urban traffic control teams

Abstract: We study the $k$-Canadian Traveller Problem (k-ctp), an online navigation problem introduced by Papadimitriou and Yannakakis in 1991. Given an undirected weighted graph $G=(V,E,ω)$ and two vertices $s,t\in V$, a traveller aims at navigating from $s$ to $t$ while minimizing the total distance travelled. However, up to $k$ edges of the graph may be blocked, and the traveller discovers that an edge is blocked only upon reaching one of its endpoints. The performance of an online algorithm for k-ctp is measured with the competitive ratio, which is the quotient between the distance actually traversed and the distance traversed by an omniscient traveller who knows all blockages in advance. A general lower bound $2k+1$ for the competitive ratio of deterministic algorithms remains valid even when the input graph is series-parallel, whereas constant competitive ratios can be achieved on restricted graph classes, such as cycles or unit-weighted outerplanar graphs. In this paper, we establish the first intermediate competitive ratio between constant and linear regimes. We prove that, on unit-weighted solid grid graphs, there exists an online algorithm with competitive ratio $O(\sqrt{k})$. This ratio is asymptotically tight and cannot be defeated, even on unit-weighted square grids. Finally, we show that both the unit-weighted and solidity assumptions are essential: going further with arbitrary edge weights (even on square grids) or non-solid unit-weighted grid structures yields a linear lower bound of $Ω(k)$.

Abstract: Vision-language models (VLMs) have demonstrated strong scene understanding and semantic judgment across diverse tasks, but their appropriate role in cooperative perception remains unclear. Directly asking a VLM to regress 3D detections is unreliable and computationally expensive, whereas using it to select the output of a single source discards useful information from other agents. We introduce VeriFuse, a bounded arbitration framework for vehicle-infrastructure cooperative 3D detection. Each agent first produces detections independently. Around each vehicle and roadside proposal, VeriFuse generates source-conditioned geometric candidates and combines the original detections, their perturbations, and cross-source hypotheses into a unified candidate pool. A frozen VLM then chooses among three admissible actions: SELECT an adequate candidate; REFINE an existing anchor when an object is supported but all candidates are geometrically inadequate; or REJECT an unsupported infrastructure-only proposal. Experiments on the DAIR-V2X dataset show that VeriFuse achieves 0.494/0.357 cooperative 3D AP50/AP70 and limits the relative vehicle-side BEV AP50 drop under a 300 ms delay to 1.7%. Overall, VeriFuse assigns the VLM a clear and constrained role in cooperative perception: semantic reasoning resolves ambiguity among cross-agent hypotheses, while deterministic constraints determine the final 3D geometry.