178 research outputs found

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

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    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

    Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

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    Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017

    Cooperative Relative Positioning for Vehicular Environments

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    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

    Cooperative Vehicle Tracking in Large Environments

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    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 Vehicle Tracking in Large Environments

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    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

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    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

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

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    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

    Collaboratively Navigating Autonomous Systems

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    The objective of this project is to focus on technologies for enabling heterogeneous networks of autonomous vehicles to cooperate together on a specific task. The prototyped test bed consists of a retrofitted electric golf cart and a quadrotor designed to perform distributed information gathering to guide decision making across the entire test bed. The system prototype demonstrates several aspects of this technology and lays the groundwork for future projects in this area

    Fusion of Perception and V2P Communication Systems for Safety of Vulnerable Road Users

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    International audienceWith cooperative intelligent transportation systems (C-ITS), vulnerable road users (VRU) safety can be enhanced by multiple means.On one hand, perception systems are based on embedded sensors to protect VRUs. However, such systems may fail due to the sensors' visibility conditions and imprecision. On the other hand, Vehicle-to-Pedestrian (V2P) communication can contribute to the VRU safety by allowing vehicles and pedestrians to exchange information. This solution is, however, largely affected by the reliability of the exchanged information, which most generally is the GPS data. Since perception and communication have complementary features, we can expect that a fusion between these two approaches can be a solution to the VRU safety. In this work, we propose a cooperative system that combines the outputs of communication and perception. After introducing theoretical models of both individual approaches, we develop a probabilistic association between perception and V2P communication information by means of multi-hypothesis tracking (MHT). Experimental studies are conducted to demonstrate the applicability of this approach in real-world environments. Our results show that the cooperative VRU protection system can benefit of the redundancy coming from the perception and communication technologies both in line-of-sight (LOS) and non-LOS (NLOS) conditions. We establish that the performances of this system are influenced by the classification performances of the perception system and by the accuracy of the GPS positioning transmitted by the communication system

    Automotive applications of high precision GNSS

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    This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas
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