Acquisition of relative vehicle trajectories to facilitate freeway merging using DSRC based V2V communication

University of Minnesota M.S.E.E. thesis.November 2017. Major: Electrical Engineering. Advisor: Imran Hayee. 1 computer file (PDF); vi, 39 pages.For the anticipated benefits of connected vehicle technology, Intelligent Transportation Systems Joint Program Office (ITSJPO) of the US Department of Transportation continues to emphasize the need for having Dedicated Short Range Communication (DSRC) based vehicle to vehicle (V2V) and/or vehicle to infrastructure (V2I) communication to enhance driver safety and traffic mobility. To take full advantage of connected vehicle technology in most safety applications, precise vehicle positioning information is neeeded in addition to V2V communication. Although, there are many techniques including vision or sensor based systems and differential GPS receivers, which can obtain precise absolute position of a vehicle at the expense of cost and complexity, some critical safety applications such as merge assist or lane change assist systems require only relative positions of surrounding vehicles with lane level resolution so a given vehicle can differentiate the vehicles on its own lane from the vehicles on adjacent lanes. We have adopted a simple approach to acquire accurate relative trajectories of surrounding vehicles using standard GPS receviers and DSRC based V2V communication. Using this approach, we have conducted field tests to successfully acquire relative trajectories of vehicles travelling on multiple lanes towards a merging junction with an accuracy of ±0.5m. The achieved accuracy level in relative trajectory was sufficient to differentiate vehicles travelling on adjacent lanes of a multiple-lane freeway

Grilles de perception évidentielles pour la navigation robotique en milieu urbain

Les travaux de recherche présentés dans cette thèse portent sur le problème de la perception de l environnement en milieu urbain, complexe et dynamique et ce en présence de mesures extéroceptives bruitées et incomplètes obtenues à partir decapteurs embarqués. Le problème est formalisé sous l angle de la fusion de données capteurs à l aide d une représentation spatiale de l environnement. Ces travaux ont été réalisés pour la navigation autonome de véhicules intelligents dans le cadre du projet national ANR CityVIP. Après avoir considéré les principaux formalismes de modélisation de l incertitude, un système de fusion de grilles spatio-référencées gérant l incertitude avec des fonctions de croyances est étudié. Ce système est notamment capable de fusionner les mesures d un lidar multi-nappes et multi-échos, obtenues à différents instants pour construire une carte locale dynamique sous la forme discrète d une grille d occupation évidentielle.Le principal avantage des fonctions de croyance est de représenter de manière explicite l ignorance et ne nécessite donc pas d introduire d information à priori non fondée. De plus, ce formalisme permet d utiliser facilement l information conflictuelle pour déterminer la dynamique de la scène comme par exemple les cellules en mouvement. Le formalisme de grilles d occupation évidentielles est présenté en détails et un modèle de capteur lidar multi-nappes et multi-echos est ensuite proposé. Deux approches de fusion séquentielle multi-grilles sont étudiées selon les paradigmes halocentréet égo-centré. Enfin, l implémentation et les tests expérimentaux des approches sont décrits et l injection d informations géographiques connues a priori est étudiée. La plupart des travaux présentés ont été implémentés en temps réel sur un véhicule du laboratoire et de nombreux tests en conditions réelles ont été réalisés avec une interface d analyse de résultat utilisant une rétro-projection dans une image grand angle. Les résultats ont été présentés dans 5 conférences internationales [Moras et al., 2010, Moras et al., 2011a, Moras et al., 2011b, Moras et al., 2012, Kurdej et al., 2012] etle système expérimental a servi à la réalisation de démonstrations officielles dans le cadre du projet CityVIP à Paris et lors de la conférence IEEE Intelligent Vehicles Symposium 2011 en Allemagne.The research presented in this thesis focuses on the problem of the perception of the urban environment which is complex and dynamic in the presence of noisy and incomplete exteroceptive measurements obtained from on-board sensors. The problem is formalized in terms of sensor data fusion with a spatial representation of the environment. This work has been carried out for the autonomous navigation of intelligent vehicles within the national project ANR CityVIP. After having considered various formalisms to represent uncertainty, a fusion of spatio-referenced grids managing uncertainty with belief functions is studied. This system is capable of merging multi-layers and multi-echoes lidar measurements, obtainedat different time indexes to build a dynamic local map as a discrete evidential occupancy grid. The main advantages of belief functions are, firstly, to represent explicitly ignorance, which reduces the assumptions and therefore avoid introducing wrong a priori information and, secondly, to easily use conflicting information to determine the dynamics of the scene such as movements of the cells. The formalism of evidential occupancy grids is then presented in details and two multi-layers and multi-echos lidar sensor models are proposed. The propagation of the information through geometrical transformations is formalized in a similar way of image transformation framework. Then, the implementation of the approach is described and the injection of prior geographic information is finally investigated. Most of the works presented have been implemented in real time on a vehicle and many tests in real conditions have been realized. The results of these researches were presented through five international conferences [Moras et al., 2010, Moras et al., 2011a, Moras et al., 2011b, Moras et al., 2012], [Kurdej et al., 2012] and the experimental vehicle was presented at the official demonstration project CityVIP in Paris and at the IEEE Intelligent Vehicles Symposium 2011, in Germany.COMPIEGNE-BU (601592101) / SudocSudocFranceF

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor