Efficient testing and validation of software components for highly automate vehicles is one of the key challenges to be solved for their massive deployment. The number of driving situation and environment variables makes validation almost intractable with real vehicles in open roads, and the testing reproducibility can only be achieved via simulation. This manuscript presents a framework and preliminary results for motion prediction of vehicles in a simulation environment that is being currently developed by the AUTOPIA Program.
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.
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 Bézier 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.
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