Contribution à la surveillance temps-réel du système "Conducteur - Véhicule - Environnement" : élaboration d'un système intelligent d'aide à la conduite

The present study, carried out in the automotive field, concerns the integration of observation, supervision and control tasks in vehicles. The aim is the development of a driver aid system based on the vehicle location. The NAICC project includes two research activities concerning the longitudinal and lateral control of a car. In the longitudinal part, the system adapts the speed to the driving task. In the lateral part, it corrects the trajectory thanks to a reference path computed on-line. This driver aid system consists of a location module identifying the vehicle on an extended database. Using the determined location and the road characteristics stored in the database, NAICC determines a reference path and the associated speed for a given driving situation. Precise vehicle location as well as precise environment information are required to perform the control tasks. Data fusion techniques, coupled to a new generation of database, are used to obtain the required precision.Le sujet de cette thèse vise l'intégration dans une automobile de fonctions d'observation, de supervision, d'aide à la décision ou encore de commande. La problématique est le développement d'une assistance à la conduite longitudinale et latérale basée sur la localisation du véhicule. Le but est de signaler et corriger les faiblesses de conduite en considérant les paramètres du véhicule, du conducteur et la topologie de la route. Selon la localisation du véhicule, une trajectoire de référence et la vitesse associée sont déterminées en fonction du conducteur et de la phase de conduite. Ces références sont utilisées pour effectuer le contrôle du véhicule ou pour informer le conducteur de l'inadéquation de ses consignes. Dans ce contexte, la localisation du véhicule et particulièrement les informations de l'environnement d'évolution doivent être pertinentes. Elles sont obtenues grâce à une base de données cartographique spécifiquement développée dans le cadre de ces travaux. Celle-ci est caractérisée par une précision supérieure à celle des bases de données traditionnellement employées dans des dispositifs de navigation

Contribution à la surveillance temps-réel du système "conducteur-véhicule-environnement" ( élaboration d'un système intelligent d'aide à la conduite)

Le sujet de cette thèse vise l'intégration dans une automobile de fonctions d'observation, de supervision, d'aide à la décision ou encore de commande. La problématique est le développement d'une assistance à la conduite longitudinale et latérale basée sur la localisation du véhicule. Le but est de signaler et corriger les faiblesses de conduite en considérant les paramètres du véhicule, du conducteur et la topologie de la route. Selon la localisation du véhicule, une trajectoire de référence et la vitesse associée sont déterminées en fonction du conducteur et de la phase de conduite. Ces références sont utilisées pour effectuer le contrôle du véhicule ou pour informer le conducteur de l'inadéquation de ses consignes. Dans ce contexte, la localisation du véhicule est particulièrement les informations de l'environnement d'évolution doivent être précises. Elles sont obtenues grâce à une base de données cartographique d'une précision supérieure à celle des bases de données traditionnelles.The present study, carried out in the automotive field, concerns the integration of observation, supervision and control tasks in vehicles. The aim is the development of a driver aid system based on the vehicle location. The NAICC project includes two research activities concerning the longitudinal and lateral control of a car. In the longitudinal part, the system adapts the speed to the driving task. In the lateral part, it corrects the trajectory thanks to a reference path computed on-line. Tis driver aid system consists of a location module identifying the vehicle on an extended database. Using the determined location ant the road characteristics stored in the database, NAICC determines a reference path and the associated speed for a given driving situation. Precise vehicle location as well as precise environment information are required to perform the control tasks. Data fusion techniques, coupled to a new generation of database, are used to obtain the required precision.MULHOUSE-SCD Sciences (682242102) / SudocSudocFranceF

Traffic Sign Recognition: Benchmark of Credal Object Association Algorithms

International audience—Static and dynamic objects detection and tracking is a classic but still open problem in Intelligent Transportation Systems. Initially formalized in the Bayesian framework, new methods using belief functions have recently emerged. Most of them have been essentially validated in simulations. This paper proposes an association and tracking framework devoted to Traffic Sign Recognition in a mono-sensor context. Potential signs are detected in the camera images. A credal association between new observations and already known objects is performed. Associated objects are tracked over time and in the image space using Kalman Filtering. This global tracking system has been used to evaluate in real-time on large datasets several state-of-the-art credal association methods. The main evaluation criteria is their capability to reduce false detections by keeping a high traffic sign detection rate

Latency Reduction in Narrowband 4G LTE Networks

The next generation cellular networks are expected to improve reliability, energy efficiency, data rate, capacity and latency. Originally, Machine Type Communication (MTC) was designed for low-bandwidth high-latency applications such as, environmental sensing, smart dustbin, etc., but there is additional demand around applications with low latency requirements, like industrial automation, driver-less cars, and so on. Improvements are required in 4G Long Term Evolution (LTE) networks towards the development of next generation cellular networks for providing very low latency and high reliability. To this end, we present an in-depth analysis of parameters that contribute to the latency in 4G networks along with a description of latency reduction techniques. We implement and validate these latency reduction techniques in the open-source network simulator (NS3) for narrowband user equipment category Cat-Ml (LTE-M) to analyze the improvements. The results presented are a step towards enabling narrowband Ultra Reliable Low Latency Communication (URLLC) networks

Latency Reduction for Narrowband LTE with Semi-Persistent Scheduling

The excessive control signaling in Long Term Evolution networks required for dynamic scheduling impedes the deployment of ultra-reliable low latency applications. Semi-persistent scheduling was originally designed for constant bit-rate voice applications, however, very low control overhead makes it a potential latency reduction technique in Long Term Evolution. In this paper, we investigate resource scheduling in narrowband fourth generation Long Term Evolution networks through Network Simulator (NS3) simulations. The current release of NS3 does not include a semi-persistent scheduler for Long Term Evolution module. Therefore, we developed the semi-persistent scheduling feature in NS3 to evaluate and compare the performance in terms of uplink latency. We evaluate dynamic scheduling and semi-persistent scheduling in order to analyze the impact of resource scheduling methods on up-link latency

Magnetometer-Augmented IMU Simulator: In-Depth Elaboration

International audienceThe location of objects is a growing research topic due, for instance, to the expansion of civil drones or intelligent vehicles. This expansion was made possible through the development of microelectromechanical systems (MEMS), inexpensive and miniaturized inertial sensors. In this context, this article describes the development of a new simulator which generates sensor measurements, giving a specific input trajectory. This will allow the comparison of pose estimation algorithms. To develop this simulator, the measurement equations of every type of sensor have to be analytically determined. To achieve this objective, classical kinematic equations are used for the more common sensors, i.e., accelerometers and rate gyroscopes. As nowadays, the MEMS inertial measurement units (IMUs) are generally magnetometer-augmented, an absolute world magnetic model is implemented. After the determination of the perfect measurement (through the error-free sensor models), realistic error models are developed to simulate real IMU behavior. Finally, the developed simulator is subjected to different validation tests

Low Latency V2X Applications and Network Requirements: Performance Evaluation

Vehicle-to-Everything (V2X) communication promises improvements in road safety and efficiency by enabling low-latency and reliable communication services for vehicles. Besides using Mobile Broadband (MBB), there is a need to develop Ultra Reliable Low Latency Communications (URLLC) applications with cellular networks especially when safety-related driving applications are concerned. Future cellular networks are expected to support novel latencysensitive use cases. Many applications of V2X communication, like collaborative autonomous driving requires very low latency and high reliability in order to support real-time communication between vehicles and other network elements. In this paper, we classify V2X use-cases and their requirements in order to identify cellular network technologies able to support them. The bottleneck problem of the medium access in 4G Long Term Evolution(LTE) networks is random access procedure. It is evaluated through simulations to further detail the future limitations and requirements. Limitations and improvement possibilities for next generation of cellular networks are finally detailed. Moreover, the results presented in this paper provide the limits of different parameter sets with regard to the requirements of V2X-based applications. In doing this, a starting point to migrate to Narrowband IoT (NB-IoT) or 5G - solutions is given

LES VEHICULES AUTONOMES ET LE RISQUE TECHNOLOGIQUE : D'OU VIENT-ON ET OU VA-T-ON ?

International audienceThis paper aims to provide some technical and scientific elements to enrich the current thinking in the legal framework about the technological risks introduced by the autonomous vehicle. As a first step, a brief review of autonomous vehicle origins is provided. In a second step, a presentation of the evolution of driving assistance systems and their contribution to road safety is given. Finally, after having introduced the 5 levels of automation, the hierarchical control architecture of a fully autonomous vehicle is presented. In parallel to this description, a discussion of the associated potential technological risks and the scientific and technical means implemented to reduce them is proposed.Cette contribution vise à apporter des éléments techniques et scientifiques en mesure d’enrichir les réflexions actuellement menées dans le cadre juridique à propos des risques technologiques introduits par le véhicule à délégation de conduite. Dans un premier temps, un court rappel de l’origine du véhicule autonome est réalisé. Dans un second temps, un exposé de l’évolution des systèmes d’aide à la conduite et de leur apport en matière de sécurité routière est donné. Enfin, après avoir introduit les cinq niveaux d’automatisation de la conduite, l’architecture hiérarchisée de commande d’un véhicule totalement autonome est présentée. Cette description s’accompagne d’une discussion sur les risques technologiques potentiels associés et sur les moyens scientifiques et techniques mis en œuvre afin de les encadrer

Coupled detection, association and tracking for Traffic Sign Recognition

International audience— This paper tackles the problem of tracking-based Traffic Sign Recognition (TSR) systems. It presents an integrated object detection, association and tracking approach based on a spatio-temporal data fusion. This algorithm tracks detected sign candidates in order to reduce false positives. Regions Of Interest (ROIs) potentially containing traffic signs are determined from the vehicle-mounted camera images. An original corner detector associated to pixel coding ensures the detection efficiency. The ROIs are combined using the Transfer-able Belief Model semantics. The associations maximizing the pairwise belief between the detected ROIs and ROIs tracked by multiple Kalman filters are processed. The track evolution helps to detect false positives. Thanks to this solution and to a feedback loop between the tracking algorithm and the ROI detector, a false positive reduction of 45% is assessed

Evaluation of attitude estimation algorithms using absolute magnetic reference data: Methodology and results

International audience—Location is a growing problem due, for instance, to the expansion of civil drones or intelligent vehicles. This expansion was made possible through the development of microelec-tromecanical systems (MEMS), cheap and miniaturized inertial sensors. In this context, this article is devoted to the development of a simulator which generates the sensor measurements, giving a specific trajectory, in order to validate and compare pose estimation algorithms. After validation of the simulator with real movements and measurements, four pose estimation algorithms from the literature are compared on different trajectories. All these algorithms use magnetic field sensors in addition of the classical inertial sensors. This comparison is led to select an algorithm for a future application with multiple IMUs