A major challenge in developing connected and automated vehicles~(CAVs) for urban environments is achieving a comprehensive understanding of the surrounding traffic scene. This relies on situational awareness, defined as the ability to perceive, interpret, and anticipate the behavior of surrounding road-users, which is essential to ensure safety. In particular, unprotected road-users, such as pedestrians and cyclists, are often occluded or located in sensor blind-spots of the CAV, which remains a critical challenge. This thesis aims to improve the situational awareness of the ego-vehicle, the CAV of primary interest, in urban environments by leveraging vehicle-to-everything (V2X) communication to incorporate information from connected road-users. A framework using set-based methods is developed to systematically handle uncertainties in measurements and initial conditions of detected pedestrians.

The objective is to address several key challenges that arise in real-world scenarios, including data inconsistency, data association, pedestrian motion prediction, and efficient reduction of redundant information. The thesis first proposes a shared situational awareness framework for occluded pedestrian-crossing scenario to compute an estimated set for the pedestrian. The framework is extended to handle measurements from V2X units that may be inconsistent with the ground truth of the detected pedestrian. To address scenarios involving multiple occluded pedestrians, a data association method based on intersection-over-union heuristics is introduced. Pedestrian motion prediction is further studied using both a data-driven approach and a bounded velocity–acceleration model applied to the estimated set obtained from the framework. An occlusion-aware extension is also developed to handle situations where occlusions affect both the ego-vehicle and V2X units by exploiting previously observed measurements. Finally, a method for selecting and filtering relevant information from multiple V2X units is proposed to reduce the computational load while maintaining effectiveness. The proposed methods are validated through numerical simulations and real-world experiments using Scania prototype automated vehicles.

En stor utmaning i utvecklingen av uppkopplade och automatiserade fordon (CAV) för stadsmiljöer är att uppnå en omfattande förståelse av den omgivande trafikbilden. Detta bygger på situationsmedvetenhet, definierad som förmågan att uppfatta, tolka och förutse omgivande trafikanters beteende, vilket är avgörande för att säkerställa säkerheten. I synnerhet är oskyddade trafikanter, såsom fotgängare och cyklister, ofta blockerade eller placerade i sensorers blinda vinklar i CAV, vilket fortfarande utgör en kritisk utmaning. Denna avhandling syftar till att förbättra situationsmedvetenheten hos ego-fordonet, CAV:n av primärt intresse, i stadsmiljöer genom att utnyttja fordon-till-allt-kommunikation (V2X) för att inkorporera information från uppkopplade trafikanter. Ett ramverk som använder på mängd-baserade metoder utvecklas för att systematiskt hantera osäkerheter i mätningar och initialvillkor för upptäckta fotgängare. 

Målet är att adressera flera viktiga utmaningar från verkliga scenarier, inklusive datainkonsekvens, dataassociation, förutsägelse av fotgängarrörelser och effektiv minskning av redundant information. Avhandlingen föreslår först ett gemensamt situationsmedvetenhetsramverk för ockluderade övergångsställen för att beräkna en uppskattad mängd för fotgängaren. Ramverket utökas för att hantera mätningar från V2X-enheter som kan vara inkonsekventa med den detekterade fotgängarens verklighet. För att hantera scenarier som involverar flera ockluderade fotgängare introduceras en dataassociationsmetod baserad på snitt-över-union heuristik. Förutsägelse av fotgängarrörelser studeras vidare med hjälp av både en datadriven metod och en begränsad hastighet-accelerations-modell tillämpad på den uppskattade mängden från ramverket. En ocklusionsmedveten utökning utvecklas också för att hantera situationer där ocklusioner påverkar både ego-fordonet och V2X-enheterna genom att utnyttja tidigare mätningar. Slutligen föreslås en metod för att välja och filtrera relevant information från flera V2X-enheter för att minska beräkningsbelastningen samtidigt som effektiviteten bibehålls. Metoderna valideras genom numeriska simuleringar och verkliga experiment med Scanias prototypbaserade automatiserade fordon.

