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  • Presentation: 2019-08-20 10:00 L1, Stockholm
    Oliveira, Rui
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Motion Planning for Heavy-Duty Vehicles2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Autonomous driving is a disrupting technology that is expected to reshape transportation systems. The benefits of autonomous vehicles include, but are not limited to, safer transportation, increased economic growth, and broader access to mobility services. Industry and academia are currently researching a variety of topics related to autonomous driving, however, the focus seems to be on passenger vehicles. As a consequence, heavy-duty vehicles, which are a significant share of transportation systems, are overlooked, and the challenges associated with these vehicles are neglected.

    This thesis studies motion planning algorithms for heavy-duty vehicles. Motion planning is a fundamental part of autonomous vehicles, it is tasked with finding the correct sequence of actions that take the vehicle towards its goal. This work focuses on particular aspects that distinguish heavy-duty vehicles from passenger vehicles, and that call for novel developments within motion planning algorithms.

    We start by addressing the problem of finding shortest paths for a vehicle in obstacle-free environments. This problem has been studied since the fifties, but the addressed vehicle models are often simplistic. We propose a novel algorithm that is able to plan paths respecting complex vehicle actuator constraints associated with the slow dynamics of heavy vehicles.

    Using the previous method, we tackle the motion planning problem in environments populated with obstacles. Lattice-based motion planners, a popular choice for this type of scenario, come with drawbacks related to the sub-optimality of solution paths, and the discretization of the goal state. We propose a novel path optimization method, which is able to significantly reduce both problems. The resulting optimized paths contain less oscillatory behavior and arrive precisely at arbitrary non-discretized goal states.

    We then study the problem of bus driving in urban environments. It is shown how this type of driving is fundamentally different than that of other vehicles, due to the chassis configuration with large overhangs. To successfully maneuver buses, distinct driving objectives need to be used in planning algorithms. Moreover, a novel environment classification scheme must be introduced. The result is a motion planning algorithm that is able to mimic professional bus driver behavior, resulting in safer driving and increased vehicle maneuverability.


  • Presentation: 2019-08-30 10:00 F11, Stockholm
    Li, Yibei
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Dynamic Optimization for Agent-Based Systems and Inverse Optimal Control2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This dissertation is concerned with three problems within the field of optimization for agent--based systems. Firstly, the inverse optimal control problem is investigated for the single-agent system. Given a dynamic process, the goal is to recover the quadratic cost function from the observation of optimal control sequences. Such estimation could then help us develop a better understanding of the physical system and reproduce a similar optimal controller in other applications. Next, problems of optimization over networked systems are considered. A novel differential game approach is proposed for the optimal intrinsic formation control of multi-agent systems. As for the credit scoring problem, an optimal filtering framework is utilized to recursively improve the scoring accuracy based on dynamic network information.

    In paper A, the problem of finite horizon inverse optimal control problem is investigated, where the linear quadratic (LQ) cost function is required to be estimated from the optimal feedback controller. Although the infinite-horizon inverse LQ problem is well-studied with numerous results, the finite-horizon case is still an open problem. To the best of our knowledge, we propose the first complete result of the necessary and sufficient condition for the existence of corresponding LQ cost functions. Under feasible cases, the analytic expression of the whole solution space is derived and the equivalence of weighting matrices is discussed. For infeasible problems, an infinite dimensional convex problem is formulated to obtain a best-fit approximate solution with minimal control residual, where the optimality condition is solved under a static quadratic programming framework to facilitate the computation.

    In paper B, the optimal formation control problem of a multi-agent system is studied. The foraging behavior of N agents is modeled as a finite-horizon non-cooperative differential game under local information, and its Nash equilibrium is studied. The collaborative swarming behaviour derived from non-cooperative individual actions also sheds new light on understanding such phenomenon in the nature. The proposed framework has a tutorial meaning since a systematic approach for formation control is proposed, where the desired formation can be obtained by only intrinsically adjusting individual costs and network topology. In contrast to most of the existing methodologies based on regulating formation errors to the pre-defined pattern, the proposed method does not need to involve any information of the desired pattern beforehand. We refer to this type of formation control as intrinsic formation control. Patterns of regular polygons, antipodal formations and Platonic solids can be achieved as Nash equilibria of the game while inter-agent collisions are naturally avoided.

    Paper C considers the credit scoring problem by incorporating dynamic network information, where the advantages of such incorporation are investigated in two scenarios. Firstly, when the scoring publishment is merely individual--dependent, an optimal Bayesian filter is designed for risk prediction, where network observations are utilized to provide a reference for the bank on future financial decisions. Furthermore, a recursive Bayes estimator is proposed to improve the accuracy of score publishment by incorporating the dynamic network topology as well. It is shown that under the proposed evolution framework, the designed estimator has a higher precision than all the efficient estimators, and the mean square errors are strictly smaller than the Cramér-Rao lower bound for clients within a certain range of scores.

  • Presentation: 2019-09-17 14:00 Q24, Stockholm
    Mäkivierikko, Aram
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
    A Needs-Based Approach towards Fostering Long-term Engagement with Energy Feedback among Local Residents2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In order to reach the current climate goals, energy consumption needs to decrease in all sectors, including households, which produce 20% of the European emissions. However, it is difficult to increase residents’ engagement in their household electricity consumption as it is an ‘invisible’ form of energy, the monetary incentives are often too small and environmental incentives are not very effective. Building on the idea that an engagement mechanism should be based on user needs, and recent research showing that social influence can be an effective way to affect consumption behaviour, this thesis examines the potential of a neighbourhood-based digital local social network providing feedback on household electricity consumption as an engagement solution. By helping neighbours to know each other better, such a network could meet the basic human need of belonging to a group, while also taking advantage of the social influence between neighbours to increase the effectiveness of the energy feedback provided.

    This thesis sought to: 1) Identify needs of residents that could be served by a local social network and explore whether such a network could provide a beneficial context for energy feedback; 2) identify and evaluate a set of design principles for energy feedback and use them to propose a prototype feedback design suitable for use in a local social network; and 3) design and implement a baseline study for measuring changes in aspects of social and environmental sustainability in a neighbourhood that introduction of a local social network can achieve, such as social cohesion, trust, safety, and energy attitudes and behaviour.

    In order to achieve these objectives, the Research Through Design methodology was used. This resulted in mixed methods research using quantitative (household survey) and qualitative (focus group interviews, stakeholder consultation workshop) methods. The research was conducted in two eco-districts in Stockholm, Sweden: Hammarby Sjöstad and Stockholm Royal Seaport.

    Regarding the first objective, results from the household survey indicated a need for increased interaction between neighbours in Stockholm Royal Seaport, while the focus group discussions revealed local communication needs that a local social network could meet. However, the possibility to use social influence between neighbours in increasing the intention to save energy was shown to be rather weak, possibly because of the current low level of connection between neighbours. Regarding the second objective, a set of design principles was identified using a literature study. They were used to create a design prototype of energy feedback that was presented to potential end-users in a stakeholder consultation workshop and then refined using suggestions given in the workshop. The workshop indicated support for many of the design principles as they were indirectly mentioned in the discussions. The design principle of fair feedback was further explored, suggesting use of typical household consumption as part of a fair comparison metric and when setting reduction goals.

    Regarding the third objective, an evaluation method with baseline survey and follow-up surveys was suggested. The household survey served as a baseline for measuring social and environmental sustainability aspects in a neighbourhood. Further research is needed on the effectiveness of a local social network as an engagement mechanism for energy feedback.