The increasing demand for transportation has led to critical challenges, including congestion, energy consumption, and safety concerns. Connected and automated vehicles (CAVs) offer a promising solution by enabling cooperative driving technologies such as vehicle platooning and intersection coordination. However, achieving these benefits requires addressing key technical challenges, particularly ensuring safe and efficient vehicle coordination in complex traffic environments.

This thesis focuses on two fundamental problems in cooperative driving: platoon formation and intersection coordination. The primary objective is to develop safety-preserving control and coordination strategies that enable efficient and secure platoon and intersection operations.

We first examine a case of intersection coordination involving highway ramp merging with CAV platoons, where closely spaced vehicles create dynamic bottlenecks for merging traffic. To address this, we propose a bi-level coordination framework in which a central coordinator optimizes merging schedules using a mixed-integer linear programming (MILP) approach. The computed schedule is then executed by individual vehicles at the control level, allowing platoons to split occasionally for the merging vehicles, balancing traffic throughput with platoon maintenance.

The platoon formation problem requires low-level control to manage the physical processes of multi-vehicle formation, including adjustments to vehicle steering, speed, and inter-vehicle spacing. We develop a safe and efficient control strategy that ensures a smooth transition from individual vehicle operation to cohesive platooning on multi-lane highways. By employing constructive barrier feedback, with an added dissipative divergent flow component to the nominal formation control for collision avoidance, our method guarantees collision avoidance with both neighboring vehicles and road boundaries, ensuring computational efficiency and practical deployability. Experimental validation confirms its effectiveness.

For intersection coordination in mixed traffic environments, where CAVs coexist with human-driven vehicles (HDVs), we introduce an invariant safe model predictive control (MPC) framework. This method ensures collision-free interactions by incorporating forward reachable sets and maximal invariant safe sets into the constraint formulation. To enhance real-world applicability, we integrate robust estimation techniques to account for measurement errors and communication uncertainties, enabling safe and efficient intersection navigation in an experimental setting.

Overall, this thesis presents novel control and coordination strategies that enhance the safety and efficiency of vehicle platooning and intersection management. These contributions pave the way for more reliable CAV deployment, with potential benefits for vehicle safety, energy efficiency, and overall traffic flow.

Den ökande efterfrågan på transporter har lett till stora utmaningar, såsom trängsel, hög energiförbrukning och säkerhetsproblem. Uppkopplade och automatiserade fordon erbjuder en lovande lösning genom att möjliggöra samarbetsbaserade teknologier, till exempel konvojkörning och korsningskoordinering. För att uppnå dessa fördelar krävs dock att nyckelteknologiska utmaningar hanteras, särskilt när det gäller att säkerställa en säker och effektiv koordinering av fordon i komplexa trafikmiljöer.

Denna avhandling fokuserar på två grundläggande problem inom samarbetsdriven körning: konvojbildning och korsningskoordinering. Huvudmålet är att utveckla säkerhetsbevarande styrnings- och koordineringsstrategier som möjliggör effektiva och säkra operationer för både konvojkörning och korsningar.

Vi undersöker först ett fall av korsningskoordinering vid påfarter till motorvägar där konvojkörning förekommer. Fordon i konvoj med minimala avstånd till varandra skapar dynamiska barriärer för trafiken från påfarten. För att åtgärda detta föreslår vi ett koordineringsramverk med två nivåer, där en central koordinator optimerar ankomstscheman med hjälp av blandad heltalslinjär programmering. Det resulterande schemat implementeras sedan av de enskilda fordonen, vilket möjliggör att konvojer kan delas upp vid vissa tillfällen för att underlätta inpasseringen av inkommande fordon – vilket balanserar trafikflödet och konvojkörningen.

Konvojbildning med automatiserade fordon kräver noggrann styrning för att hantera de fysiska processerna vid flerfordonsbildning, vilket bland annat innebär justeringar av fordonens styrning, hastighet och avstånd. Vi utvecklar en säker och effektiv styrningsstrategi som säkerställer en smidig övergång från individuell fordonsdrift till sammanhållen konvojkörning på motorvägar med flera körfält. Genom att använda konstruktiv barriäråterkoppling, där en dissipativ divergent flödeskomponent läggs till den nominella formationsregulatorn för att undvika kollisioner, garanterar vi både säkerheten gentemot andra fordon och mot väggränser – vilket gör metoden både beräkningseffektiv och praktiskt tillämpbar. Experimentell validering bekräftar metodens effektivitet.

För korsningskoordinering i blandad trafik, där automatiserade fordon och mänskliga förare finns, introducerar vi en säkerhetsbevarande modellprediktiv reglering. Denna metod säkerställer kollisionfria interaktioner genom att använda åtkomliga mängder och maximala oföränderliga säkra mängder under bivillkoren i optimeringsproblemet. För att förbättra den praktiska tillämpningen integreras robusta estimeringsmetoder för att hantera mätfel och kommunikationsosäkerheter, vilket möjliggör säker och effektiv navigering i korsningar i experimentella inställningar.

Sammanfattningsvis presenterar denna avhandling nya reglerings- och koordineringsstrategier som förbättrar säkerheten och effektiviteten i konvojbildning och korsningshantering. Dessa bidrag banar vägen för en mer tillförlitlig användning av automatiserade fordon, med potentiella fördelar för fordonssäkerhet, energieffektivitet och övergripande trafikflöde.

This paper proposes a novel formation control design for safe platooning and merging of a group of vehicles in multi-lane road scenarios. Provided a leader vehicle is independently controlled, the objective is controlling the follower vehicles to drive in the desired lane with a constant desired distance behind the neighboring (preceding) vehicle while preventing collisions with both the neighboring vehicle and the road's edges. Inspired by the recent concept of constructive barrier feedback, the proposed controller for each follower vehicle is composed of two parts: one is the nominal controller that ensures its tracking of the neighboring vehicle; another is for collision avoidance by using divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring vehicle and road edges without compromising the nominal controller's performance. The key contribution of this work is that the proposed control method ensures collision-free platooning and merging control in multi-lane road scenarios with computational efficiency and systematic stability analysis. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.

This paper addresses the coordination challenge at intersections of mixed traffic involving both Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). To strike a balance between coordination performance and safety guarantees, we propose an invariant safe Contingency Model Predictive Control (CMPC) framework. The CMPC framework incorporates two parallel horizons for the ego vehicle: a nominal horizon optimized for performance based on the most likely prediction of the opponent HDV, and a contingency horizon designed to maintain an invariant safe backup plan for emergencies. In the contingency horizon, we consider the worst-case behavior of the human driver and formulate safety constraints using the forward reachable sets of the HDV within the planning horizon. These safety constraints are complemented by maximal invariant safe sets as terminal constraints. The two horizons are tied together by enforcing equality of the feedback inputs at the beginning of the horizons. We provide theoretical evidence supporting the recursive feasibility and persistent performance improvement of the invariant safe CMPC compared to our previously proposed nominal invariant safe Model Predictive Control (MPC). Through simulation studies, we evaluate the proposed method. The simulation results demonstrate that the CMPC approach achieves enhanced performance by reducing conservatism while simultaneously preserving the invariant safety property.

Connected and automated vehicles (CAVs) are a transformative technology that promises to bring innovative solutions to transportation systems. One of their significant advantages is the elimination of human factors, which makes them capable of resolving the congestion problem prevalent in areas such as ramp merging points and road intersections. Through the sharing of information between vehicles and with the infrastructure, CAVs can coordinate their cross-time cooperatively. This collaborative approach results in avoiding unnecessary stops, which leads to more efficient utilization of the road infrastructure and improved safety for all road participants.

In this licentiate thesis work, we study the problem of formulating coordination strategies for CAVs to efficiently traverse through conflicting regions while maintaining safety with other road participants such as other CAVs or platoons of CAVs or human-driven vehicles (HDVs). We emphasize the challenges when platoons or HDVs are involved and develop coordination solutions on various scales accordingly.   

We start by considering a highway ramp merging scenario that involves platoons of CAVs. In such scenarios, vehicles in a platoon drive in close proximity and create moving barriers for merging traffic. To address this challenge, we propose a bi-level coordination framework. A central coordinator schedules the merging time and speed for all CAVs by solving a mixed integer linear programming (MILP) problem, optimizing traffic performance while maintaining platoon formation. This assigned schedule is executed by each individual vehicle at the control level. When integrated, this framework allows platoons to split occasionally for the merging vehicles, balancing traffic throughput with platoon formation.

In intersections where mixed traffic is present, CAVs need to anticipate the driving behavior of HDVs accurately to plan a safe future trajectory. Due to the unpredictable nature of human behavior, we propose an invariant safe model predictive control (MPC) that considers worst-case scenarios using forward reachable sets to guarantee safety at all times. In addition, to compensate for conservatism, we apply a contingency MPC (CMPC) framework with parallel horizons. One horizon is for safety guarantees in contingency, while the other is for optimizing performance. The methods are applied to general intersection problems through distributed implementation and produce safe and efficient coordination results.

Lastly, we examine the challenges that arise in the real-life implementation of the proposed coordination strategies. To account for potential occlusions, estimation errors, and communication defects, we propose a communication-based and integrated framework from estimation to control. Through set estimation and reachability analysis, we generate robust set estimations of surrounding vehicles and integrate this information with the coordination stack to ensure safe navigation through intersections in an experimental setting.

Det uppkopplade och automatiserade fordonet är en transformerande teknik som förväntas ge nya lösningar för transportsystemet. Genom att ta bort den mänskliga faktorn har utplaceringen av uppkopplade och automatiserade fordon potential att lösa trängselproblemen vid påfarter till motorvägar eller vägkorsningar. Genom att dela information mellan fordon och infrastruktur kan automatiserade fordon samarbeta och schemalägga övergångstider. Genom att undvika onödiga stopp kan väginfrastrukturen utnyttjas mer effektivt och samtidigt erbjuda ökad säkerhet för trafikdeltagarna.

I detta licentiatavhandlingsarbete studerar vi problemet med att formulera samordningsstrategier för automatiserade fordon för att effektivt korsa genom konfliktområden i trafiken samtidigt som säkerheten bibehålls för andra vägdeltagare, såsom andra automatiserade fordon, konvojkörning av automatiserade fordon eller mänskligt drivna fordon. Vi betonar utmaningarna som uppstår när konvojkörning eller mänskliga förare är inblandade och utvecklar samordningslösningar i olika skalor därefter.

Vi börjar med att behandla ett scenario för påfarter till motorvägar när konvojkörning förekommer. Fordon i ett tåg bildar en konvojkörning genom att köra i nära avstånd till varandra. Som ett resultat av detta beter sig konvojkörningen som rörliga barriärer för trafiken från påfarten. För att lösa problemet föreslår vi ett ramverk med två nivåer. En central koordinator schemalägger en korsningstid och hastighet för alla automatiserade fordon genom att lösa en blandad heltals linjär programmering som optimerar trafikprestanda och upprätthåller konvojbildningen. Det tilldelade schemat körs individuellt på fordonens nivå. När den är integrerad, tillåter ramverket att konvojkörningen bryts vid enstaka tillfällen för fordon från påfarten att köra in. Resultatet blir en optimal balansering mellan trafikflödet och konvojbildningen.

Vid korsningar med blandad trafik måste automatiserade fordon korrekt förutse beteendet för det mänskliga föraren och planera en säker framtida bana genom korsningen. På grund av det oförutsägbara mänskliga beteendet föreslår vi en oföränderlig säker modellprediktiv reglering genom att överväga de värsta scenarierna med användning av framåtnåbar uppsättning. För att kompensera för konservatismen tillämpar vi ett ramverk för beredskapsmodellprediktiv reglering som använder parallella horisonter, en för säkerhetsgarantier i beredskap och den andra för att optimera prestanda. Genomen distribuerad implementering tillämpas metoderna för generella korsningsproblem och ger säkra och effektiva samordningsresultat.

Till slut tar vi upp de problem som uppstår vid implementering i verkligheten. För att ta hänsyn till potentiella ocklusioner, uppskattningsfel och kommunikationsdefekter föreslår vi ett kommunikationsbaserat och integrerat ramverk från uppskattning till kontroll. Genom att använda uppskattning och nåbarhetsanalys gör vi robusta uppskattningar av omgivande fordon och integrerar denna information med samordningsramverket för att navigera fordon säkert genom korsningar i en experimentell miljö.

In this paper, we propose an integrated framework for safe intersection coordination of connected and automated vehicles (CAVs) in mixed traffic. An intelligent intersection is introduced as a central node to orchestrate state data sharing among connected agents and enable CAV to acknowledge the presence of human-driven vehicles (HDVs) beyond the line of sight of onboard sensors. Since state data shared between agents might be uncertain or delayed, we design the intelligent intersection to safely compensate for these uncertainties and delays using robust set estimation and forward reachability analysis. When the intersection receives state data from an agent, it first generates a zonotope to capture the possible measurement noise in the state estimate. Then, to compensate for communication and processing delays, it uses forward reachability analysis to enlarge the set to capture all the possible states the agent could have occupied throughout the delays. Finally, using the resulting set as the initial condition, a distributed model predictive control onboard the CAV will plan an invariant safe motion by considering the worst-case behavior of human drivers. As a result, the vehicle is guaranteed to be safe while driving through the intersection. A prototype of our proposed framework is implemented using.

The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin to minimize the cost of violating soft constraints due to uncertainty and disturbances. The module can be combined with any controller and is based on modifying the controller output, in a least squares sense, such that constraints are satisfied within the considered horizon. The proposed method is computationally efficient and suitable for real-time applications. The effectiveness of the method is illustrated by a simple heater example and a nonlinear and time-varying example in penicillin fed-batch production optimization. Copyright

This paper considers the heterogeneous traffic intersection where both Human Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) exist. In such a dynamic environment, CAVs must act in a way such that safety is guaranteed at all times, which is challenging due to the unpredictable nature of human behavior. To guarantee safety, in this paper we consider the worst-case behavior of HDVs by constructing the forward reachable set and ensuring collision avoidance against the forward reachable set within the CAV's planning horizon. To ensure safety at all times, a maximal invariant safe set is designed and used as a terminal constraint such that within this set there is always admissible control for CAVs to react against all possible future behavior of other vehicles safely. Finally, we propose to solve the intersection coordination problem within a Distributed Model Predictive Control (DMPC) framework where all pairwise safety constraints among CAVs are decoupled by prioritization. As a result, each CAVs solves a Mixed Integer Quadratic Programming (MIQP) problem considering collision avoidance with all CAVs of higher priority and with all HDVs. We give theoretical proof of the recursive feasibility of our proposed DMPC formulation and practical invariant safety guarantees. The resulting solution is evaluated in simulation and shows that our coordination framework can provide invariant safe coordination in a heterogeneous traffic intersection.

Vehicle platooning is an emerging and promising technology with the benefit of fuel-saving and traffic capacity improvement, but the presence of long platoons near merging roads could act as a long barrier for merging traffic. This can lead to merging failure and traffic performance degradation without proper treatment. This paper addresses the merging coordination problem for Connected and Automated Vehicles (CAVs) and Platoons of CAVs to achieve an efficient traffic flow at the merging zone without collisions. We present a bilevel framework where we decouple traffic coordination from vehicle motion control. At the traffic coordination level, a centralized coordinator schedules a merging time and speed for each approaching CAV passing through the merging point with Mixed Integer Linear Programming (MILP). The goal of the coordinator is to optimize traffic performance while considering the presence of platoons. At the vehicle control level, each vehicle plans its motion with the assigned schedule as terminal constraints. The individual motion plan is then followed by the vehicle while keeping a minimum safety distance to its neighbor. The resulting solution is evaluated in simulation and it is shown that our coordination framework can adequately manage traffic for the on-ramp merging scenario with CAVs and platoons.

A batched sparse (BATS) code provides a novel two-stage coding structure for the multi-hop network, in which the outer code performed at the source node generates a potentially unlimited number of batches and the inner code at the intermediate network nodes applies network coding on packets belonging to the same batch. Previous works have studied the performance of BATS codes in the erasure channels, in which the packet loss rate \varepsilon is always assumed to be a constant on each link. However, in some application scenarios such as the Industrial Internet of Things (IIoTs) where there are a number of mobile nodes in the networks, the channel conditions could be time-variant due to the mobility of nodes, resulting the packet loss rate \varepsilon varying over time as well. Therefore this paper studies the rank distribution which is one of the most significant performance metric of BATS codes under time-variant channels by assuming the packet loss between links modeled as a random variable instead of a constant value. Closed-form expressions of rank distribution are obtained with the packet loss rate \varepsilon following two typical types of distributions. Both numerical and simulation results are provided to verify our analysis.

In this work, we present a novel approach based on linear matrix inequalities to design a linear-time varying model predictive controller for a nonlinear system with guaranteed stability. The proposed method utilizes a multi-model description to model the nonlinear system where the dynamics is represented by a group of linear-time invariant plants, which makes the resulting optimization problem easy to solve in real-time. In addition, we apply the control invariant set designed as the final stage constraint to bound the additive disturbance introduced to the plant by other subsystems interfacing with the controller. We show that the persistent feasibility is ensured with the presence of such constraint on the disturbance of the specified kind. The proposed method is then put into the context of emergency lane change for steering control of automated vehicles and its performance is verified via numerical evaluation.