Crucially, this distribution of border points is agnostic of routing speed profiles. It’s based only on whether a road is passable or not. This means the same set of clusters and border points can be used for all car routing profiles (default, shortest, fuel-efficient) and all bicycle profiles (default, prefer flat terrain, etc.). Only the travel time/cost values of the shortcuts between these points change based on the profile. This is a massive factor in keeping storage down – map data only increased by about 0.5% per profile to store this HH-Routing structure!
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3月2日消息，英伟达同意向两家开发数据中心光学技术的企业总计投资40亿美元，这类技术对人工智能系统至关重要。英伟达周一分别发布声明称，将在多年期协议中分别向Lumentum和Coherent投资20亿美元。两项交易均包括采购协议以及先进激光组件的使用。相关资金将用于支持研发。