Autonomous agents, such as robots and automated vehicles, rely on their ability to perceive and interpret their environment to make informed decisions and execute actions that align with their goals. A key aspect of this capability is situation awareness, which involves understanding the current state of the environment and predicting its future evolution. Traditional autonomous systems address perception and prediction as separate tasks within a sequential pipeline, where raw sensor data is processed into increasingly abstract representations. While this structured approach has driven significant advancements, it remains constrained by sensor limitations, including occlusions, measurement uncertainty, and adverse weather conditions.

This thesis investigates how predictions from past observations can enhance perception algorithms, enabling agents to infer missing information, reduce uncertainty, and better anticipate changes. To support this integration, alternative environment representations are explored that allow feedback between prediction and perception while capturing uncertainty. This tighter coupling improves decision-making, particularly in complex and partially observable environments.

The contributions include: (1) a reachability-based reasoning framework for tracking possible hidden obstacles; (2) its extension to handle delayed and partial external data; (3) a probabilistic mapping method, Transitional Grid Maps (TGM), that jointly models static and dynamic occupancy; and (4) an extension of TGM to mitigate weather-induced sensor noise.

The proposed methods are evaluated in simulated and real scenarios where traditional perception pipelines struggle, such as occluded, highly dynamic and noisy environments. By bridging the gap between perception and prediction, this work contributes to the development of more robust and intelligent autonomous systems.

Autonoma agenter, såsom robotar och självkörande fordon, är beroende av sin förmåga att uppfatta och tolka omgivningen för att fatta välgrundade beslut och utföra handlingar i linje med sina mål. En viktig del av denna förmåga är situationsmedvetenhet, som innebär att förstå miljöns nuvarande tillstånd och förutse dess framtida utveckling. Traditionella autonoma system hanterar perception och prediktion som separata steg i en sekventiell kedja, där sensordata bearbetas till alltmer abstrakta representationer. Även om detta strukturerade tillvägagångssätt lett till stora framsteg, begränsas det av sensorbrister, inklusive skymda objekt, mätosäkerhet och ogynnsamt väder.

Denna avhandling undersöker hur prediktioner från tidigare observationer kan förbättra perceptionsalgoritmer, så att agenter kan sluta sig till saknad information, minska osäkerhet och bättre förutse förändringar. För att möjliggöra denna integration utforskas alternativa omgivningsrepresentationer som ger återkoppling mellan prediktion och perception, samtidigt som osäkerheter kan hanteras. Denna tätare koppling förbättrar beslutsfattandet, särskilt i komplexa och delvis observerbara miljöer.

Avhandlingens huvudsakliga bidrag inkluderar: (1) ett reso\-nemangs\-ramverk baserat på nåbarhet för att spåra möjliga dolda hinder; (2) dess utvidgning för att hantera fördröjd och ofullständig extern data; (3) en probabilistisk kartmetod, Transitional Grid Maps (TGM), som gemensamt modellerar statisk och dynamisk ockupation; och, (4) utvidgning av TGM för att förbättrad hantering av väderrelaterat sensorbrus.

Metoderna utvärderas i scenarier där traditionella perceptionskedjor har problem, exempelvis i skymda, mycket dynamiska och brusiga miljöer. Genom att överbrygga klyftan mellan perception och prediktion bidrar detta arbete till utvecklingen av robustare och intelligentare autonoma system.

Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements on off-board perception systems, the communication network, and AV tactical safety behavior. The methodology is instantiated as a concrete set of models and a tool, exercised through a case study involving intersection traffic conflicts. We show how the age of information and observation uncertainty affect the collaborative system design space and further discuss the generalization and other findings from both the methodology and case study development.

Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.

Introducing connectivity and collaboration promises to address some of the safety challenges facing automated vehicles. This is especially the case for scenarios that suffer from the so-called information gap, where occlusions and rule violating road users pose safety risks, and challenges in reconciling performance and safety. 

Establishing new collaborative systems, encompassing connected vehicles, off-board perception systems and a communication network, can help to overcome the information gap, thus contributing to enhance traffic safety and performance. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. 

We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods and a software tool that enables to explore the design space, the potential for safety enhancements, the corresponding requirements on off-board perception systems and the communication network, and tactical safety behaviors of connected automated vehicles. The methodology is instantiated in terms of a concrete set of models and a tool, exercised through a case study involving intersection traffic conflicts.    

We show how the age of information and measurement uncertainty affect the collaborative system design space and further discuss the generalization and other findings from both the methodology and case study development. 

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.

Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking - with corresponding multiple interpretations of ”edge computing” (e.g. device edge, network edge, distributed cloud, etc.). Considering the strong drivers for edge-computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g. avoiding specific gaps.

Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

Hidden traffic participants pose a great challenge for autonomous vehicles. Previous methods typically do not use previous observations, leading to over-conservative behavior. In this paper, we present a continuation of our work on reasoning about objects outside the current sensor view. We aim to demonstrate our recently proposed method on an autonomous platform and evaluate its reliability and real-time feasibility when using real sensor data. Showing a significant driving performance increase on a real platform, without compromising safety, would be a significant contribution to the field of autonomous driving.

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper.