Congestion pricing is widely used to improve transportation network efficiency where individual decentralized route choices can lead to system-level inefficiencies. Pigouvian tolls achieve the system optimum but depend on demand and can lose effectiveness under demand shifts, particularly on structurally critical bottl...
Sheryl Paul, Samuel Williams, Ke-Xin Wang et al.· 0 citations
Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, hete...
Sheryl Paul, Ke-Xin Wang, Ruo-Lin Li et al.· 0 citations
Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints e...
Yi-Qi Zhao, Taekyung Kim, Hideki Okamoto et al.· 0 citations
Video2STL is presented, a framework that converts observation-only videos into parametric Signal Temporal Logic (STL) specifications and uses the resulting formal representation for robot learning, and supports cross-embodiment transfer from human or animal videos to robot control.
Merve Atasever, Keyan Azbijari, Cagan Bakirci et al.· 0 citations
Two encodings of the problem of planning multi-agent paths that satisfy constraints written in STL-GO are presented, one based on mixed-integer programming (MIP) and another based on satisfiability modulo theory (SMT), with soundness guarantees.
This study presents a comparative case study of representative motion planning methods drawn from major benchmark ecosystems, including CARLA, nuPlan, and the Waymo Open Dataset to highlight the strengths and weaknesses of current approaches.
Merve Atasever, Alfredo Reina Corona, Zhuo-Chen Liu et al.· 1 citation
This work studies whether large language models (LLMs) can fill this gap by generating Parametric Signal Temporal Logic (PSTL) specifications that are subsequently used for policy learning, and instantiate the parameters of the generated PSTL templates using expert trajectories and retain only specifications that are c...
Merve Atasever, Keyan Azbijari, Cagan Bakirci et al.· 0 citations
Logic-VLA is introduced, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.
Celina Shiyu Wang, Yiqi Zhao, Junjie Ye et al.· 1 citation
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