Abstract
Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors. The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors. In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.Abstract
Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards [...]Abstract
Mines, construction sites, road construction and quarries are examples of applications where construction equipment are used. In a production chain consisting of several construction machines working together, the work needs to be optimised and coordinated to achieve an environmental friendly, energy efficient and productive production. Recent rapid development within positioning services, telematics and human machine interfaces (HMI) opens up for control of individual machines and optimisation of transport missions where several construction machines co-operate. The production chain on a work site can be split up in different sub-tasks of which some can be transport missions. Taking off in a transport mission where one wheel loader ("loader" hereinafter) and two articulated haulers ("haulers" hereinafter) co-operate to transport material at a set production rate [ton/h], a method for fuel optimal control is developed. On the mission level, optimal cycle times for individual sub-tasks such as wheel loader loading, hauler transport and hauler return, are established through the usage of Pareto fronts. The haulers Pareto fronts are built through the development of a Dynamic Programming (DP) algorithm that trades fuel consumption versus cycle time for a road stretch by means of a time penalty constant. Through varying the time penalty constant n number of times, discrete fuel consumption - cycle time values can be achieved, forming the Pareto front. At a later stage, the same DP algorithm is used to generate fuel optimal vehicle speed and gear trajectories that are used as control signals for the haulers. Input to the DP algorithm is the distance to be travelled, road inclination, rolling resistance coefficient and a max speed limit to avoid unrealistic optimisation results. Thus, a method to describe the road and detect the road related data is needed to enable the optimisation. A map module is built utilising an extended Kalman Filter, Rauch-Tung-Striebel smoother and sensor fusion to merge data and estimate parameters not observable by sensors. The map module uses a model of the vehicle, sensor signals from a GPS or GNSS sensor and machine sensors to establish a map of the road. The wheel loader Pareto front is based on data developed in previous research combined with Volvo in-house data. The developed optimisation algorithms are implemented on a PC and in an interactive computer tablet based system. A human machine interface is created for the tablet, guiding the operators to follow the optimal control signals, which is speed for the haulers and cycle time for the loader. To evaluate the performance of the system it is tested in real working conditions. The contributions develop algorithms, set up a demo mission control system and carry out experiments. Altogether rendering in a platform that can be used as a base for a future design of an off-road transport mission control system.Abstract
Mines, construction sites, road construction and quarries are examples of applications where construction equipment are used. In a production chain consisting of several construction machines working together, the work needs to be optimised and coordinated to achieve an environmental [...]Abstract
Detection and tracking of other vehicles and lane geometry will be required for many future intelligent driver assistance systems. By integrating the estimation of these two features into a single filter, a more optimal utilization of the available information can be achieved. For example, it is possible to improve the lane curvature estimate during bad visibility by studying the motion of other vehicles. This paper derives and evaluates various approximations that are needed in order to deal with the non-linearities that are introduced by such an approach.Abstract
Detection and tracking of other vehicles and lane geometry will be required for many future intelligent driver assistance systems. By integrating the estimation of these two features into a single filter, a more optimal utilization of the available information can be achieved. For [...]Abstract
network-wide coordination system for heavy-duty vehicle platooning with the purpose of reducing fuel consumption is developed. Road freight is by far the dominating mode for overland transport with over 60 % modal share in the OECD countries and is thus critically important for the economy. Overcoming its strong dependency on fossil fuels and manual labor as well as handling rising congestion levels are therefore important societal challenges. Heavy-duty vehicle platooning is a promising near-term automated-driving technology. It combines vehicle-to-vehicle communication and on-board automation to slipstream in a safe manner, which can reduce fuel consumption by more than 10 %. However, in order to realize these benefits in practice, a strategy is needed to form platoons in an operational context. We propose a platoon coordination system that supports the process of automatically forming platoons over large geographic areas. We develop an architecture in which fleet management systems send start locations, destinations, and arrival deadlines to a platoon coordinator. By computing desirable speed profiles and by letting the vehicles' on-board systems track them, vehicles can meet en route and form platoons. Matching vehicles into platoons and deriving suitable speed profiles is treated as an optimization problem with the objective of maximizing the overall fuel savings under the constraint that vehicles arrive in time at their destinations. By updating the speed profiles and the platoon configurations based on real-time measurements of vehicle position and platoon state, the system can accommodate new vehicles joining on the fly. Using real-time measurements also makes the system resilient to disturbances and changing operating conditions. This thesis seeks to develop the theoretical foundations of such a system and evaluate its potential to improve transport efficiency. We first explore the coordination of vehicle pairs. Fuel-optimal speed profiles are derived. The uncertainty arising from traffic is taken into account by modeling travel time distributions and considering the probability of two vehicles successfully merging. Building on this coordination algorithm for vehicle pairs, we derive algorithms for larger platoons and vehicle fleets. This results in an NP-hard combinatorial optimization problem. The problem is formulated as an integer program and results on the solution structure are derived. In order to handle realistic fleet sizes with thousands of vehicles and continental sized geographical areas under real-time operation, heuristic algorithms are developed. The speed profiles resulting from the combinatorial optimization are further improved using convex optimization. Moreover, we derive efficient algorithms to identify all pairs of vehicles that can platoon. Simulations demonstrate that the proposed algorithm is able to compute plans for thousands of vehicles. Coordinating approximately a tenth of Germany's heavy-duty vehicle traffic, platooning rates over 65 % can be achieved and fuel consumption can be reduced by over 5 %. The proposed system was implemented in a demonstrator system. This demonstrator system has been used in experiments on public roads that show the technical feasibility of en route platoon coordination. Ett nätverksomfattande system för att koordinera körning i lastbilskolonner i syfte att minska bränsleförbrukningen utvecklas i denna avhandling.Vägtransport är med marginal det mest dominerande sättet för landtransport med en andel över 60 % i OECD-länderna och är således avgörande för samhällsekonomin. Dess starka beroende av fossila bränslen och arbetskraft samt ökande trängsel är därför viktiga utmaningar för samhället. Vägtransport är på väg att genomgå grundläggande förändringar genom elektrifiering, kommunikation och automation. Lastbilskolonnkörning är en teknologi för att automatisera fordon, som är redo att lanseras inom en snar framtid. Kolonnkörning kan ge bränslebesparingar på över 10 % tack vare att automatisering och kommunikation mellan fordon möjliggör så kallad slipstreaming på ett säkert sätt. Det behövs dock en strategi för att sätta ihop lastbilskolonner för att kunna dra nytta av denna teknologi. Vi föreslår därför ett automatiserat koordineringssystem som stödjer processen att sätta ihop lastbilskolonner inom stora geografiska områden. Vi utvecklar en arkitektur där fleet management system skickar start- och målpunkter samt senaste ankomsttid till koordineringssystemet. Genom att beräkna passande hastighetsprofiler och genom att låta lastbilarnas färddatorer följa dem kan lastbilar mötas på vägen och bygga ihop kolonner. Problemet att matcha fordon till kolonner och att komma fram till passande hastighetsprofiler hanteras som ett optimeringsproblem med målet att maximera den totala bränslebesparingen. Genom att uppdatera lösningen baserade på realtidsmätningar av fordonspositioner och kolonntillstånd kan systemet hantera tillkommande lastbilar och kan vara robust gentemot störningar och förändrade operativa förutsättningar. Denna avhandling strävar efter att utveckla teoretiska förutsättningar för ett sådant system och att utvärdera dess potential att öka transportsystemets effektivitet. Vi börjar med att undersöka koordinering av fordonspar och härleder bränsleoptimala hastighetsprofiler. Den osäkerhet som beror på trafiken tas hänsyn till genom att modellera hastighetsfördelningar och genom att betrakta sannolikheten att två fordon träffas och kan påbörja kolonnkörning. Vi bygger på denna algoritm för fordonspar för att härleda algoritmer som kan hantera större kolonner och större antal lastbilar. Det resulterar i ett NP-svårt kombinatoriskt optimeringsproblem. Problemet formuleras som ett heltalsoptimieringsproblem och vi kommer fram till resultat som visar strukturen av lösningen. Resulterande hastighetsprofiler förbättras ytterligare med hjälp av konvex optimering. Förutom detta utvecklas effektiva algoritmer för att identifiera alla fordonspar som kan köra i kolonn. Simuleringar demonstrerar att den föreslagna algoritmen klarar att beräkna planer för tusentals lastbilar. Vi visar att en samording av c:a 10 % av Tysklands lastbilstrafik kan tillåta att mer än 65 % av den totala körsträckan körs i kolonn och att bränsleåtgången kan minskas med mer än 5 %. Det presenterade systemet implementerades i ett demonstratorsystem. Experiment med demonstratorn på allmän väg har visat att koordinering av kolonnkörning på vägen är tekniskt genomförbart. "p"QC 20180403Abstract
network-wide coordination system for heavy-duty vehicle platooning with the purpose of reducing fuel consumption is developed. Road freight is by far the dominating mode for overland transport with over 60 % modal share in the OECD countries and is thus critically important for the [...]Abstract
The fuel consumption of heavy-duty vehicles can be reduced by using information about the upcoming road section when controlling the vehicles. Most manufacturers of heavy-duty vehicles today offer such look-ahead controllers for highway driving, where the information consists of the road grade and the velocity only has small variations. This thesis considers look-ahead control for applications where the velocity of the vehicle has large variations, such as distribution vehicles or vehicles in mining applications. In such conditions, other look-ahead information is important, for instance legal speed limits and curvature. Fuel-efficient control is found by formulating and solving the driving missions as optimal control problems. First, it is shown how look-ahead information can be used to set constraints in the optimal control problems. A velocity reference from a driving cycle is modified to create an upper and a lower bound for the allowed velocity, denoted the velocity corridor. In order to prevent the solution of the optimal control problem from deviating too much from a normal way of the driving, statistics derived from data collected during live truck operation are used when formulating the constraints. It is also shown how curvature and speed limits can be used together with actuator limitations and driveability considerations to create the velocity corridor. Second, a vehicle model based on forces is used to find energy-efficient velocity control. The problem is first solved using Pontryagin's maximum principle to find the energy savings for different settings of the velocity corridor. The problem is then solved in a receding horizon fashion using a model predictive controller to investigate the influence of the control horizon on the energy consumption. The phasing and timing of traffic lights are then added to the available information to derive optimal control when driving in the presence of traffic lights. Third, the vehicle model is extended to include powertrain components in two different approaches. In a first approach, a Boolean variable is added to represent open or closed powertrain. This enables the vehicle to freewheel, in order to save fuel by reducing the losses due to engine drag. The problem is formulated as a mixed integer quadratic program. In a second approach, the full powertrain is modeled including a fuel map and a model of the gearbox losses, both based on measurements on real components. The problem is solved using dynamic programming, with transitions between states including gear shifts, freewheeling, and coasting in gear. Forth, the optimal control framework is used to implement an optimal control-based powertrain controller in a real Scania truck. The problem is first solved offline resulting in trajectories for velocity and freewheeling. These are used online in the vehicle as references to the existing controllers for torque and gear demands. Experiments are performed with fuel measurements, resulting in 16% fuel savings, compared to 18% savings by solving the optimal control problem. Bränsleförbrukningen för tunga fordon kan sänkas genom att använda information om framtida vägförhållanden för att styra fordonen. De flesta fordonstillverkare erbjuder idag prediktiva farthållare för motorvägskörning, där information består av data för väglutning och fordonets hastighet endast har små variationer. Denna avhandling behandlar körfall där hastighetsvariationerna är stora, som för t.ex. fordon i distributionsdrift eller gruvfordon. För sådana fordon är andra typer av information viktiga, som t.ex. hastighetsbegränsningar och kurvatur. Genom att formulera köruppdraget som ett optimalt styrproblem, tas bränsleeffektiv styrning fram. För det första visas hur framförhållningsinformation kan användas för att sätta bivillkor i det optimala styrproblemet. Utifrån en hastighetsreferens från en körcykel skapas en hastighetskorridor, vilken består av en övre och en undre gräns för den tillåtna hastigheten. För att förhindra att hastigheten i lösning avviker för mycket från ett normalt körsätt används data från verklig lastbilskörning när bivillkoren sätts. Här visas också hur kurvatur och hastighetsbegränsningar kan användas tillsammans med begränsningar på fordonets aktuatorer och anpassning för körbarhet när hastighetskorridoren skapas. För det andra används en fordonsmodell baserad på krafter för att hitta energiminimerande hastighetsstyrning. Styrproblemet löses med hjälp av Pontryagins maximum princip för att undersöka energibesparingarna för olika inställningar på hastighetskorridoren. Problemet formuleras sedan på receding-horizon form och en modellprediktiv regulator används för att undersöka horisontlängdens inverkan på energiförbrukningen. Tid och fas för trafikljus läggs sedan till den tillgängliga informationen för att hitta den optimala körstrategin vid körning bland trafikljus. För det tredje utökas fordonsmodellen till att innehålla drivlinekomponenter via två olika ansatser. I den första ansatsen används en Boleansk variabel för att representera huruvida drivlinan är öppen eller stängd. Detta gör att fordonet kan frirulla, vilket sparar bränsle genom att minska släpförlusterna i motorn. Problemet formuleras som ett blandat kvadratiskt heltalsproblem. I den andra ansatsen modelleras hela drivlinan, med en bränslemussla för motorn och förlustmodell för växellådan baserade på tidigare mätningar. Problemet löses genom dynamisk programmering med övergångar mellan tillstånd genom växlingar, frirullning och släpning. För det fjärde används optimal styrning för att implementera en regulator för drivlinestyrning i en Scania-lastbil. Problemet löses först offline, vilket ger trajektorier för hastighet och frirullning. Dessa används sedan online i fordonet som referens till befintliga regulatorer för momentstyrning och växelval. Experiment med bränslemätning ger 16% uppmätt bränslebesparing mot 18% besparing från lösningen till det optimala styrproblemet. "p"QC 20200518Abstract
The fuel consumption of heavy-duty vehicles can be reduced by using information about the upcoming road section when controlling the vehicles. Most manufacturers of heavy-duty vehicles today offer such look-ahead controllers for highway driving, where the information consists of the [...]Abstract
Traffic congestion is a constantly growing problem, with a wide array ofnegative effects on the society, from wasted time and productivity to elevated air pollution and increased number of accidents. Classical traffic control methods have long been successfully employed to alleviate congestion, improving the traffic situation of many cities and highways. However, traffic control is not universally employed, because of the necessity of installing additional equipment and instating new legislation. The introduction of connected, autonomous vehicles offers new opportunities for sensing and controlling the traffic. The data that most of the vehicles nowadays provide are already widely used to measure the traffic conditions. It is natural to consider how vehicles could also be used as actuators, driving them in a specific way so that they affect the traffic positively. However, many of the currently considered strategies for congestion reduction using autonomous vehicles rely on having a high penetration rate, which is not likely to be the case in the near future. This raises the question: How can we influence the overall traffic by using only a small portion of vehicles that we have direct control over? There are two problems in particular that this thesis considers, congestion wave dissipation and avoidance, and platoon catch-up coordination. First, we study how to dissipate congestion waves by use of a directly controlled vehicle acting as a moving bottleneck. Traffic data can help predict disturbances and constraints that the vehicle will face, and the individual vehicles can be actuated to improve the overall traffic situation. We extend the classical cell transmission model to include the influence of a moving bottleneck, and then use this model to devise a control strategy for an actuator vehicle. By employing such control, we are able to homogenize the traffic without significantly reducing throughput. Under realistic conditions, it is shown that the average total variation of traffic density can be reduced over 5%, while the total travel time increases only 1%. Second, we study how to predict and control vehicles catching up in order to form a platoon, while driving in highway traffic. The influences of road grade and background traffic are examined and vehicles attempting to form a platoon are modelled as moving bottlenecks. Using this model, we are able to predict how much the vehicles might be delayed because of congestion and adjust the plan accordingly, calculating the optimal platoon catch-up speeds for the vehicles by minimizing their energy consumption. This leads to a reduction of energy cost of up to 0.5% compared to the case when we ignore the traffic conditions. More importantly, we are able to predict when attemptingto form a platoon will result in no energy savings, with approximately 80% accuracy. Trafikstockning är ett ständigt växande problem, med ett brett utbud av negativa effekter på samhället, från bortkastad tid och produktivitet till ökade mängd luftföroreningar och antal olyckor. Klassiska metoder för trafik kontroll har länge använts framgångsrikt för att lindra detta problem, med förbättrad trafiksituation för många städer och motorvägar. Trafik kontrollen är emellertid inte universellt tillämpad eftersom den är beroende av ytterligare utrustning och ny lagstiftning som behover instaleras och införas. Införandet av uppkopplade, autonoma fordon medför nya möjligheter att mäta och kontrollera trafiken. Data som de flesta fordon tillhandahållar redan idag används allmänt för att mäta trafikförhållandena. Det är naturligt att överväga hur fordon också skulle kunna användas som ställdon, genom att driva dem på ett visst sätt så att de påverkar trafiken positivt. Men många av dagens strategierna för trängselnedsättning med hjälp av autonoma fordon är beroende av att de tillämpas av en stor del av fordonen, vilket sannoliktinte kommer att bli fallet inom en snar framtid. Det väcker frågan: Hur kan vi påverka den totala trafiksituationen genom att kontrollera en liten del avfordonen? Det finns två problem specifika problem som den här avhandlingentar hänsyn till, trängselvågsavledning och –undvikande samt koordinering av fordonståg av lastbilar. I det första problemet studerar vi hur vi kan skingra trängselvågor med hjälp av ett direktstyrt fordon som fungerar som en rörlig flaskhals. Trafikdatakan hjälpa till att förutsäga störningar och begränsningar som fordonet kommer att stöta på, och de enskilda fordonen kan styras för att förbättra den totala trafiksituation. Vi utvidgar den klassiska cellöverföringsmodellenför att inkludera påverkan av en rörlig flaskhals och använder sedan denna modell för att utforma en kontrollstrategi för ett styrbart fordon. Genom att använda sådan styrning kan vi homogenisera trafiken utan att avsevärt minska genomströmningen. Under realistiska förhållanden visar vi att den genomsnittliga totala variationen i trafiktäthet kan minskas med över 5%, medan den totala körtiden ökar med endast 1%. I det andra problemet studerar vi hur vi kan förutsäga och styra fordonens hastighetsprofiler vid formering av fordonståg under körning i motorvägstrafik. Påverkan av väglutning och motorvägstrafik undersöks, och fordon som försöker bilda en fordonståg modelleras som rörliga flaskhalsar. Med denna modell kan vi förutsäga förseningar på grund av trängsel och justera planen i enlighet med dessa, samt beräkna de optimala hastigheterna för fordonengenom att minimera energiförbrukningen. Detta leder till en minskning av energikostnaden på upp till 0,5% i jämförelse med fallet när vi ignorerar trafikförhållandena. Ännu viktigare är att vi kan vi förutsäga när försök att bildaett fordonståg kommer att resultera i utebliven energibesparing, med ungefär 80% noggrannhet. "p"QC 20181212"/p""p"The research leading to these results has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 674875, VINNOVA within the FFI program under contract 2014-06200, the Swedish Research Council, the Swedish Foundation for Strategic Research and Knut and Alice Wallenberg Foundation. The author is affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP).Abstract
Traffic congestion is a constantly growing problem, with a wide array ofnegative effects on the society, from wasted time and productivity to elevated air pollution and increased number of accidents. Classical traffic control methods have long been successfully employed to alleviate [...]Abstract
The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges. First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario. Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results. Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.Abstract
The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced [...]Abstract
The operation of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Tests conducted on flat roads have shown the potential of platooning to reduce the fuel consumption of about 10%. However, platoons are expected to operate on public highways with varying topography alongside other vehicles. Due to the large mass and limited engine power of heavy-duty vehicles, road slopes have a significant impact on feasible and optimal speed profiles. For single vehicles, experiments have shown that optimizing the speed according to the road profile resulted in fuel saving of up to 3.5%. The use of such a look-ahead control framework is expected to lead to large benefits also for platooning. This thesis presents the design of safe and fuel-efficient control of heavy-duty vehicle platoons driving on realistic road profiles. The scenario where the platooning vehicles cooperate to optimize their overall fuel-efficiency is studied together with the scenario where the vehicles do not explicitly cooperate. First, we propose a control architecture that splits the cooperative platooning control problem into two layers. The top layer computes a reference speed profile that ensures fuel-efficient operation of the entire platoon based on dynamic programming. The bottom layer relies on model predictive control to safely track the reference speed. Simulations show the ability of the proposed controller to save up to 12% of fuel for following vehicles compared to existing platoon controllers and to safely react to emergency braking of the leading vehicle. Second, we propose a gear management layer that fits in the cooperative platooning control architecture and explicitly takes the gear selection into account. The underlying optimal control problem aims at minimizing the vehicle fuel consumption and the reference tracking deviations. Simulations indicate how this formulation outperforms existing alternatives, both in terms of fuel-efficiency and tracking error. Third, we address non-cooperative platooning by proposing a vehicle-following controller suitable for fuel-efficient control of heavy-duty vehicles. The proposed controller explores both the benefits given by the short inter-vehicular distance and those given by pulse-and-glide, i.e., alternating traction and coasting phases. A simulation study suggests fuel saving of up to 18% compared to the single vehicle case, and up to 7% compared to when a constant-distance vehicle-following controller is used. Last, we propose a vehicle-following controller aimed at exploiting long preview of the preceding vehicle trajectory by directly manipulating the inputs of low-level vehicle controllers. This is achieved through a model predictive controller that uses a short prediction horizon and includes a terminal state set that incorporates preview information about the preceding vehicle. Experiments indicate the ability of the controller to avoid unnecessary braking, while simulations show behavior similar to the optimal control behavior. "p"QC 20180416Abstract
The operation of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Tests conducted on flat roads have shown the potential of platooning to reduce the fuel consumption [...]