The capabilities of automated vehicles has increased over the last years, and different driving strategies have shown promising results on a variety of scenarios. However, there are still many challenges to be solved, and handling crowded roundabouts is one of them. This kind of scenario requires both safe and efficient maneuvers from the autonomous driving systems in order to maintain a proper traffic flow. This work presents a strategy to generate different speed profiles for a set of path candidates in order to obtain a merging maneuver according to current traffic scene. The proposed mechanism relies on the use of fictitious accelerations generated by leader and lag vehicles, incorporating by design comfort and safety bounds. The autonomous driving system proposed in this work was tested on realistic driving scenarios collected from public datasets and its performance was compared to human drivers on the same scenarios. The results showed in a variety of situations that the automated vehicle was capable of merging into roundabouts with tight merging gaps while maintaining both comfort and safety constraints.

Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design.

Motion planning is a process of constant negotiation with the rest of the traffic agents and is highly conditioned by their movement prediction. Indeed, an incorrect prediction could cause the motion planning algorithm to adopt overly conservative or reckless behaviors that can eventually become a dangerous driving situation. This paper presents a framework integrating motion planning and interaction-aware motion prediction algorithms, which interact with each other and are able to run in real-time on complex areas such as roundabouts or intersections. The proposed motion prediction strategy generates a multi-modal probabilistic estimation of the future positions and intentions of the surrounding vehicles by taking into account traffic rules, vehicle interaction, road geometry and the reference trajectory of the ego-vehicle; the resulting predictions are fed into a sampling-based maneuver and trajectory planning algorithm that identifies the possible collision points for every generated trajectory candidate and acts accordingly. This framework enables the automated driving system to have a more agile behavior than other strategies that use more simplistic motion prediction models and where the planning stage does not provide feedback. The approach has been successfully evaluated and compared with a state-of-art approach in highly-interactive scenarios generated from public datasets and real-world situations in a software-in-the-loop simulation system.

Autonomous vehicles will find an infinite number of possible scenarios while driving in urban environments and need to react in a proper manner. For that reason, it is important to have algorithms that can propose driving alternatives for different type of scenarios in a global and unified way instead of using rule-based algorithms which depend on the driving scene. This paper presents a reachability estimation algorithm designed to obtain a safe and comfort-optimized trajectory set for different driving scenarios. First, a finite number of path candidates are created using Bézler curves. Then, all valid path candidates are combined with the reachable sets of dynamic obstacles to generate speed profiles consistent with safety and comfort requirements. The output of this algorithm would allow a decision-making strategy to select the optimum candidate depending on different criteria.

This work presents a maneuver planner for automated vehicles, seamlessly integrated with a motion planner, that is able to make tactical decisions to safely navigate in urban environments. The proposed maneuver planner is able to select the most suitable target lane for the ego-vehicle and to manage different regulatory signals like right of way, must stop or pedestrian crossings. The integration with the motion planner allows to select the current maneuver after exploring a reachable set of trajectories, which brings flexibility to the system in complex and dynamic driving scenarios. The architecture was validated on a real vehicle in two urban scenarios with other traffic agents: an intersection with a pedestrian crossing and an overtaking scenario on a two-way road.

This paper presents the design and implementation of a complete communication system using the GMSK modulation scheme. The hardware is described in VHDL and implemented on Altera FPGA. Additionally, each block used to perform the implementation is completely described accomplishing all the requirements of this kind of modulation. Mueller & Müller algorithm is also performed to ensure timing synchronization between transmitter and receiver. This communication system was developed with educational purposes.