No abstract

This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently. Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions. The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98%of the time, while at the same time not being too passive. Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%.

No abstract

Protocols to create metal−organic framework (MOF)/polymer composites for separation, chemical capture, and catalytic applications currently rely on relatively slow solution-based processing to form single MOF composites. Here, we report a rapid, high-yield sorption-vapor method for direct simultaneous growth of single and multiple MOF materials onto untreated flexible and stretchable polymer fibers and films. The synthesis utilizes favorable reactant absorption into polymers coupled with rapid vapor-driven MOF crystallization to form high surface area (>250 m 2 /g composite ) composites, including UiO-66-NH 2 , HKUST-1, and MOF-525 on spandex, nylon, and other fabrics. The resulting composites are robust and maintain their functionality even after stretching. Stretchable MOF fabrics enable rapid solid-state hydrolysis of the highly toxic chemical warfare agent soman and paraoxon-methyl simulant. We show that this approach can readily be scaled by solution spray-coating of MOF precursors and to large area substrates.

No abstract