Titlebook: Decision-making Strategies for Automated Driving in Urban Environments; Antonio Artu?edo Book 2020 The Editor(s) (if applicable) and The A

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Decision-Making Architecture,to have the expected behaviour in the driving scenarios for which it has been designed. These driving scenarios are more and more demanding as they include a growing number of inter-connected and heterogeneous agents and the associated caseload may become intractable. Thus, the design of the archite

Optimum

Optimum

Motion Prediction and Manoeuvre Planning,caused by the impossibility to understand the nearby environment, predict future states, and act consequently by human drivers in critical situations where the behavior of traffic scene agents is highly unpredictable and the time to act is very low. In this sense, the anticipation to future driving

Spinal-Tap

Optimal Trajectory Generation,scenarios. Whilst much progress has been attained in the perception and localization domains, digital representations of the world are still incomplete. As a result, understanding the spatio-temporal relationship between the subject vehicle and the relevant entities whilst constrained by the road ne

conceal

Conclusions,ex driving situations in a safe and human-like manner. In this regard, this thesis addresses the problem of motion planning in urban environments by proposing a decision-making and planning architecture where several modules with different functionalities provide human-like trajectories to the vehic

Hectic

Global Planning and Mapping, the decision-making architecture as shown in Fig.?3.4. The proposed approach relies on OSM data to firstly use a global planner to find a route that reach a destination point set through the HMI. After that, it is computed a road corridor in which the vehicle will be able to drive.