Fuel efficiency is an ever present problem in today's modern world. The United States in particular is in need of a solution to lowering greenhouse gas emissions caused by transit and freight across its spread out cities. In fact in the United States the average commute time of an individual is 26 minutes, meaning that round trip people are driving about an hour every day, to and from work. But that gas consumption is pittiling compared to that of the freight industry. Heavy Duty Vehicles (HDVs) commonly known as semi trucks, account for three quarters of US freight emissions and 7.5% of total US greenhouse gas emissions [2]. But this can be cut down considerably with the implementation of platooning. Platooning is when multiple vehicles follow in close distance to reduce aerodynamic drag, causing significant fuel savings. In this paper, we will go over an algorithm to help vehicles join with already formed platoons on the road, increasing their fuel efficiency and therefore saving cost to the driver in addition to lessening the negative effect on the planet. This will be done using a modified A* algorithm. The base weight of zero will be the amount of gas the vehicle would consume taking the shortest path that google maps recommends, alone, with no platooning. Paths may end up with negative weights due to the fuel savings caused by joining existing platoons during the vehicles’ travel. The algorithm will have access to a map of the roadways and the GPS data of nearby platoons. It will then perform a cost-benefit analysis to determine if the fuel savings from joining a platoon will outweigh the cost of going outside of its original path in order to join the other vehicles.

Today, a rapid development towards fuel efficient technological aids for vehicles is in progress. One step towards this is the development of platooning systems. The main concept of platooning is to let several heavy duty vehicles (HDVs) drive in a convoy and share important information with each other via wireless communication. This thesis describes one out of three subsystems in a project developed to handle the process from raw sensor data to control signal. The goal of the project is to achieve a safe and smooth control with the main purpose of reduced fuel consumption. This subsystem processes the raw sensor data received from the different HDVs. The purpose is to estimate the positions and velocities of the vehicles in a platoon, taking into account that packet-loss, out of sequence measurements and irrelevant information can occur. This is achieved by filtering the information from different sensors in an Extended Kalman Filter and converting it into a local coordinate system with the origin in the ego vehicle. Moreover, the estimates are sorted and categorized into classes with respect to the status of the vehicles. The result of the thesis is useful estimates that are independent of outer effects in a local reference system with origin in the host vehicle. This information can then be used for further sensor fusion and implementation of a Model Predictive Controller (MPC) in two other subsystems. These three subsystems result in a smooth and safe control with an average reduced fuel consumption of approxi- mately 11.1% when the vehicles drive with a distance of 0.5 seconds in a simulated environment. / Dagens utveckling inom fordonsindustrin fokuserar mer och mer påutveckling av bränsleeffektiva hjälpmedel. Ett steg i denna riktning är utvecklingen av platooningsystem. Huvudkonceptet med platooning är att låta flera tunga fordon köra i följd i en konvoj och dela viktig information med varandra via trådlös kommuni- kation och en automatiserad styrstrategi. Detta examensarbete beskriver ett utav tre delsystem i ett projekt som är utvecklat för att hantera en process från rå sensordata till styrsignaler för fordonen. Målet är att uppnå en säker och mjuk reglering med huvudsyftet att reducera bränsleförbrukningen. Det här delsystemet behandlar mottagen sensordata från de olika fordonen. Målet med delsystemet är att skatta positioner och hastigheter för fordonen i konvojen med hänsyn till att förlorad, försenad eller irrelevant information från det trådlösa nätverket kan förekomma. Detta uppnås genom filtrering i ett Extended Kalman Filter och konvertering till ett lokalt referenssystem med origo i det egna fordo- net. Utöver detta sorteras informationen och kategoriseras in i olika klasser efter fordonens status. Examensarbetet resulterade i användbara skattningar oberoende av yttre om- ständigheter i ett lokalt referenssystem med origo i det egna fordonet. Denna information kan användas vidare för ytterligare sensorfusion och implementering av en modellbaserad prediktionsregulator (MPC) i två andra delsystem. De tre delsystemen resulterade i en mjuk och säker reglering och en reducerad bränsleför- brukning med i genomsnitt 11.1% då fordonen körde med 0.5 sekunders avstånd i en simulerad miljö.

This thesis is concerned with an experimental platform for studying cooperative driving and techniques for embedded systems programming. Cooperative driving systems use vehicle-to-vehicle and vehicle-to-infrastructure communication for safe, smooth and efficient transportation. Cooperative driving systems are considered as a promising solution for traffic situations such as blind crossings. For the thesis work we use a robotic vehicle known as PIE (Platform for Intelligent Embedded Systems) equipped with a wireless communication device, electrical motors and controlled via a SAM7-P256 development board. For the infrastructure side we use a SAM7-P256 development board equipped with nRF24l01. Vehicle to vehicle and base station to vehicle communication is established and different platooning scenarios are implemented. The scenarios are similar to platooning scenarios from the Grand Cooperative Driving Challenge GCDC1. The performance of the platoon control algorithm is measured in terms of throughput (a measure of string stability), smoothness and safety, where the safety requirements serve as pass/fail criteria.

<p>This thesis is concerned with an experimental platform for studying cooperative driving and techniques for embedded systems programming. Cooperative driving systems use vehicle-to-vehicle and vehicle-to-infrastructure communication for safe, smooth and efficient transportation. Cooperative driving systems are considered as a promising solution for traffic situations such as blind crossings. For the thesis work we use a robotic vehicle known as PIE (Platform for Intelligent Embedded Systems) equipped with a wireless communication device, electrical motors and controlled via a SAM7-P256 development board. For the infrastructure side we use a SAM7-P256 development board equipped with nRF24l01. Vehicle to vehicle and base station to vehicle communication is established and different platooning scenarios are implemented. The scenarios are similar to platooning scenarios from the Grand Cooperative Driving Challenge GCDC1. The performance of the platoon control algorithm is measured in terms of throughput (a measure of string stability), smoothness and safety, where the safety requirements serve as pass/fail criteria.</p>

The aim of platooning is to enable several Heavy Duty Vehicles (HDVs) to drive in a convoy and act as one unit to decrease the fuel consumption. By introducing wireless communication and tight control, the distance between the HDVs can be decreased significantly. This implies a reduction of the air drag and consequently the fuel consumption for all the HDVs in the platoon. The challenge in platooning is to keep the HDVs as close as possible to each other without endangering safety. Therefore, sensor fusion is necessary to get an accurate estimate of the relative distance and velocity, which is a pre-requisite for the controller. This master thesis aims at developing a sensor fusion framework from on-board sensor information as well as other vehicles’ sensor information communicated over a WiFi link. The most important sensors are GPS, that gives a rough position of each HDV, and radar that provides relative distance for each pair of HDV’s in the platoon. A distributed solution is developed, where an Extended Kalman Filter (EKF) estimates the state of the whole platoon. The state vector includes position, velocity and length of each HDV, which is used in a Model Predictive Control (MPC). Furthermore, a method is discussed on how to handle vehicles outside the platoon and how various road surfaces can be managed. This master thesis is a part of a project consisting of three parallel master’s theses. The other two master’s theses investigate and implement rough pre-processing of data, time synchronization and MPC associated with platooning. It was found that the three implemented systems could reduce the average fuel consumption by 11.1 %.

By using vehicle-to-vehicle communication, vehicles can cooperate in many waysby sending positions and other relevant data between each other. One popularexample is platooning where many, especially heavy vehicles, drive on a trailwith short distances resulting in a reduction of air resistance. To achieve a goodefficiency of the platooning it is required that vehicle fleets are coordinated, sothat the percentage of time for driving in platoon is maximized without affectingthe total driving time and distance too much. For large fleets, this is a complexoptimization problem which would be difficult to solve by only using the realworld as the test environment. To provide a more adaptable test environment for the communication and platooningcoordination, an augmented reality with virtual vehicles (“Ghost trucks")with relevant communication abilities are developed. In order to realise the virtualtesting environment for trucks, Scania initiated a project that could be dividedinto the workload of three master theses. This thesis involved the part ofdeveloping the virtual vehicles, which include the development of a truck modeland a driver model. The developed truck model consists of a single track vehicle model and severalpowertrain models of different complexity provided by Scania. Additionally, thedriver model consists of steering wheel and speed controls in order to keep thetruck on a safe distance from the lead truck and stay on a preferred lane. The keyfeature of the driver-truck model is its modular design, which provides great flexibilityin selecting the level of detail for each component. The driver-truck modelcan be duplicated and simulated together in real time and performs platooningwith each other in a road system based on the real world. As the driver-truckmodel is module based, it can easily be extended for future purposes with morecomplex functions. The driver-truck model is implemented in Simulink and the simulation performancefor different model complexity is evaluated. It is demonstrated that theflexibility of the developed model allows a balanced decision to be made betweenrealistic truck behavior and simulation speed. Furthermore, multi-truck simulationsare performed using the model, which demonstrate the effectiveness of themodel in the evaluation of truck platooning operations.

The freight transport industry faces big challenges as the demand for transport and fuel prices are steadily increasing, whereas the environmental impact needs to be significantly reduced. Heavy-duty vehicle (HDV) platooning is a promising technology for a sustainable transportation system. By semi-autonomously governing each platooning vehicle at small inter-vehicle spacing, we can effectively reduce fuel consumption, emissions, and congestion, and relieve driver tension. Yet, it is not evident how to synthesise such a platoon control system and how constraints imposed by the road topography affect the safety or fuel-saving potential in practice. This thesis presents contributions to a framework for the design, implementation, and evaluation of HDV platooning. The focus lies mainly on establishing fuel-efficient platooning control and evaluating the fuel-saving potential in practice. A vehicle platoon model is developed together with a system architecture that divides the control problem into manageable subsystems. Presented results show that a significant fuel reduction potential exists for HDV platooning and it is favorable to operate the vehicles at a small inter-vehicle spacing. We address the problem of finding the minimum distance between HDVs in a platoon without compromising safety, by setting up the problem in a game theoretical framework. Thereby, we determine criteria for which collisions can be avoided in a worst-case scenario and establish the minimum safe distance to a vehicle ahead. A systematic design methodology for decentralized inter-vehicle distance control based on linear quadratic regulators is presented. It takes dynamic coupling and engine response delays into consideration, and the structure of the controller feedback matrix can be tailored to the locally available state information. The results show that a decentralized controller gives good tracking performance and attenuates disturbances downstream in the platoon for dynamic scenarios that commonly occur on highways. We also consider the problem of finding a fuel-efficient controller for HDV platooning based on road grade preview information under road and vehicle parameter uncertainties. We present two model predictive control policies and derive their fuel-saving potential. The thesis finally evaluates the fuel savings in practice. Experimental results show that a fuel reduction of 3.9–6.5 % can be obtained on average for a heterogenous platoon of HDVs on a Swedish highway. It is demonstrated how the savings depend on the vehicle position in the platoon, the behavior of the preceding vehicles, and the road topography. With the results obtained in this thesis, it is argued that a significant fuel reduction potential exists for HDV platooning. / <p>QC 20140527</p>

Indiana University-Purdue University Indianapolis (IUPUI) / With the fast development of 5G networks and the advancement in networking technologies, more and more new technologies such as internet of vehicles (IoV) is catching our eyes. With technologies of artificial intelligence and automatic control, IoV is transformed into an intelligent transportation system (ITS). The object of this thesis is to analyze the connectivity probability issues in vehicle ad hoc networks (VANETs), which is a subset of ITS. This will be achieved by a platoon-based two dimensional model. In order to make the results more accurate and more close to real scenario, different situations will be analyzed separately, and different types of platoon will be included. In addition, other system parameters are also discussed and stimulated. The results show that many parameters like the increases of traffic density, ratio of platoon, and lane numbers will improve connectivity probability. No-leader based platoons are easier to connect to the base stations compared to leader based platoons.

Vehicle platooning is an important innovation in the automotive industry that aims at improving safety, mileage, effciency, and the time needed to travel. This research focuses on the various aspects of vehicle platooning, one of the important aspects being analysis of different control strategies that lead to a stable and robust platoon. Safety of passengers being a very important consideration, the control design should be such that the controller remains robust under uncertain environments. As a part of the Department of Energy (DOE) project, this research also tries to show a demonstration of vehicle platooning using robots. In an automated highway scenario, a vehicle platoon can be thought of as a string of vehicles, following one another as a platoon. Being equipped by wireless communication capabilities, these vehicles communicate with one another to maintain their formation as a platoon, hence are "cognitive." Autonomous capable vehicles in tightly spaced, computer-controlled platoons will lead to savings in energy due to reduced aerodynamic forces, as well as increased passenger comfort since there will be no sudden accelerations or decelerations. Impacts in the occurrence of collisions, if any, will be very low. The greatest benefit obtained is, however, an increase in highway capacity, along with reduction in traffic congestion, pollution, and energy consumption. Another aspect of this project is the automated electric transportation (AET). This aims at providing energy directly to vehicles from electric highways, thus reducing their energy consumption and CO2 emission. By eliminating the use of overhead wires, infrastructure can be upgraded by electrifying highways and providing energy on demand and in real time to moving vehicles via a wireless energy transfer phenomenon known as "wireless inductive coupling." The work done in this research will help to gain an insight into vehicle platooning and the control system related to maintaining the vehicles in this formation.

This report proposes a solution for an automated vehicle follower based on one front-facing monocular camera that can be used to achieve platooning for a lower cost than the systems available on the market today. The sensor will be local to the automated follower vehicle, i.e. no Vehicle-to-Vehicle (V2V) communication. A state-of-the-art chapter describes di erent aspects of platooning, computer vision techniques, state of the art hardware developed especially for autonomous driving as well as systems closely related to the proposed solution. The theory behind the performed implementations such as trajectory, controls, image operations and vehicle models will be presented, followed by a chapter dedicated to the actual implementation. The experimental vehicle used to validate the solution was a modied 1=12 scale radio controlled (RC) car. An Arduino controls the steering and driving motor, and a PC mounted on the vehicle uses a webcam to capture images. The preceding vehicles position relative the follower vehicle was calculated from captured images from the webcam and a trajectory towards the preceding vehicles path was generated from a cubic curve. Measurements from a stereo vision system was used to evaluate the accuracy of the follower vehicle and minimal spacing needed between follower and preceding vehicle. The follower vehicle satisfy the behavior of following a preceding vehicle, but the accuracy of the follower vehicle should be improved to generate a more accurate trajectory before being tested on a larger scale vehicle. The solution shows that a monocular camera can be used to follow a vehicle, and with implementation of a GPS module and a fuzzy velocity controller it could be used to test on a full sized vehicle. / Denna rapport foreslar en losning som gor det mojligt att automatisera ett fordon genom en monokular kamera som foljer en framforvarande ledbil som skulle kunna anvandas inom platooning for en lagre kostnad an de losningar som nns pa marknaden idag. Sensorn ar lokal till det automatiserade fordonet och anvander sig inte utav V2V kommunikation. Ett state-of-the-art kapitel beskriver olika aspekter inom platooning av fordon, datorseende, specikt framtagen hardvara for automatiserade bilar samt automatiserande system som nns inkluderade i bilar idag. Teorin bakom implementationen av bilens trajektoria, reglerteknik, bildbehandlingsoperationer och fordonsmodeller presenteras ocksa. Teorin anvands sedan for att utveckla en prototyp som anvands till att besvara forskningsfragorna. Prototypfordonet ar en modierad radiostyrd bil i skala 1/12. En Arduino styr drivmotor och styrning medan en PC monterad pa bilen anvander sig av en webbkamera for att ta bilder. Ledbilens position relativt foljbilen beraknas med hjalp av bilderna och en bana att folja efter genereras av en tredjegradskurva. Matningar genom ett stereo vision system anvandes for att besvara fragor angaende noggranheten for den utvecklade efterfoljande bilen samt lagsta sakra avstand som kan anvandas mellan bilarna. Den utvecklade prototypbilen foljer efter ledbilen pa ett onskvart satt, dock borde trajektorian som den foljer utvecklas mera innan testning utfors pa storre fordon. Losningen pavisar att en monokular kamera kan anvandas for att folja efter en bil. Om systemet utokas med en GPS modul och en fuzzy hastighetskontroll kan denna losning testas med bilar i full storlek.