PRE-CRASH EXTRACTION OF THE CONSTELLATION OF A FRONTAL COLLISION BETWEEN TWO MOTOR VEHICLES

One of the strategic objectives of the European Commission is to halve the number of road traffic fatalities by 2020. In addition, in 2010, the United Nations General Assembly initiated the "Decade of Action for Road Safety 2011-2020" to reduce the number of fatalities and decrease the number of road traffic injuries. To address the scourge of road traffic accidents, this thesis presents a research study which has devised and evaluated a novel algorithm for extracting the constellation of an unavoidable frontal vehicle-to-vehicle accident. The primary research questions addressed in this work are: • What are the most significant collision parameters which influence the injury severity for a frontal collision between two motor vehicles? • How to extract the constellation of a crash before the accident occurs? In addition, the secondary research questions given below were addressed: • How to integrate physical constraints, imposed on the rate of acceleration of a real vehicle, together with data from vehicle-to-vehicle (V2V) communication, into the crash constellation extraction algorithm? • How to integrate uncertainties, associated with the data captured by sensors of a real vehicle, into a simulation model devised for assessing the performance of crash constellation extraction algorithms? Statistical analysis, conducted to determine significant collision parameters, has identified three significant crash constellation parameters: the point of collision on the vehicle body and the relative velocity between the vehicles; and the vehicle alignment offset (or vehicle overlap). The research reported in this thesis has also produced a novel algorithm for analysing the data captured by vehicle sensors, to extract the constellation of an unavoidable vehicle-to-vehicle frontal accident. The algorithm includes a model of physical constraints on the acceleration of a vehicle, cast as a gradual rise and eventual saturation of vehicle acceleration, together with the acceleration lag relative to the timing of information received from V2V communication. In addition, the research has delivered a simulation model to support the evaluation of the performance of crash constellation extraction algorithms, including a technique for integrating (into the simulation model, so that the simulation can approach real-world behaviour) the uncertainties associated with the data captured by the sensors of a real vehicle. The results of the assessment of the soundness of the simulation model show that the model produces the expected level of estimation errors, when simulation data is considered on its own or when it is compared to data from tests performed with a real vehicle. Simulation experiments, for the performance evaluation of the crash constellation extraction algorithm, show that the uncertainty associated with the estimated time-to-collision decreases as vehicle velocity increases or as the actual time-to-collision decreases. The results also show that a decreasing time-to-collision leads to a decreasing uncertainty associated with the estimated position of the tracked vehicle, the estimated collision point on the ego vehicle, and the estimated relative velocity between the two vehicles about to collide. The results of the performance assessment of the crash constellation extraction algorithm also show that V2V information has a beneficial influence on the precision of the constellation extraction, with regards to the predicted time-to-collision, the predicted position and velocity of the oncoming vehicle against which a collision is possible; the predicted relative velocity between the two vehicles about to collide, and the predicted point of collision on the body of the ego vehicle. It is envisaged that the techniques, developed in the research reported in this thesis, will be used in future integrated safety systems for motor vehicles. They could then strongly impact passenger safety by enabling optimal activation of safety measures to protect the vehicle occupants, as determined from the estimated constellation of the impending crash

Distributed pedestrian detection alerts based on data fusion with accurate localization

Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and trustable tracking of any pedestrian movement. The application is complemented by an efficient communication protocol, able to alert vehicles in the surroundings by a fast and reliable communication. The combination of a powerful location, based on a GPS with inertial measurement, and accurate obstacle localization based on data fusion has allowed locating the detected pedestrians with high accuracy. Tests proved the viability of the detection system and the efficiency of the communication, even at long distances. By the use of the alert communication, dangerous situations such as occlusions or misdetections can be avoided.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01, GRANT TRA2010-20225-C03-03, GRANT TRA 2011-29454-C03-02 and iVANET TRA2010-15645) and CAM through SEGVAUTO-II (S2009/DPI-1509)

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Relative Positioning for Vehicular Environments

Fahrerassistenzsysteme sind ein wesentlicher Baustein zur Steigerung der Sicherheit im Straßenverkehr. Vor allem sicherheitsrelevante Applikationen benötigen eine genaue Information über den Ort und der Geschwindigkeit der Fahrzeuge in der unmittelbaren Umgebung, um mögliche Gefahrensituationen vorherzusehen, den Fahrer zu warnen oder eigenständig einzugreifen. Repräsentative Beispiele für Assistenzsysteme, die auf eine genaue, kontinuierliche und zuverlässige Relativpositionierung anderer Verkehrsteilnehmer angewiesen sind, sind Notbremsassitenten, Spurwechselassitenten und Abstandsregeltempomate. Moderne Lösungsansätze benutzen Umfeldsensorik wie zum Beispiel Radar, Laser Scanner oder Kameras, um die Position benachbarter Fahrzeuge zu schätzen. Dieser Sensorsysteme gemeinsame Nachteile sind deren limitierte Erfassungsreichweite und die Notwendigkeit einer direkten und nicht blockierten Sichtlinie zum Nachbarfahrzeug. Kooperative Lösungen basierend auf einer Fahrzeug-zu-Fahrzeug Kommunikation können die eigene Wahrnehmungsreichweite erhöhen, in dem Positionsinformationen zwischen den Verkehrsteilnehmern ausgetauscht werden. In dieser Dissertation soll die Möglichkeit der kooperativen Relativpositionierung von Straßenfahrzeugen mittels Fahrzeug-zu-Fahrzeug Kommunikation auf ihre Genauigkeit, Kontinuität und Robustheit untersucht werden. Anstatt die in jedem Fahrzeug unabhängig ermittelte Position zu übertragen, werden in einem neuartigem Ansatz GNSS-Rohdaten, wie Pseudoranges und Doppler-Messungen, ausgetauscht. Dies hat den Vorteil, dass sich korrelierte Fehler in beiden Fahrzeugen potentiell herauskürzen. Dies wird in dieser Dissertation mathematisch untersucht, simulativ modelliert und experimentell verifiziert. Um die Zuverlässigkeit und Kontinuität auch in "gestörten" Umgebungen zu erhöhen, werden in einem Bayesischen Filter die GNSS-Rohdaten mit Inertialsensormessungen aus zwei Fahrzeugen fusioniert. Die Validierung des Sensorfusionsansatzes wurde im Rahmen dieser Dissertation in einem Verkehrs- sowie in einem GNSS-Simulator durchgeführt. Zur experimentellen Untersuchung wurden zwei Testfahrzeuge mit den verschiedenen Sensoren ausgestattet und Messungen in diversen Umgebungen gefahren. In dieser Arbeit wird gezeigt, dass auf Autobahnen, die Relativposition eines anderen Fahrzeugs mit einer Genauigkeit von unter einem Meter kontinuierlich geschätzt werden kann. Eine hohe Zuverlässigkeit in der longitudinalen und lateralen Richtung können erzielt werden und das System erweist 90% der Zeit eine Unsicherheit unter 2.5m. In ländlichen Umgebungen wächst die Unsicherheit in der relativen Position. Mit Hilfe der on-board Sensoren können Fehler bei der Fahrt durch Wälder und Dörfer korrekt gestützt werden. In städtischen Umgebungen werden die Limitierungen des Systems deutlich. Durch die erschwerte Schätzung der Fahrtrichtung des Ego-Fahrzeugs ist vor Allem die longitudinale Komponente der Relativen Position in städtischen Umgebungen stark verfälscht.Advanced driver assistance systems play an important role in increasing the safety on today's roads. The knowledge about the other vehicles' positions is a fundamental prerequisite for numerous safety critical applications, making it possible to foresee critical situations, warn the driver or autonomously intervene. Forward collision avoidance systems, lane change assistants or adaptive cruise control are examples of safety relevant applications that require an accurate, continuous and reliable relative position of surrounding vehicles. Currently, the positions of surrounding vehicles is estimated by measuring the distance with e.g. radar, laser scanners or camera systems. However, all these techniques have limitations in their perception range, as all of them can only detect objects in their line-of-sight. The limited perception range of today's vehicles can be extended in future by using cooperative approaches based on Vehicle-to-Vehicle (V2V) communication. In this thesis, the capabilities of cooperative relative positioning for vehicles will be assessed in terms of its accuracy, continuity and reliability. A novel approach where Global Navigation Satellite System (GNSS) raw data is exchanged between the vehicles is presented. Vehicles use GNSS pseudorange and Doppler measurements from surrounding vehicles to estimate the relative positioning vector in a cooperative way. In this thesis, this approach is shown to outperform the absolute position subtraction as it is able to effectively cancel out common errors to both GNSS receivers. This is modeled theoretically and demonstrated empirically using simulated signals from a GNSS constellation simulator. In order to cope with GNSS outages and to have a sufficiently good relative position estimate even in strong multipath environments, a sensor fusion approach is proposed. In addition to the GNSS raw data, inertial measurements from speedometers, accelerometers and turn rate sensors from each vehicle are exchanged over V2V communication links. A Bayesian approach is applied to consider the uncertainties inherently to each of the information sources. In a dynamic Bayesian network, the temporal relationship of the relative position estimate is predicted by using relative vehicle movement models. Also real world measurements in highway, rural and urban scenarios are performed in the scope of this work to demonstrate the performance of the cooperative relative positioning approach based on sensor fusion. The results show that the relative position of another vehicle towards the ego vehicle can be estimated with sub-meter accuracy in highway scenarios. Here, good reliability and 90% availability with an uncertainty of less than 2.5m is achieved. In rural environments, drives through forests and towns are correctly bridged with the support of on-board sensors. In an urban environment, the difficult estimation of the ego vehicle heading has a mayor impact in the relative position estimate, yielding large errors in its longitudinal component

Pedestrian Protection Using the Integration of V2V Communication and Pedestrian Automatic Emergency Braking System

Indiana University-Purdue University Indianapolis (IUPUI)The Pedestrian Automatic Emergency Braking System (PAEB) can utilize on-board sensors to detect pedestrians and take safety related actions. However, PAEB system only benefits the individual vehicle and the pedestrians detected by its PAEB. Additionally, due to the range limitations of PAEB sensors and speed limitations of sensory data processing, PAEB system often cannot detect or do not have sufficient time to respond to a potential crash with pedestrians. For further improving pedestrian safety, we proposed the idea for integrating the complimentary capabilities of V2V and PAEB (V2V-PAEB), which allows the vehicles to share the information of pedestrians detected by PAEB system in the V2V network. So a V2V-PAEB enabled vehicle uses not only its on-board sensors of the PAEB system, but also the received V2V messages from other vehicles to detect potential collisions with pedestrians and make better safety related decisions. In this thesis, we discussed the architecture and the information processing stages of the V2V-PAEB system. In addition, a comprehensive Matlab/Simulink based simulation model of the V2V-PAEB system is also developed in PreScan simulation environment. The simulation result shows that this simulation model works properly and the V2V-PAEB system can improve pedestrian safety significantly

Controller with Vehicular Communication Design for Vehicular Platoon System

PhD ThesisTracked Electric Vehicles (TEV) which is a new mass-transport system. It aims to provide a safe, efficient and coordinated traffic system. In TEV, the inter-vehicular distance is reduced to only a quarter of the regular car length and where drive at 200km/h enabling mass transport at uniform speed. Under this requirement, the design of the controller is particularly important. This thesis first developed an innovative approach using adaptive Proportion, integral and derivation (PID) controller using fuzzy logic theory to keep variable time-gap between dynamic cars for platooning system with communication delay. The simulation results presented show a significant improvement in keeping time-gap variable between the cars enabling a safe and efficient flow of the platooning system. Secondly, this thesis investigates the use of Slide Mode Control (SMC) for TEV. It studies different V2V communication topology structures using graph theory and proposes a novel SMC design with and without global dynamic information. The Lyapunov candidate function was chosen to study the impact which forms an integral part for current and future research. The simulation results show that this novel SMC has a tolerance ability for communication delay. In order to present the real time TEV platoon system, a similar PI controller has been utilized in a novel automated vehicle, based on Raspberry Pi, multi-sensors and the designed Remote Control (RC) car. Thirdly, in order to obtain precise positioning information for vehicles in platoon system, this thesis describes Inertial Measurement Unit (IMU)/Global Navigation Satellite System (GNSS) data fusion to achieve a highly precise positioning solution. The results show that the following vehicles can reach the same velocity and acceleration as the leading vehicle in 5 seconds and the spacing error is less than 0.1m. The practical results are in line with those from the simulated experiment

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

FRIEND: A Cyber-Physical System for Traffic Flow Related Information Aggregation and Dissemination

The major contribution of this thesis is to lay the theoretical foundations of FRIEND — A cyber-physical system for traffic Flow-Related Information aggrEgatioN and Dissemination. By integrating resources and capabilities at the nexus between the cyber and physical worlds, FRIEND will contribute to aggregating traffic flow data collected by the huge fleet of vehicles on our roads into a comprehensive, near real-time synopsis of traffic flow conditions. We anticipate providing drivers with a meaningful, color-coded, at-a-glance view of flow conditions ahead, alerting them to congested traffic. FRIEND can be used to provide accurate information about traffic flow and can be used to propagate this information. The workhorse of FRIEND is the ubiquitous lane delimiters (a.k.a. cat\u27s eyes) on our roadways that, at the moment, are used simply as dumb reflectors. Our main vision is that by endowing cat\u27s eyes with a modest power source, detection and communication capabilities they will play an important role in collecting, aggregating and disseminating traffic flow conditions to the driving public. We envision the cat\u27s eyes system to be supplemented by road-side units (RSU) deployed at regular intervals (e.g. every kilometer or so). The RSUs placed on opposite sides of the roadway constitute a logical unit and are connected by optical fiber under the median. Unlike inductive loop detectors, adjacent RSUs along the roadway are not connected with each other, thus avoiding the huge cost of optical fiber. Each RSU contains a GPS device (for time synchronization), an active Radio Frequency Identification (RFID) tag for communication with passing cars, a radio transceiver for RSU to RSU communication and a laptop-class computing device. The physical components of FRIEND collect traffic flow-related data from passing vehicles. The collected data is used by FRIEND\u27s inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic flow-related information along the roadway. The second contribution of this thesis is the development of an incident classification and detection algorithm that can be used to classify different types of traffic incident Then, it can notify the necessary target of the incident. We also compare our incident detection technique with other VANET techniques. Our third contribution is a novel strategy for information dissemination on highways. First, we aim to prevent secondary accidents. Second, we notify drivers far away from the accident of an expected delay that gives them the option to continue or exit before reaching the incident location. A new mechanism tracks the source of the incident while notifying drivers away from the accident. The more time the incident stays, the further the information needs to be propagated. Furthermore, the denser the traffic, the faster it will backup. In high density highways, an incident may form a backup of vehicles faster than low density highways. In order to satisfy this point, we need to propagate information as a function of density and time

Communications par Lumière Visible et Radio pour la Conduite Coopéraive Autonome: application à la conduite en convois.

By realizing both low-cost implementationand dual functionality, VLC has becomean outstanding intriguing supportivetechnology by using the vehicular existedinfrastructure.This thesis aims to contribute to theautonomous vehicular communicationand urban mobility improvements. Thework addresses the main radio-basedV2V communication limitations and challengesfor ITS hard-safety applicationsand intends to deploy the vehicular lightingsystem as a supportive communicationsolution for platooning of IVCenabledautonomous vehicles. The ultimateobjectives of this Ph.D. researchare to integrate the VLC system withinthe existing C-ITS architecture by developinga VLC prototype, together withsufficient hand-over algorithms enablingVLC, RF, and perception-based solutionsin order to ensure the maximumsafety requirements and the continuousinformation exchange between vehicles.The feasibility and efficiency of thesystem implementation and hand-overalgorithms were subjects to deep investigationsusing computer simulators andtest-bed that considers applications ofautomated driving. In addition to the improvementin road capacity when platoonformations are used. The carried outsimulations followed-up by experimentalresults proved that the integration of VLCwith the existed RF solutions lead to adefinite benefit in the communicationchannel quality and safety requirementsof a platooning system when a properhand-over algorithm is used.La communication par lumière visibleVLC est devenue une technologie attractivevu qu’elle assure une implémentationà faible coût et une doublefonctionnalité. En effet, elle permetd’utiliser l’infrastructure déjà existantesur le véhicule à savoir les lampesd’arrière et frontales comme des unitésde transmission. Cette thèse s’intéresseà rendre plus efficace les communicationsdes véhicules autonomes ainsi quela gestion de la mobilité urbaine. Nousnous intéressons tout d’abord aux principaleslimitations des communicationsradio sans fil dans le contexte des applicationsde sécurité routière à hautes exigences.Nous nous concentrons ensuiteau déploiement d’un système d’éclairagesur les véhicules dans le but de fournir unmoyen de communication de soutien auxcommunications radio pour l’applicationde peloton. L’objectif primordial decette thèse est d’intégrer la technologieVLC dans l’architecture de communicationITS en implémentant un prototypede communication VLC et en concevantde nouveaux algorithmes de handoverpermettant une transition transparenteentre différents moyens de communicationinter-véhiculaires (VLC, communicationsans fil et techniques de perception).Le but est d’assurer les exigencesde sécurité requises par les applicationset l’échange continue de l’informationentre véhicules. L’efficacité de ces algorithmesa été validée à travers de nombreusessimulations et test-bed réels aucours desquels nous avons considérél’application de conduite automatisée.Ces différentes méthodes de validationont démontré que l’intégration de la technologieVLC avec les solutions de communicationsradio permet d’améliorer laqualité du canal de transmission ainsique la satisfaction des exigences de sécuritérelatives à l’application de peloton

Control and Perception for Autonomous Driving

Autonomous driving requires perception and control. The first part of the dissertation is focused on an aspect of control related to automatic vehicle following that is not well understood, namely, the influence of imperfect wireless connectivity in vehicle platooning applications. The primary goal of most research in vehicle platooning is to enable the shortest inter vehicular spacing while maintaining safety, since short following distances are known to improve fuel efficiency and traffic mobility. It is also known that wireless connectivity can be exploited to achieve tighter platoon formations, but the effect of imperfections of wireless links on platoon stability were not well understood. This work proposes an algorithm to estimate the smallest time headway that guarantees safety based on the average packet reception rate. The algorithm has been corroborated using Model in Loop (MIL) simulations as well as test runs with a hybrid car. This thesis also develops a method to estimate the maximum perturbation in spacing error of any vehicle in a string stable platoon based on the lead vehicle's acceleration maneuver. This allows a designer to pick a safe standstill distance. The second part of this dissertation explores the challenges of environment perception and sensor fusion under adverse visibility conditions for autonomous driving. The sensor stack for autonomous vehicles usually consists of on one or more of radars, visible spectrum/RGB (Red-Green-Blue) cameras and lidars. RGB cameras perform poorly in low light conditions (at night) as well as in direct sunlight. While automotive radars are resilient to environmental conditions, they only offer a low resolution output. In this thesis, we explore the benefits of combining a Long Wavelength Infrared (LWIR) thermal camera with a radar sensor for detection and tracking of vehicles/pedestrians in poor visibility conditions. A modified Joint Probabilistic Data Association (JPDA) filter is implemented on real-world data to demonstrate the feasibility of the proposed system