Vehicle-to-everything (V2X) communication holds significant promise for augmenting autonomous driving capabilities. Particularly in dense traffic with occluded areas, V2X can be used to share information about the respective observed areas between traffic participants. In turn, reducing uncertainty about unseen areas can lead to less conservative behaviors while maintaining collision avoidance.This paper aims to leverage V2X to improve situation awareness for trajectory planning. We particularly address two challenges: First, the ego vehicle may not always receive up-to-date information. Second, some areas may remain occluded despite receiving information from other participants.In this work, we fuse the received information about the detected free space. We use reachability analysis to compute areas that are guaranteed to be free despite being occluded. This way, we can maintain collision-avoidance guarantees. We demonstrate the benefits of our proposed method both in simulations and physical experiments.

The safety of unprotected road-users is crucial in any traffic scenario. In most urban scenarios, there are multiple occlusions and blind spots, which can lead to unsafe situations. These occlusions are typically caused by buildings, moving vehicles, and parked vehicles. Situational awareness of an ego-vehicle is defined as the ability of the vehicle to perceive and comprehend traffic conditions while estimating the state of nearby vehicles and unprotected road users. Presently, this ability completely relies on the information provided by the sensors mounted onboard of the ego-vehicle. The information from onboard sensors is usually limited to their field-of-view. In this work, a specific scenario of an occluded pedestrian crossing is considered. The proposed approach is to leverage vehicle-to-everything~(V2X) communication to obtain information from sensors mounted on other vehicles or infrastructure, located in the near surrounding of the ego-vehicle. 

The objective is to build situational awareness by finding sensor information from both local and connected sensors. We focus on a framework that can handle varying uncertainties and provide safety guarantees for the road-users. The framework can compensate for measurement uncertainty and uncertainty in the initial state of the detected road-user. This framework employs an algorithm based on set-based estimation, where the noise is assumed to be unknown but bounded. This algorithm computes an estimated set by integrating measurement sets from both local and connected sensors for the unprotected road-user in the scenarios. To enhance computational efficiency, these sets are represented as convex polygons, specifically zonotopes and constrained zonotopes. We implement the framework in a real system, evaluating its feasibility, efficiency, and robustness under dynamic conditions. We conduct thorough testing, ensuring that the framework meets performance requirements and delivers reliable situational awareness in practical scenarios. 

The safety of unprotected road-users is crucial in any urban traffic. Occlusions and blind spots in the field-of-view of a vehicle can lead to unsafe situations. In this work, a specific pedestrian-crossing scenario is considered with an occlusion in the ego-vehicle's field-of-view. A novel framework is presented to enhance situational awareness based on vehicle-to-everything (V2X) communication to share perception data between vehicle and roadside units. It leverages set-based estimation utilizing a computationally efficient algorithm, for which the pedestrian is guaranteed to be located in a constrained zonotope. The proposed method has been validated through both simulation and real experiments. The real experiments are carried out on a test track using Scania autonomous vehicles.

In this work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed's capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.

In this paper, we present a data-driven approach for safely predicting the future state sets of pedestrians. Previous approaches to predicting the future state sets of pedestrians either do not provide safety guarantees or are overly conservative. Moreover, an additional challenge is the selection or identification of a model that sufficiently captures the motion of pedestrians. To address these issues, this paper introduces the idea of splitting previously collected, historical pedestrian trajectories into different behavior modes for performing data-driven reachability analysis. Through this proposed approach, we are able to use data-driven reachability analysis to capture the future state sets of pedestrians, while being less conservative and still maintaining safety guarantees. Furthermore, this approach is modular and can support different approaches for behavior splitting. To illustrate the efficacy of the approach, we implement our method with a basic behavior-splitting module and evaluate the implementation on an open-source data set of real pedestrian trajectories. In this evaluation, we find that the modal reachable sets are less conservative and more descriptive of the future state sets of the pedestrian.

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.