[Show abstract][Hide abstract] ABSTRACT: Road-traffic-incident analysis has shown that 52% of incidents are caused by a collision between two vehicles or between a vehicle and an obstacle. In this paper, the REduce Speed of Collision Under Emergency (RESCUE) collision-mitigation system (version 1.0) is presented and evaluated toward various typical road situations. The aim of the RESCUE system is to decrease the kinetic energy dissipated during a collision through automatic emergency braking that occurs 1 s before the collision. This emergency braking is triggered by an alarm coming from a decision unit taking into consideration the results of a generic obstacle-detection system-based on fusion between stereovision and laser scanner-and a warning area in front of the vehicle. The different subsystems are presented. Then, the behavior of the RESCUE collision-mitigation system toward various typical dangerous road situations is assessed through systematic tests. These quantitative tests are completed by qualitative ones carried out on 737 km of open roads (freeways, highways, rural roads, downtown) to provide a more precise idea about the false-alarm rate. The experiments show the system is promising in terms of reliability, genericity, and efficiency
Preview · Article · Feb 2007 · IEEE Transactions on Vehicular Technology
[Show abstract][Hide abstract] ABSTRACT: To be exploited for driving assistance purpose, a road obstacle detection system must have a good detection rate and an extremely low false detection rate. Moreover, the field of possible applications depends on the detection range of the system. With these ideas in mind, we propose in this paper a long range generic road obstacle detection system based on fusion between stereovision and laser scanner. The obstacles are detected and tracked by the laser sensor. Afterwards, stereovision is used to confirm the detections. An overview of the whole method is given. Then the confirmation process is detailed: three algorithms are proposed and compared on real road situations
[Show abstract][Hide abstract] ABSTRACT: We propose a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context. The proposed system is able to estimate the position and the height, width and depth of generic obstacles at video frame rate (25 frames per second). The vehicle pitch, estimated by stereovision, is used to filter laser scanner raw data. Objects out of the road are removed using road lane information computed by stereovision. Various fusion schemes are proposed and one is experimented. Results of experiments in real driving situations (multi-pedestrians and multi-vehicles detection) are presented and stress the benefits of our approach.
[Show abstract][Hide abstract] ABSTRACT: Road traffic incidents analysis has shown that 52% of them are caused by a collision between two vehicles or between a vehicle and an obstacle. In this paper, a collision mitigation system is proposed and evaluated towards various typical road situations. The aim of the system is to decrease the kinetic energy of the collision through automatic emergency braking that occurs 1 second before the collision. This emergency braking is triggered by an alarm coming from a decision unit taking into consideration the results of a generic obstacles detection system -based on fusion between stereovision and laser scanner- and a warning area in front of the vehicle. The different sub-systems are presented. Various typical dangerous road situations are then introduced. The behavior of the collision mitigation system towards these situations is presented through real tests carried out in the context of the ARCOS French project. These experiments show the reliability, the genericity and the efficiency of the system. In particular, the false alarm rate is low, the detection rate is high and the system proves to be reactive.
[Show abstract][Hide abstract] ABSTRACT: In this article, we present how, starting from an credibilist multi-object association algorithm we can carry out a multi-sensor fusion algorithm. The tracking algorithm makes a data association between predicted information and observations. These information are imperfect. The algorithm takes into account the inaccuracy and the uncertainty of the data and the reliability of the sensors. Association is realized with the belief theory. This method can be applied to the fusion of several homogeneous data sources. The problem arises when information are heterogeneous. Here, we answer this problem by using a decentralized architecture which breaks up into two stages. The first consists in having at first a local processor to each sensor. This local processing makes it possible to obtain a set of homogeneous data. The second stage uses these homogeneous data to carry out global fusion. This fusion gives a representation and a global view of a dynamic environment around a reference vehicle the most faithful and most reliable by using all available information. Moreover, this very general approach shows the polyvalence of this algorithm which con be in any case-used for multi-object matching, local tracking, multi-sensors fusion and global tracking.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present a method based on believe theory to combine expert opinion or symbolic sensor data. We consider applications with large frame of discernment and we propose generalisation for believe mass combination. In order to take into account of unknown hypothesis, we introduce a new framework for Dempster's combination: it is called the extended open world. This framework offers the possibility to have an opinion about the conflict between the experts and about the opportunity to introduce a new hypothesis in the frame of discernment. Some results highlight advantages of this framework in decision process.
[Show abstract][Hide abstract] ABSTRACT: Situation characterization requires numerous data in order to
recognize real driving situation. The quality of data is important to
assume the quality of situation recognition and characterization. The
aim of this work is to describe imperfections in sensor data processing
and their implications in decision processing. Expected results are a
granularity definition to model and propagate imperfection. In this
paper we focus on uncertainty characterization deriving from the
reliability and confidence in sensors and data processing
[Show abstract][Hide abstract] ABSTRACT: Dans cet article, nous présentons un algorithme de cartographie de l'environnement dynamique autour d'un véhicule instrumenté (STRADA). Il s'appuie sur deux outils mathématiques pour gérer l'imprécision et l'incertitude qui caractérisent la présence et la position d'un véhicule : le filtrage de Kalman pour l'estimation et la prédiction, et la théorie de la croyance pour l'association et le suivi multi-objet. Nous pouvons ainsi quantifier l'imprécision et la certitude d'un résultat à chaque étape du traitement de l'information. Cette étude entre de le cadre du projet CASSICE et plus généralement dans le développement de système d'aide à la conduite automobile.