Kim and Hirtle (1995) suggested that knowledge of routes is represented as a sequence of intersection-based choice points where procedural decisions must be made, and Lupien et al. elaborate to point out that route complexity is contingent upon the number of intersection-based decision points (Lupien et al., 1998).
It estimates a sequence of intersections from the source vehicle to the destination vehicle.
A truth is presented by a sequence of intersections that every two consecutive intersections denote a road.
Some algorithms, such as GSR [13] and GPCR [14], determine a sequence of intersections to connect the source and the destination using the Dijkstra algorithm.
We use a sequence ([p_1, p_2, ldots, p_n]), which consists of a source, a destination, and a sequence of consecutive road intersections in-between, to represent a route.
Consequently, in order to address the influences from the above issues, a well-designed routing protocol often consists of two steps: (1) select an optimal route, consisting of a sequence of passed road intersections; (2) select the next hop, usually through greedy forwarding.
For Schnorr, the effective uniform measure zero sets should be defined as the intersection of a sequence of sets whose measures are not merely bounded by computable reals (as in section B.2), but are themselves computable reals.
In our method, we decompose surfaces of revolution into a sequence of coaxial revolute quadrics and reduce the intersection problem for two surfaces of revolution to the intersection problem for two revolute quadrics.
We then convert the equivalent multi-objective optimal control problem into a sequence of single-objective optimal switching control problems by using a modified normal boundary intersection method.
There is a sequence of steps.
