition with a new “global number segmentation” before digit recognition

International audienceIn this paper, we present an improved European speed-limit sign recognition system based on an original “global number segmentation” (inside detected circles) before digit segmentation and recognition. The global speed-limit sign detection and correct recognition rate, currently evaluated on videos recorded on a mix of French and German roads, is around 94 %, with a misclassification rate below 1%, and not a single validated false alarm in several hours of recorded videos. Our greyscale-based system is intrinsically insensitive to colour variability and quite robust to illumination variations, as shown by an on-road evaluation under bad weather conditions (cloudy and rainy) which yielded 84% good detection and recognition rate, and by a first night-time on-road evaluation with 75% correct detection rate. Due to recognition occurring at digit level, our system has the potential to be very easily extended to handle properly all variants of speed-limit signs from various European countries. Regarding computation load, videos with images of 640x480 pixels can be processed in real-time at ~20frames/s on a standard 2.13GHz dual-core laptop

Detection and recognition of end-of-speed-limit and supplementary signs for improved european speed limit support

International audienceWe present two new features for our prototype of European Speed Limit Support system: detection and recognition of end-of-speed-limit signs, as well as a framework for detection and recognition of supplementary signs located below main signs and modifying their scope (particular lane, class of vehicle, etc...). The end-of-speed-limit signs are globallyrecognized by a Multi-Layer Perceptron (MLP) neural network. The supplementary signs are detected by applying a rectangle-detection in a region below recognized speed-limit signs, followed by a MLP neural network recognition. A common French+German end-of-speed-limit signs recognition has been designed and successfully tested, yielding 82% detection+recognition. Results for detection and recognition of a first kind of supplementary sign (French exit-lane) are already satisfactory (78% correct detection rate), and our framework can easily be extended to handle other types of supplementary signs. To our knowledge, we are the first team presenting results on detection and recognition of supplementary signs below speed signs, which is a crucial feature for a reliable Speed Limit Support

Tracking both pose and status of a traffic light via an Interacting Multiple Model filter

International audienceEither for driver assistance systems or autonomous vehicles, detecting traffic lights (status and pose) is required when Intelligent Transport Systems go downtown. As detection algorithms could still have some misclassification on the traffic light status, this paper proposes a solution to nearly avoid this problem. An Interacting Multiple Model filter is used to track both the position and the status of a traffic light through the time and to increase traffic light recognition performances for automation purpose.Aussi bien pour la conduite autonome que pour les systèmes d'aide à la conduite, il est nécessaire de pouvoir détecter les feux de trafic (status et position) pour que les différents systèmes de transport intelligent puissent fonctionner en centre ville. Les algorithmes de détection simples pouvant toujours produire des erreurs de classification sur le status du feu, ce papier propose une solution pour contourner le problème. Un filtre "Interacting Multiple Model" est en effet utilisé pour suivre à la fois la position et le status du feux dans le temps et pour ainsi améliorer les performances d'un système global

Credibilist Simultaneous Localization and Mapping with a LIDAR

International audienceFrom the early beginning, the Simultaneous Localization And Mapping (SLAM) problem has been approached using a probabilistic background. A new solution based on the Transferable Belief Model (TBM) framework is proposed in this article. It appears that this representation of knowledge affords numerous advantages over the classic probabilistic ones and leads to particularly good performances (an average of 3.2% translation drift and 0.0040deg/m rotation drift), especially when it comes to crowded environment. By introducing the basic concepts of a Credibilist SLAM, this article aims at proving that the use of this new theoretical context opens a lot of perspectives for the SLAM community.Dès le départ, la problématique de localisation et cartographie simultanée (SLAM) a été approchée avec un contexte probabiliste. Une nouvelle solution basée sur les modèles de croyance transférable (TBM) est proposée dans cet article. Ce type de représentation de la connaissance s'avère avantageux en comparaison aux probabilités et conduit à de particulièrement bonnes performances (une moyenne de 3,2% en dérive en translation et de 0,0040 deg/m en dérive en rotation), spécialement pour les environnements encombrés. En introduisant les concepts de base d'un SLAM crédibiliste, cet article tente de prouver que l'utilisation de ce nouveau contexte théorique ouvre de nombreuses perspectives pour la communauté du SLA

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

International audienceNowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment

Decision-making for automated vehicles at intersections adapting human-like behavior

International audienceLearning from human driver’s strategies for solving complex and potentially dangerous situations including interaction with other road users has the potential to improve decision-making methods for automated vehicles. In this paper, we focus on simple unsignalized intersections and roundabouts in presence of another vehicle. We propose a human-like decision-making algorithm for these scenarios built up from human drivers recordings. The algorithm includes a risk assessment to avoid collisions in the intersection area.Three road topologies with different interaction scenarios were presented to human participants on a previously developed simulation tool. The same scenarios have been used to validate our decision-making process. The algorithm showed promising results with no collisions in all setups and the ability to successfully determine to go before or after another vehicle

Optimisation des Lois de Commande d Éclairage Automobile par Fusion de Données

La conduite nocturne avec un système d éclairage conventionnel n est pas suffisamment sécurisante. En effet, si l on roule beaucoup moins de nuit, plus de la moitié des accidents de la route arrivent pendant cette période. Dans le but de réduire ces accidents du trafic nocturne, le projet européen Adaptive Front lighting System (AFS) a été initié. L objectif pour les constructeurs et équipementiers automobiles est d aboutir en 2008 à un changement de réglementation de l éclairage automobile. Pour cela, ils explorent les différents modes de réalisation possible de nouvelles fonctions d éclairage basées sur la déformation du faisceau lumineux, et étudient la pertinence, l efficacité par rapport à la situation de conduite, mais aussi les dangers associés à l utilisation, pour ces fonctions, d éclairage, d informations issues du véhicule ou de l environnement. Depuis 2003, des véhicules proposent d orienter l éclairage en virage, cette solution, ne tenant compte que des actions du conducteur sur le volant, permet d améliorer la visibilité en orientant le faisceau vers l intérieur du virage. Cependant, le profil de route (intersections, courbure, etc.) n étant pas toujours connu du conducteur, les performances liées à cette solution sont dès lors limitées. Or les systèmes embarqués de navigation, d une part peuvent apporter des informations primordiales sur cette forme, et d autre part disposent d informations contextuelles (ouvrages d art, nature de la route, rayon de virage, limitations de vitesse en vigueur ). Le sujet de cette thèse a pour objectif d optimiser les lois de commande des systèmes d éclairage en s appuyant sur la fusion des informations issues des systèmes de navigation avec celles des capteurs embarqués dans le véhicule (caméras, ), tout en sachant jusqu à quel point les systèmes actuels et futurs peuvent répondre à ces attentes de manière efficace et fiable. Ainsi, cette fusion des informations, appliquée ici à la prise de décision, permet de définir les situations et les contextes de conduite de l environnement d évolution du véhicule (autoroute, ville, etc.) et de choisir la loi appropriée parmi les différentes lois de commande d éclairage développées pour répondre aux fonctionnalités recherchées (code autoroute, code ville, code virage). Cette démarche permet de choisir en temps réel, et par anticipation, entre ces différentes lois de commande. Elle permet, par conséquent, l amélioration de la robustesse du système d éclairage. Deux points sont à l origine de cette amélioration. Premièrement, à partir du système de navigation, nous avons développé un capteur virtuel d horizon glissant événementiel permettant la détermination précise des différentes situations de conduite en utilisant un automate d états finis. Il permet ainsi de pallier aux problèmes de la nature ponctuelle des informations du système de navigation. Deuxièmement, nous avons développé un capteur virtuel générique de détermination des situations de conduite basé sur la théorie des croyances en utilisant un système de navigation et la vision. Ce capteur combine les confiances en provenance des deux sources pour mieux distinguer les différentes situations et les différents contextes de conduite et de pallier aux problèmes des deux sources prises indépendamment. Il permet également de construire une confiance du système de navigation. Ce capteur générique est généralisable à des systèmes d aide à la conduite (ADAS) autre que l éclairage. Ceci a été montré en l appliquant à un système de détection des limitations de vitesses réglementaires SLS (Speed Limit Support). Les deux capteurs virtuels développés ont été appliqués à l optimisation de l éclairage AFS et au système SLS. Ces deux systèmes ont été implémentés sur un véhicule de démonstration et ils sont actuellement opérationnels. Ils ont été évalués par différents types de conducteur allant des non experts aux experts de l éclairage et des systèmes d aide à la conduite (ADAS). Ils ont été également montrés auprès des constructeurs automobiles (PSA, Audi, Renault, Honda, etc.) et au cours de différents techdays et ils ont prouvé leur fiabilité lors des démonstrations sur routes ouvertes avec des différentes situations et différents contextes de conduite.Night-time driving with conventional headlamps is particularly unsafe. Indeed, if one drives much less at night, more than half of the driving fatalities occur during this period. To reduce these figures, several automotive manufacturers and suppliers participated to the European project Adaptive Front lighting System (AFS). This project has the aim to define new lightings functions based on a beam adaptation to the driving situation. And, it has to end in 2008 with a change of regulation of the automotive lighting allowing so realisation of all new AFS functions. For that, they explore the possible realisation of such new lighting functions, and study the relevance, the efficiency according to the driving situation, but also the dangers associated with the use, for these lighting functions, of information from the vehicle or from the environment. Since 2003, some vehicles are equipped by bending lights, taking account only of actions of the driver on the steering wheel. These solutions make it possible to improve the visibility by directing the beam towards the interior of the bend. However, the road profile (intersections, bends, etc) not being always known for the driver, the performances related to these solutions are consequently limited. However the embedded navigation systems, on the one hand can contain information on this road profile, and on the other hand have contextual information (engineering works, road type, curve radius, speed limits ). The topic of this thesis aims to optimize lighting control laws based on fusion of navigation systems information with those of vehicle embedded sensors (cameras, ), with consideration of their efficiency and reliability. Thus, this information fusion, applied here to the decision-making, makes it possible to define driving situations and contexts of the vehicle evolution environment (motorway, city, etc) and to choose the appropriate law among the various of developed lighting control laws (code motorway lighting, town lighting, bending light). This approach makes it possible to choose in real time, and by anticipation, between these various lighting control laws. It allows, consequently, the improvement of the robustness of the lighting system. Two points are at the origin of this improvement. Firstly, using the navigation system information, we developed a virtual sensor of event-based electronic horizon analysis allowing an accurate determination of various driving situations. It uses a finite state machine. It thus makes it possible to mitigate the problems of the ponctual nature of the navigation system information. Secondly, we developed a generic virtual sensor of driving situations determination based on the evidence theory of using a navigation system and the vision. This sensor combines confidences coming from the two sources for better distinguishing between the various driving situations and contexts and to mitigate the problems of the two sources taken independently. It also allows building a confidence of the navigation system using some of their criteria. This generic sensor is generalizable with other assistance systems (ADAS) that lighting one. This was shown by applying it to a speed limit detection system SLS (Speed Limit Support). The two developed virtual sensors were applied to the optimization of lighting system (AFS) and for the SLS system. These two systems were implemented on an experimental vehicle (demonstration vehicle) and they are currently operational. They were evaluated by various types of driver going from non experts to experts. They were also shown to car manufacturers (PSA, Audi, Renault, Honda, etc.) and during different techdays. They proved their reliability during these demonstrations on open roads with various driving situations and contexts.MULHOUSE-SCD Sciences (682242102) / SudocSudocFranceF

Improvement of multisensor fusion in speed limit determination by quantifying navigation reliability

International audienceSpeed limit determination is a complex task that may be solved by fusing data from GIS (Geographical Information System) and camera sensor. Among the existing data fusion models the Dempster-Shafer Belief Theory is found to be the most appropriate in this application. A confidence measure weights each source output, namely speed limit present on road sign and driving situation. Using the discounting scheme of Dempster-Shafer, we propose a new way of computing the navigation confidence measure by taking into account the reliability of the GIS. Preliminary tests showed that our method achieves promising results and solves conflicts between vision and navigation-based system