Learning Motion Primitives Automata for Autonomous Driving Applications

Motion planning methods often rely on libraries of primitives. The selection of primitives is then crucial for assuring feasible solutions and good performance within the motion planner. In the literature, the library is usually designed by either learning from demonstration, relying entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical system’s property, e.g., symmetries. In this work, we propose a method combining data with a dynamical model to optimally select primitives. The library is designed based on primitives with highest occurrences within the data set, while Lie group symmetries from a model are analysed in the available data to allow for structure-exploiting primitives. We illustrate our technique in an autonomous driving application. Primitives are identified based on data from human driving, with the freedom to build libraries of different sizes as a parameter of choice. We also compare the extracted library with a custom selection of primitives regarding the performance of obtained solutions for a street layout based on a real-world scenario

From Specifications to Behavior: Maneuver Verification in a Semantic State Space

To realize a market entry of autonomous vehicles in the foreseeable future, the behavior planning system will need to abide by the same rules that humans follow. Product liability cannot be enforced without a proper solution to the approval trap. In this paper, we define a semantic abstraction of the continuous space and formalize traffic rules in linear temporal logic (LTL). Sequences in the semantic state space represent maneuvers a high-level planner could choose to execute. We check these maneuvers against the formalized traffic rules using runtime verification. By using the standard model checker NuSMV, we demonstrate the effectiveness of our approach and provide runtime properties for the maneuver verification. We show that high-level behavior can be verified in a semantic state space to fulfill a set of formalized rules, which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201

Traffic Management System for the combined optimal routing, scheduling and motion planning of self-driving vehicles inside reserved smart road networks

The topic discussed in this thesis belongs to the field of automation of transport systems, which has grown in importance in the last decade, both in the innovation field (where different automation technologies have been gradually introduced in different sectors of road transport, in the promising view of making it more efficient, safer, and greener) and in the research field (where different research activities and publications have addressed the problem under different points of view). More in detail, this work addresses the problem of autonomous vehicles coordina tion inside reserved road networks by proposing a novel Traffic Management System (TMS) for the combined routing, scheduling and motion planning of the vehicles. To this aim, the network is assumed to have a modular structure, which results from a certain number of roads and intersections assembled together. The way in which roads and intersections are put together defines the network layout. Within such a system architecture, the main tasks addressed by the TMS are: (1) at the higher level, the optimal routing of the vehicles in the network, exploiting the available information coming from the vehicles and the various elements of the network; (2) at a lower level, the modeling and optimization of the vehicle trajectories and speeds for each road and for each intersection in the network; (3) the coordination between the vehicles and the elements of the network, to ensure a combined approach that considers, in a recursive way, the scheduling and motion planning of the vehicles in the various elements when solving the routing problem. In particular, the routing and the scheduling and motion planning problems are formulated as MILP optimization problems, aiming to maximize the performance of the entire network (routing model) and the performance of its single elements - roads and intersections (scheduling and motion planning model) while guaranteeing the requested level of safety and comfort for the passengers. Besides, one of the main features of the proposed approach consists of the integration of the scheduling decisions and the motion planning computation by means of constraints regarding the speed limit, the acceleration, and the so-called safety dynamic constraints on incompatible positions of conflicting vehicles. In particular, thanks to these last constraints, it is possible to consider the real space occupancy of the vehicles avoiding collisions. After the theoretical discussion of the proposed TMS and of its components and models, the thesis presents and discusses the results of different numerical experiments, aimed at testing the TMS in some specific scenarios. In particular, the routing model and the scheduling and motion planning model are tested on different scenarios, which demonstrate the effectiveness and the validity of such approach in performing the addressed tasks, also compared with more traditional methods. Finally, the computational effort needed for the problem solution, which is a key element to take into account, is discussed both for the road element and the intersection element

Situational Awareness Enhancement for Connected and Automated Vehicle Systems

Recent developments in the area of Connected and Automated Vehicles (CAVs) have boosted the interest in Intelligent Transportation Systems (ITSs). While ITS is intended to resolve and mitigate serious traffic issues such as passenger and pedestrian fatalities, accidents, and traffic congestion; these goals are only achievable by vehicles that are fully aware of their situation and surroundings in real-time. Therefore, connected and automated vehicle systems heavily rely on communication technologies to create a real-time map of their surrounding environment and extend their range of situational awareness. In this dissertation, we propose novel approaches to enhance situational awareness, its applications, and effective sharing of information among vehicles.;The communication technology for CAVs is known as vehicle-to-everything (V2x) communication, in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) have been targeted for the first round of deployment based on dedicated short-range communication (DSRC) devices for vehicles and road-side transportation infrastructures. Wireless communication among these entities creates self-organizing networks, known as Vehicular Ad-hoc Networks (VANETs). Due to the mobile, rapidly changing, and intrinsically error-prone nature of VANETs, traditional network architectures are generally unsatisfactory to address VANETs fundamental performance requirements. Therefore, we first investigate imperfections of the vehicular communication channel and propose a new modeling scheme for large-scale and small-scale components of the communication channel in dense vehicular networks. Subsequently, we introduce an innovative method for a joint modeling of the situational awareness and networking components of CAVs in a single framework. Based on these two models, we propose a novel network-aware broadcast protocol for fast broadcasting of information over multiple hops to extend the range of situational awareness. Afterward, motivated by the most common and injury-prone pedestrian crash scenarios, we extend our work by proposing an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection for vulnerable road users. Finally, as humans are the most spontaneous and influential entity for transportation systems, we design a learning-based driver behavior model and integrate it into our situational awareness component. Consequently, higher accuracy of situational awareness and overall system performance are achieved by exchange of more useful information

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences