Development and Simulation-based Testing of a 5G-Connected Intersection AEB System

In Europe, 20% of road crashes occur at intersections. In recent years, evolving communication technologies are making V2V and V2I faster and more reliable; with such advancements, these crashes, as well as their economic cost, can be partially reduced. In this work, we concentrate on straight path intersection collisions. Connectivity-based algorithms relying on 5G technology and smart sensors are presented and compared to a commercial radar AEB logic in order to evaluate performances and effectiveness in collision avoidance or mitigation. The aforementioned novel safety systems are tested in a blind intersection and low adherence scenario. The first algorithm proposed is obtained by incorporating connectivity information to the original control scheme, while the second algorithm proposed is a novel control logic fully capable of utilizing also adherence estimation provided by smart sensors. Test results show an improvement in terms of safety for both the architecture and high prospects for future developments

Probabilistic Framework for Behavior Characterization of Traffic Participants Enabling Long Term Prediction

This research aims at developing new methods that predict the behaviors of the human driven traffic participants to enable safe operation of autonomous vehicles in complex traffic environments. Autonomous vehicles are expected to operate amongst human driven conventional vehicles in the traffic at least for the next few decades. For safe navigation they will need to infer the intents as well as the behaviors of the human traffic participants using extrinsically observable information, so that their trajectories can be predicted for a time horizon long enough to do a predictive risk analysis and gracefully avert any risky situation. This research approaches this challenge by recognizing that any maneuver performed by a human driver can be divided into four stages that depend on the surrounding context: intent determination, maneuver preparation, gap acceptance and maneuver execution. It builds on the hypothesis that for a given driver, the behavior not only spans across these four maneuver stages, but across multiple maneuvers. As a result, identifying the driver behavior in any of these stages can help characterize the nature of all the subsequent maneuvers that the driver is likely to perform, thus resulting in a more accurate prediction for a longer time horizon. To enable this, a novel probabilistic framework is proposed that couples the different maneuver stages of the observed traffic participant together and associates them to a driving style. To realize this framework two candidate Multiple Model Adaptive Estimation approaches were compared: Autonomous Multiple Model (AMM) and Interacting Multiple Model(IMM) filtering approach. The IMM approach proved superior to the AMM approach and was eventually validated using a trajectory extracted from a real world dataset for efficacy. The proposed framework was then implemented by extending the validated IMM approach with contextual information of the observed traffic participant. The classification of the driving style of the traffic participant (behavior characterization) was then demonstrated for two use case scenarios. The proposed contextual IMM (CIMM) framework also showed improvements in the performance of the behavior classification of the traffic participants compared to the IMM for the identified use case scenarios. This outcome warrants further exploration of this framework for different traffic scenarios. Further, it contributes towards the ongoing endeavors for safe deployment of autonomous vehicles on public roads

Planificació de trajectòries per esquivar obstacles

Aquest treball s’emmarca dins el projecte Elektra, que tracta sobre el desenvolupament d’un vehicle elèctric autònom completament automatitzat. Aquest vehicle està ubicat al Centre de Visió per Computador (CVC) i hi col·labora el centre de recerca CS2AC-UPC. El treball realitzat s’ha centrat en un estudi teòric i simulat de les accions que ha de fer el vehicle per evitar col·lisions. El comandament d’aquests vehicles es realitza mitjançant diferents llaços de control. El control més bàsic (baix nivell) és el seguiment d’una trajectòria definida mitjançant l’angle d’orientació rodes del vehicle i la velocitat de circulació. En un nivell superior, es troba el planificador de trajectòries, aquest consta de dos subnivells: el nivell que anomenem global i el local. El nivell global consisteix en definir una sèrie de punts (‘waypoints’) o coordenades que marquen el trajecte a realitzar. El nivell local té per objectiu definir una trajectòria realitzable entre els punts del nivell global. Aqueta trajectòria s’haurà de modificar en el cas de que un obstacle impedeixi el pas del vehicle. Aquest treball final de grau s’ubica en aquest últim cas, es tracta de cercar algoritmes, que a partir de la informació del vehicle i de l’entorn, com pot ser la distància i la mida d’un obstacle, es calculi una trajectòria alternativa per evitar la col·lisió o superar l’obstacle, i, un cop superat, tornar a la trajectòria global. Per poder validar l’algoritme desenvolupat es disposa de dos entorns de simulació. Un és el Matlab/Simulink que s’ha utilitzat per desenvolupar el codi, i l’altre és un entorn de simulació en 3D anomenat Unity, que permet, a partir d’una programació preestablerta, afegir el codi i els algoritmes necessaris per simular el comportament de vehicle en presència d’obstacles. Un cop simulat i comprovat el correcte funcionament, un equip del Centre de Visió per Computador adaptarà el codi per poder-lo introduir al vehicle i veure com reacciona al espai real

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account

Cross-traffic assistance considering uncertainties in measurements and prediction

Obwohl die Statistik aufzeigt, dass sich an Kreuzungen gehäuft Unfälle ereignen, wird der Fahrer in dieser offensichtlich komplexen Situation bis dato nur in wenigen Serienfahrzeugen von Assistenzsystemen unterstützt. Eine Ursache hierfür ist die eingeschränkte Einsicht konventioneller On-Board Sensorik wie Radar und Kamera in den seitlichen Kreuzungsarm, wodurch potentiell vorhandener Querverkehr erst spät erfasst werden kann. Die Kommunikation zwischen Fahrzeugen stellt einen vielversprechenden technologischen Fortschritt zur Beherrschung kritischer Situationen im Kreuzungsbereich dar. Bei der Anwendung dieser Technologie entstehen eine Reihe von neuen Fragestellungen: Reicht die Genauigkeit der satellitengestützten Lokalisierung aus, um dem Fahrer eine möglichst falschwarnungsfreie, aber dennoch wirksame Assistenz anbieten zu können? Wie kann bei der Bewertung der Kritikalität mit den variierenden Unsicherheiten aus der Lokalisierung umgegangen werden? Welchen Einfluss nehmen die Unsicherheiten, die aus dem Fahrerverhalten resultieren? Um diese Fragen zu beantworten, wird in dieser Arbeit ein prototypisches Assistenzsystem entwickelt und im Rahmen eines Feldversuchs mit Probanden getestet. Die Messdaten dienen einerseits dazu, durch Expertenanalyse und Fahrerbefragung Optimierungspotential des bestehenden Systems zu identifizieren und liefern andererseits eine umfangreiche Datenbasis für die Evaluierung von neuen Ansätzen. Dies stellt die Grundlage dar für die im weiteren Verlauf entwickelten Methoden zur Handhabung von Unsicherheiten aus Fahrerverhalten und Sensorik. Die Abbiegeabsicht wird mit einem neuartigen Ansatz bewertet, der aus Sicht des Fahrers mehrere mögliche Manöver plant. Anhand der zeitlichen Entwicklung der optimalen Überführungskosten der geplanten Trajektorien wird auf die Abbiegeabsicht des Fahrers geschlossen. Das Verfahren wird anhand zahlreicher realer Abbiegemanöver evaluiert. Es kann gezeigt werden, dass eine verlässliche Detektion bereits zu einem Zeitpunkt möglich ist, welcher dem Fahrer eine hinreichend große Reaktionszeitreserve ermöglicht, um eine kritische Situation selbst zu entschärfen. Der Umgang mit den Unsicherheiten aus der Sensorik gelingt, indem zunächst zeitbasierte Kriterien zur Bewertung der Kritikalität im deterministischen Fall untersucht werden. Anschließend wird mit Hilfe der Methode der exakten Monome und mehrdimensionaler Gauß-Quadratur eine recheneffiziente Approximation für den probabilistischen Fall vorgeschlagen.Traversing an intersection is a challenging task for human drivers. Vehicle accident statistics, which provide evidence of this adverse circumstance, indicate an increased frequency of accidents. However, advanced driver assistance systems that provide assistance during intersection situations are not available in all series production cars. Among other reasons this is due to the reduced detection range of conventional sensors, such as radar or cameras for vehicles on lateral crossroads. Cross-traffic assistance based on vehicle-to-vehicle communication technology exhibits promising attributes for the control of this type of situation because crossing traffic can be detected even without a line-of-sight. However, the application of this technology introduces the following new issues: Is the precision of satellite based positioning sufficiently accurate to provide effective assistance to a driver while maintaining a low false-positive warning rate? What is the best approach to coping with the varying uncertainty of localization measurements during criticality assessment? How is the uncertainty about the intention of the driver related to this issue? To answer these questions, a prototype system is developed and extensively tested during a field-operational test using naive probands. The acquired data enables the optimization of the current system via analysis by experts and driver surveys. The data also serves as an extensive data base for the evaluation of the new algorithms developed in this thesis, which focus both on the uncertainty in the driver’s behavior and in measurement. Turning maneuver intention is estimated by a novel approach, in which several possible maneuvers are planned from the viewpoint of the driver. To infer the intended maneuver, the gradient of the optimal cost-to-go of each planned trajectory is employed. This approach is evaluated with numerous turning maneuvers and enables early and reliable detection of the actual conducted maneuver, which facilitates an effective warning. The ability to handle measurement uncertainty is addressed by examining time-based criticality measures for the deterministic case. Subsequently, an efficient approximation for the probabilistic case, which is based on a method of exact monomials and multidimensional Gaussian quadrature, is proposed

Calcul de trajectoires pour la préconisation de manoeuvres automobiles sur la base d'une perception multi-capteur (application à l'évitement de collision)

Les systèmes d aide à la conduite, en général, et plus particulièrement les systèmes d aide à l évitement de collision sont de plus en plus en présents dans les véhicules car ils ont un très fort potentiel de réduction du nombre d accidents de la circulation.En effet, ces systèmes ont pour rôle d assister le conducteur, voire de se substituer à lui lorsque la situation de conduite indique un risque de collision important. Cette thèse traite du développement de ces systèmes en abordant quelques problématiques rencontrées.Afin de réagir convenablement, le système a d abord besoin d une représentation aussi fidèle que possible de l environnement du véhicule. La perception est faite au moyen de capteurs extéroceptifs qui permettent de détecter les objets et d en mesurer divers paramètres selon leur principe de mesure. La fusion des données individuelles des capteurs permet d obtenir une information globale plus juste, plus certaine et plus variée. Ce travail traite en profondeur des méthodes de suivi d objets par fusion de données multi-capteur, multimodale au niveau piste. Les approches proposées ont été évaluées puis approuvées grâce à des données de roulage réel et sur des données de conduite simulées.Il est ensuite nécessaire de faire une analyse de la scène perçue au cours du temps afin d évaluer le risque de collision encouru par le véhicule porteur du système. Cette thèse propose des méthodes de prédiction de trajectoire et de calcul de probabilité de collision, à divers horizons temporels afin de quantifier le risque de collision et d établir ainsi divers niveaux d alerte au conducteur. Un simulateur de scénarios automobiles a été utilisé pour valider la cohérence des méthodes d analyse de scène.Enfin, lorsque le risque de collision atteint un seuil jugé critique, le système doit calculer une trajectoire d évitement de collision qui sera ensuite automatiquement exécutée. Les principales approches de planification de trajectoires ont été revues et un choix a été fait et motivé en accord avec le contexte de système d aide à la conduite.Driver assistant systems in general, and specially collision avoidance systems are more and more installed in recent vehicles because of their high potential in reducing the number road accidents. Indeed, those systems are designed to assist the driver or even to take its place when the risk of collision is very important. This thesis deals with the main challenges in the development of collision avoidance systems. In order to react in a convenient way, the system must, first, build a faithful representation of the environment of the ego-vehicle. Perception is made by means of exteroceptive sensors that detect objects and measure different parameters, depending on their measurement principle. The fusion of individual sensor data allows obtaining a global knowledge that is more accurate, more certain and more varied. This research work makes a deep exploration of high level multisensor, multimodal, multitarget tracking methods. The proposed approaches are evaluated and validated on real driving data and also on simulated scenarios. Then, the observed scene is continuously analyzed in order to evaluate the risk of collision on the ego-vehicle. The thesis proposes methods of vehicle trajectory prediction and methods to calculate the probability of collision at different prediction times. This allows defining different levels of alert to the driver. an automotive scenarion simulator is used to test and validate the proposed scene analysis approaches. Finally, when the risk of collision reaches a defined critical value, the system must compute a collision avoidance trajectory that will be automatically followed. The main approaches of trajectory planning have been revisited et one has chosen according to the context of driver assistant system.COMPIEGNE-BU (601592101) / SudocSudocFranceF