This work conducts a systematic experimental evaluation of the state-of-the-art Reference Aware Model Predictive Controller (RA-MPC) for autonomous vehicles. The RA-MPC is a path-tracking controller, that maximizes tracking accuracy and comfort. The controller uses a kinematic vehicle model with a nonlinear curvature response table that adapts the steering response online to the vehicle and operating conditions. The adaptiveness and robustness of the controller are analyzed by evaluating the performance on a highway truck, loaded and empty mining trucks, and a city bus. Moreover, highway-like and city-like scenarios are performed using the exact same implementation and parameter settings for all vehicles. The controller and model adaption achieved a very good path tracking performance in all experiments, deviating at most 25 cm from the reference path.

Autonomous Vehicle (AV) technology promises safer, greener, and more efficient means of transportation for everyone. AVs are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where Heavy-Duty Vehicles (HDVs) operate. This thesis addresses lateral motion control for autonomous HDVs using Model Predictive Control (MPC). Lateral control for HDVs still has many open questions to be addressed, in particular, precise path tracking while ensuring a smooth, comfortable, and stable ride, coping with both external and internal disturbances, and adapting to different vehicles and conditions.

To address these challenges, a comprehensive control module architecture is designed to adapt seamlessly to different vehicle types and interface with various planning and localization modules. Furthermore, it is designed to address system delays, maintain certain error bounds, and respect actuation constraints.

This thesis presents the Reference Aware MPC (RA-MPC) for autonomous vehicles. This controller is iteratively improved throughout the thesis. The RA-MPC introduces a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller uses the linear time-varying MPC framework and considers control input rate and acceleration constraints to account for steering limitations. Furthermore, multiple models and control inputs are considered throughout the thesis. Ultimately, curvature acceleration is used as the control input, which together with stability ingredients, allows for stability guarantees under certain conditions via Lyapunov techniques.

MPC is highly dependent on the prediction model used. This thesis proposes and compares different models. First, an offline-fitted, vehicle-specific nonlinear curvature response function is proposed and integrated into the kinematic bicycle model. The curvature response function is modeled as two Gaussian functions. To enhance the model's versatility and applicability to a fleet of vehicles the nonlinear curvature response table kinematic model is presented. This model replaces the function with a table, which is estimated online by means of Kalman filtering, adapting to the current vehicle and operating conditions.

All controllers and models are simulated and experimentally validated on Scania HDVs and iteratively compared to the previous state-of-the-art. The RA-MPC with the nonlinear curvature response table kinematic model is shown to be the best for the problems and conditions considered. The robustness and adaptiveness of the proposed approach are highlighted by testing different vehicle configurations (a haulage truck, a mining truck, and a bus), operating conditions, and scenarios. The model allows all vehicles to accomplish the scenarios with very similar performance. Overall, the results show an average absolute lateral error to path no bigger than 7 cm, and a worst-case deviation no bigger than 25 cm. These results demonstrate the controller's ability to handle a fleet of HDVs, without the need for vehicle-specific tuning or intervention from expert engineers.

Teknik för autonoma fordon lovar säkrare, grönare och effektivare transportmedel för alla. Autonoma fordon förväntas få sin första stora inverkan i inhägnade områden, såsom gruvområden, hamnar och byggarbetsplatser, där tunga fordon är i drift. Denna avhandling behandlar lateral rörelsereglering för autonoma tunga fordon med hjälp av modellprediktiv reglering (Model Predictive Control, MPC). Lateral rörelsereglering för tunga fordon har fortfarande flera öppna utmaningar, i synnerhet gällande precis banföljning som garanterar en mjuk, komfortabel och stabil resa, hanterar både externa och interna störningar och anpassar sig till olika fordon och förhållanden.

För att möta dessa utmaningar är en omfattande regleringsarkitektur utformad för att sömlöst anpassa sig till olika fordonstyper och gränssnitt mot olika planering- och lokaliseringsmoduler. Därutöver är arkitekturen utformad för att hantera systemfördröjningar, bibehålla särskilda felmarginaler och respektera styrdons begränsningar.

Denna avhandling presenterar den Referensmedvetna modellprediktiva regulatorn (Reference Aware MPC, RA-MPC) för autonoma fordon. Denna regulator är iterativt för-bättrad genom hela avhandlingen. Den referensmedvetna modellprediktiva regulatorn introducerar en metod för att systematiskt hantera referenssignaler genererade av rörelseplanerare som beaktar andra typer av algoritmer och fordonsmodeller än regulatorn. Regulatorn använder det linjärt tidsvarierande MPC-ramverket och beaktar begränsningar på styrsignalsförändringar och accelerationer för att ta hänsyn till begränsningar på styrningen. Vidare beaktas flertalet modeller och styrsignaler genom hela avhandlingen. I slutändan används kurvaturacceleration som styrsignal, vilket tillsammans med stabilitetsingredienser möjliggör stabilitetsgarantier under särskilda förhållanden via Lyapunov-tekniker.

MPC är starkt beroende av den prediktionsmodell som används. Denna avhandling föreslår och jämför ett flertal olika modeller. Först föreslås ett offline-skattat fordonsspecifikt ickelinjärt kurvaturstegsvar som integreras i den kinematiska cykelmodellen. Överföringsfunktionen är modellerad som två Gaussiska funktioner. För att förbättra modellens mångsidighet och tillämpbarhet på en fordonsflotta presenteras en ickelinjär kurvatursvarstabell för den kinematiska modellen. Denna modell ersätter överföringsfunktionen med en tabell. Tabellen uppskattas online med hjälp av Kalman-filtrering anpassad till aktuellt fordon och driftförhållanden.

Alla regulatorer och modeller är experimentellt validerade på Scaniafordon, jämförs iterativt med de senaste regulatorerna från forskningsfronten och både för- och nackdelar diskuteras. RA-MPC:n med den ickelinjära kurvatursvarstabellen för den kinematiska modellen har visat sig vara den bästa regulatorn för de problem och förhållanden som studerats. Robustheten och anpassningsförmågan hos den föreslagna metoden framhävs genom att testa olika driftsförhållanden, scenarier och fordonskonfigurationer (en distributionsbil, en gruvlastbil och en buss). Modellen anpassar sig och konvergerar snabbt efter driftstart, vilket gör att alla fordon kan utföra alla scenarier med mycket liknande prestanda. För den utvärderade flottan visar resultaten ett genomsnittligt absolut lateralt fel till vägen som inte är större än 7 cm och en värsta avvikelse som inte är större än 25 cm. Dessa resultat visar regulatorns förmåga att hantera en flotta av tunga fordon, utan behov av fordonsspecifik justering eller handpåläggning från erfarna ingenjörer.

This work addresses the design of a path tracking controller for autonomous vehicles. It reformulates the Reference Aware MPC in order to guarantee closed-loop stability, while maintaining a safe and comfortable ride, and minimizing wear and tear of vehicle components. Stability is proved via Lyapunov techniques. Furthermore, to adapt the response of the controller online while in operation, a novel model for the nonlinear curvature response of the vehicle is proposed. This model is estimated online by means of Kalman filtering. Both the proposed controller and curvature response model are evaluated with simulations and through experiments on a Scania construction truck, where the advantages to the previous state-of-the-art are highlighted and discussed.

Autonomous vehicle technology is shaping the future of road transportation. This technology promises safer, greener, and more efficient means of transportation for everyone. Autonomous vehicles are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where heavy-duty vehicles (HDVs) operate. Although research for autonomous systems has boomed in recent years, there are still many challenges associated with them. This thesis addresses lateral motion control for autonomous HDVs using model predictive control (MPC).

First, the autonomous vehicle architecture and, in particular, the control module architecture are introduced. The control module receives the current vehicle states and a trajectory to follow, and requests a velocity and a steering-wheel angle to the vehicle actuators. Moreover, the control module needs to handle system delays, maintain certain error bounds, respect actuation constraints, and provide a safe and comfortable ride.

Second, a linear robust model predictive controller for disturbed discrete-time nonlinear systems is presented. The optimization problem includes the initial nominal state of the system, which allows to guarantee robust exponential stability of the disturbance invariant set for the discrete-time nonlinear system. The controller effectiveness is demonstrated through simulations of an autonomous vehicle lateral control application. Finally, the controller limitations and possible improvements are discussed with the help of a more constrained autonomous vehicle example.

Third, a path following reference aware MPC (RA-MPC) for autonomous vehicles is presented. The controller makes use of the linear time-varying MPC framework, and considers control input rates and accelerations to account for limitations on the vehicle steering dynamics and to provide a safe and comfortable ride. Moreover, the controller includes a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller is verified through simulations and through experiments with a Scania construction truck. The experiments show an average lateral error to path of around 7 cm, not exceeding 27 cm on dry roads.

Finally, the nonlinear curvature response of the vehicle is studied and the MPC prediction model is modified to account for it. The standard kinematic bicycle model does not describe accurately the lateral motion of the vehicle. Therefore, by extending the model with a nonlinear function that maps the curvature response of the vehicle to a given request, a better prediction of the vehicle's movement is achieved. The modified model is used together with the RA-MPC and verified through simulations and experiments with a Scania construction truck, where the improvements of the more accurate model are verified. The experiments show an average lateral error to path of around 5 cm, not exceeding 20 cm on wet roads.

Autonoma fordon förväntas få en stor inverkan på framtidens transporter av gods och personer. En teknologi som lovar säkrare, grönare och effektivare transporter till alla. Den typ av verksamhet som autonoma fordon först förväntas få ett större genomslag inom är transporter i avskilda områden, så som gruvområden, hamnar och byggplatser. Även om forskning kopplat till autonoma system har exploderat under den senaste åren kvarstår fortfarande ett flertal frågeställningar. Denna avhandling fokuserar på lateral rörelsestyrning av tunga autonoma fordon med modellprediktiva regulatorer (MPC).

Avhandlingen består av fyra huvuddelar. I först delen introduceras det autonoma fordonets systemarkitektur, med fokus på regulatormodulen. Regulatormodulen genererar hastighet och rattvinkel referenser till fordonets hastighetaktuator och rattvinkelaktuator baserat på fordonets nuvarande tillstånd samt den givna referensbanan som fordonet skall följa. Regulatormodulen behöver dessutom hantera fördröjningar i systemet, säkerställa att systemet inte överskrider givna felmarginaler, hantera aktuator och systembegränsningar, och sist men inte minst framföra fordonet på ett säkert och komfortabelt sätt.

I andra delen presenteras en robust modellprediktiv regulator för ett tidsdiskret olinjärt system med störningar. I  optimeringsproblemet inkluderas systemets nominella initialtillstånd, detta möjliggör garanterad robust exponentiell stabilitet för det tidsdiskreta olinjära systemets störningsinvarianta tillståndsmängd. Regulatorns prestanda visas genom simuleringar av ett autonomt fordon där regulatorn kontrollerar fordonets laterala rörelse. Begränsningar och potentiella förbättringar av regulatorn diskuteras utifrån exempel med ökade begränsningar.

I tredje delen presenteras en referens medveten modellprediktiv regulator (RA-MPC), en regulator utvecklad för att styra ett autonomt fordon längs en given referensbana. Regulator baseras på en linjärt tidsvarierande MPC och begränsningar i fordonets styrdynamik hanteras genom att beräkna dessa baserat på in insignalernas, referensbana, värden och derivator. Genom att beakta begränsningarna på detta sätt möjliggörs en komfortabel och säker körning. En systematisk metod för att hantera referensbanor som genererats av rörelseplanerare baseras på algoritmer och modeller som skiljer sig från de som används i regulatorn presenteras också. Den metoden är även implementerad i regulatorn. Regulatorn har utvärderats med såväl simuleringar som tester. Testerna har genomförts i en Scania lastbil av anläggningstyp. Experimenten visade på en lateral avvikelse från referensbana på 7 cm i genomsnitt och en maximal avvikelse på 27 cm då fordonet kördes på torr asfalt.

I den sista delen studeras olinjär respons i fordonets kurvaturreglering och hur detta kan hanteras i MPC’ns prediktions modell av fordonet presenteras också. En prediktions modell baserad på en standard kinematisk cykelmodell beskriver inte fordonets laterala rörelse tillräckligt bra för det studerade systemet. Dock, genom att utvidga modellen med en funktion som mappar fordonets respons mot en given kurvaturbegäran kan noggrannhet av fordonets rörelse förbättras. Modellen tillsammans med RA-MPC utvärderades genom simuleringar och tester. Testerna har genomförts i en Scania lastbil av anläggningstyp. Utvärderingen visade att den introducerade modellen gav en förbättrad precision. Experimenten visade på en lateral avvikelse från referensbanan på 5 cm i genomsnitt och en maximal avvikelse på 20 cm då fordonet kördes på våt asfalt.

This paper investigates how to compensate for curvature response mismatch in lateral Model Predictive Control (MPC) of an autonomous vehicle. The standard kinematic bicycle model does not describe accurately the vehicle yaw-rate dynamics, leading to inaccurate motion prediction when used in MPC. Therefore, the standard model is extended with a nonlinear function that maps the curvature response of the vehicle to a given request. Experimental data shows that a two Gaussian functions approximation gives an accurate description of this mapping. Both simulation and experimental results show that the corresponding modified model significantly improves the control performance when using Reference Aware MPC for autonomous driving of a Scania heavy-duty construction truck.

This paper presents a path following controller for autonomous vehicles, making use of the linear time-varying model predictive control (LTV-MPC) framework. The controller takes into consideration control input rates and accelerations, not only to account for limitations in the steering dynamics, but also to provide a safe and comfortable ride while minimizing wear and tear of the vehicle components. Furthermore, it introduces a method to handle model references generated by motion planning algorithms that can consider different vehicle models from the controller. The proposed controller is verified by simulations and through experiments in a Scania construction truck, and is shown to have better performance than the state-of-the-art smooth and accurate MPC.

Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large vehicles, such as buses. We present a path planning framework that addresses the demanding driving task of buses in highly constrained environments, such as urban areas. The approach is formulated as an optimization problem using the road-aligned vehicle model. The road-aligned frame introduces a distortion on the vehicle body and obstacles, motivating the development of novel approximations that capture this distortion. These approximations allow for the formulation of safe and accurate collision avoidance constraints. Unlike other path planning approaches, our method exploits curbs and other sweepable regions, which a bus must often sweep over in order to manage certain maneuvers. Furthermore, it takes full advantage of the particular characteristics of buses, namely the overhangs, an elevated part of the vehicle chassis, that can sweep over curbs. Simulations are presented, showing the applicability and benefits of the proposed method.

Cooperative automated driving is a promising development in reducing energy consumption and emissions, increasing road safety, and improving traffic flow. The Grand Cooperative Driving Challenge (GCDC) 2016 was an implementation oriented project with the aim to accelerate research and development in the field. This paper describes the development of the two vehicle systems with which KTH participated in GCDC 2016. It presents a reference system architecture for collaborative automated driving as well as its instantiation on two conceptually different vehicles: a Scania truck and the research concept vehicle, built at KTH. We describe the common system architecture, as well as the implementation of a selection of shared and individual system functionalities, such as V2X communication, localization, state estimation, and longitudinal and lateral control. We also present a novel approach to trajectory tracking control for a four-wheel steering vehicle using model predictive control and a novel method for achieving fair data age distribution in vehicular communications.

This paper addresses the design of time-varying model predictive control of an autonomous vehicle in the presence of input rate constraints such that closed-loop stability is guaranteed. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost to the controller formulation. The terminal set is the maximum positive invariant set of a multi-plant description of the vehicle linear time-varying model. The terminal cost is an upper-bound on the infinite cost-to-go incurred by applying a linear-quadratic regulator control law. The proposed control design is experimentally tested and successfully stabilizes an autonomous Scania construction truck in an obstacle avoidance scenario.

This paper presents a robust model predictive controller for discrete-time nonlinear systems, subject to state and input constraints and unknown but bounded input disturbances. The prediction model uses a linearized time-varying version of the original discrete-time system. The proposed optimization problem includes the initial state of the current nominal model of the system as an optimization variable, which allows to guarantee robust exponential stability of a disturbance invariant set for the discrete-time nonlinear system. From simulations, it is possible to verify the proposed algorithm is real-time capable, since the problem is convex and posed as a quadratic program.