An ontology-based approach to relax traffic regulation for autonomous vehicle assistance

Traffic regulation must be respected by all vehicles, either human- or computer- driven. However, extreme traffic situations might exhibit practical cases in which a vehicle should safely and reasonably relax traffic regulation, e.g., in order not to be indefinitely blocked and to keep circulating. In this paper, we propose a high-level representation of an automated vehicle, other vehicles and their environment, which can assist drivers in taking such "illegal" but practical relaxation decisions. This high-level representation (an ontology) includes topological knowledge and inference rules, in order to compute the next high-level motion an automated vehicle should take, as assistance to a driver. Results on practical cases are presented

Real-time visual perception : detection and localisation of static and moving objects from a moving stereo rig

International audienceWe present a novel method for scene reconstruction and moving object detection and tracking, using extensive point tracking (typically more than 4000 points per frame) over time. Current neighbourhood is reconstructed in the form of a 3D point cloud, which allows for extra features (ground detection, path planning, obstacle detection). Reconstruction framework takes moving objects into account, and tracking over time allows for trajectory and speed estimation

Comprehensive Extrinsic Calibration of a Camera and a 2D Laser Scanner for a Ground Vehicle

Cameras and laser scanners are two important kinds of perceptive sensors and both become more and more commonly used for intelligent ground vehicles; the calibration of these sensors is a fundamental task. A new method is proposed to perform COMPREHENSIVE extrinsic calibration of a SINGLE camera-2D laser scanner pair, i.e. the process of revealing ALL the spatial relationships among the camera coordinates system, the laser scanner coordinates system, the ground coordinates system, and the vehicle coordinates system. The proposed method is mainly based on the convenient and widely used chessboard calibration practice and can be conveniently implemented. The proposed method has been tested on both synthetic data and real data based experiments, which validate the effectiveness of the proposed method.La caméra et le scanner laser sont deux types importants de capteurs perceptifs et tous les deux deviennent de plus en plus communs pour de nombreuses applications des véhicules intelligents. La calibration de ces capteurs est une tâche fondamentale. Dans ce rapport, on a propose une nouvelle méthode pour réaliser la calibration extrinsèque compréhensive d'une seule paire caméra-scanner laser 2D, à savoir le procédé de révéler tous les relations spatiales parmi un système de coordonnées caméra, un système de coordonnées scanner laser, un système de coordonnées terrestre, et un système de coordonnées véhicule. La méthode proposée se fonde principalement sur la practique de cabliration au damier et est facile à mettre en œuvre. Des tests des données réelles et des données synthétiques ont validé la performance de la méthode proposée

Vehicle recognition and tracking using a generic multi-sensor and multi-algorithm fusion approach

International audienceThis paper tackles the problem of improving the robustness of vehicle detection for Adaptive Cruise Control (ACC) applications. Our approach is based on a multisensor and a multialgorithms data fusion for vehicle detection and recognition. Our architecture combines two sensors: a frontal camera and a laser scanner. The improvement of the robustness stems from two aspects. First, we addressed the vision-based detection by developing an original approach based on fine gradient analysis, enhanced with a genetic AdaBoost-based algorithm for vehicle recognition. Then, we use the theory of evidence as a fusion framework to combine confidence levels delivered by the algorithms in order to improve the classification 'vehicle versus non-vehicle'. The final architecture of the system is very modular, generic and flexible in that it could be used for other detection applications or using other sensors or algorithms providing the same outputs. The system was successfully implemented on a prototype vehicle and was evaluated under real conditions and over various multisensor databases and various test scenarios, illustrating very good performances

Proposition for propagated occupation grids for non-rigid moving objects tracking

International audienceAutonomous navigation among humans is, however simple it might seems, a difficult subject which draws a lot a attention in our days of increasingly autonomous systems. From a typical scene from a human environment, diverse shapes, behaviours, speeds or colours can be gathered by a lot of sensors ; and a generic mean to perceive space and dynamics is all the more needed, if not easy. We propose an incremental evolution over the well-known occupancy grid paradigm, introducing grid cell propagation over time and a limited neighbourhood, handled by probabilistic calculus. Our algorithm runs in real-time from a GPU implementation, and considers completely generically space-cells propagation, without any a priori requirements. It produces a set of belief maps of our environment, handling occupancy, but also items dynamics, relative rigidity links, and an initial object classification. Observations from free-space sensors are thus turned into information needed for autonomous navigation

Extended occupation grids for non-rigid moving objects tracking

International audienceWe present an evolution of traditional occupancy grid algorithm, based on an extensive probabilistic calculus of the evolution of several variables on a cell neighbourhood. Occupancy, speed and classification are taken into account, the aim being to improve overall perception of an highly changing un- structured environment. Contrary to classical SLAM algorithms, no requisite is made on the amount of rigidity of the scene, and tracking do not rely on geometrical characteristics. We believe that this could have important applications in the automotive field, both from autonomous vehicle and driver assistance, in some areas difficult to address with current algorithms. This article begins with a general presentation of what we aim to do, along with considerations over traditional occupancy grids limits and their reasons. We will then present our proposition, and detail some of its key aspects, namely update rules and perfor- mance consequences. A second part will be more practical, and will begin with a brief presentation of the GPU implementation of the algorithm, before turning to sensor models and some results

An improved "flies" method for stereo vision: application to pedestrian detection

In the vast research field of intelligent transportation systems, the problem of detection (and recognition) of environment objects, for example pedestrians and vehicles, is indispensable but challenging. The research work presented in this paper is devoted to stereo-vision based method with pedestrian detection as its application (a sub-part of the French national project “LOVe”: Logiciels d'Observation des Vulnerables). With a prospect of benefiting from an innovative method i.e. the genetic evolutionary “flies” method proposed by former researchers on continuous data updating and asynchronous data reading, we have carried on the “flies” method through the task of pedestrian detection affiliated with the “LOVe” project. Compared with former work of the “flies” method, two main contributions have been incorporated into the architecture of the “flies” method: first, an improved fitness function has been proposed instead of the original one; second, a technique coined “concentrating” has been integrated into the evolution procedure. The improved “flies” method is used to offer range information of possible objects in the detection field. The integrate scheme of pedestrian detection is presented as well. Some experimental results are given for validating the performance improvements brought by the improved “flies” method and for validating the pedestrian detection method based on the improved “flies” method

Recognition of Supplementary Signs for Correct Interpretation of Traffic Signs

International audienceTraffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. The main difficulty of supplementary sub-sign recognition is the potentially infinite number of classes, as nearly any information potentially infinite number of classes, as nearly any information can be written on them. In this paper, we propose and evaluate a hierarchical approach for recognition of supplementary signs, in which the "meta-class" of the sub-sign (Arrow, Pictogram, Text or Mixed) is first determined. The classification is based on the pyramid-HOG feature, completed by dark area proportion measured on the same pyramid. Evaluation on a large database of images with and without supplementary signs shows that the classification accuracy of our approach 95% precision and recall. When used on output of our sub-sign specific detection algorithm, the global correct detection and recognition rate is 91%

Subsign detection with region-growing from contrasted seeds

International audienceSpeed limit determination systems for cars based on vision are more and more developed. Roadsign detection is nowadays a well managed problem. However, in some situations this information is not sufficient to know the speed limitation. Restrictions are sometimes applicable and specified by subsigns. These small rectangles often provide essential information about the applicability scope (vehicle type, condition, lane, etc.) of speed limits. We present an approach of subsign localization based on region growing with an initial step of seed selection using morphological reconstruction. A comparison is also performed with three other techniques based on edge, color and graph on two databases gathering French and German subsigns. The obtained subsign correct detection is above 65%

Detection of Unfocused Raindrops on a Windscreen using Low Level Image Processing

International audienceIn a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time. We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms