Turn-by-turn navigation

About: Turn-by-turn navigation is a research topic. Over the lifetime, 2243 publications have been published within this topic receiving 52838 citations.

Gps based fuel efficiency optimizer

Abstract: Routing options for fuel efficient use of a motor vehicle employing a GPS-based navigation system are computed based on travel information and external data. A navigation device receives route input from a driver or by recording a travelled route. External data such as traffic conditions, weather conditions, actual fuel consumption, road blockages, traffic light configurations, and the like is received from one or more networked information sources. A fuel efficient route based on the original route and the received external data is computed for the whole route or individual sections between stops on the route for display to the driver. Fuel efficient route(s) may be recomputed with up-to-date external data upon driver request, predefined intervals, or updates to external data.

Efficient data mining for web navigation patterns

Abstract: The concept of preference is proposed on the analysis of the present algorithms for mining user navigation patterns. It is based on the following hypothesis: if there are many different selections to leave a page, those selections that occur more frequently and the next page is viewed longer reveal user interest and preference. Representing user navigation interest and intention accurately by comparing relatively access ratio and the average of relatively access ratio of viewing time and selective intention, preference can be used for mining user navigation pattern instead of confidence. The higher preference, the more prefer to choose the selection. According to the conception, we propose two efficient algorithms based on the concept, UAM and PNT, which are developed for mining user preferred navigation patterns. Considering the structure of Web site, UAM can get user access preferred path by the page–page transition probabilities statistics of all users behaviours. PNT looks far into the past to correctly discriminate the different behavioral modes of the different users. Experiments show accuracy and scalability of the algorithms. It is suitable for applications in E-business, such as to optimize Web site or to design personalized service.

Navigation assistance method and system

Abstract: In a navigation assistance system (figs. 1-3), information is received via the Internet (16) from a user (18a...1Sn) pertaining to water tri contemplated by the user. The system includes a database (12) storing waterway data. The database is accessed in response to the information from the user. At least one recommended over-water route is derived in response to the user information and in accordance with waterway data in the database (12), the recommended route being communicated to the user via the computer network (through item 24). The deriving of the route includes only route segments extending through operatively navigable areas (figs. 4-9).

SAINT: Self-Adaptive Interactive Navigation Tool for Cloud-Based Vehicular Traffic Optimization

Abstract: This paper proposes a self-adaptive interactive navigation tool (SAINT), which is tailored for cloud-based vehicular traffic optimization in road networks. The legacy navigation systems make vehicles navigate toward their destination less effectively with individually optimal navigation paths rather than network-wide optimal navigation paths, particularly during rush hours. To the best of our knowledge, SAINT is the first attempt to investigate a self-adaptive interactive navigation approach through the interaction between vehicles and vehicular cloud. The vehicles report their navigation experiences and travel paths to the vehicular cloud so that the vehicular cloud can know real-time road traffic conditions and vehicle trajectories for better navigation guidance for other vehicles. With these traffic conditions and vehicle trajectories, the vehicular cloud uses a mathematical model to calculate road segment congestion estimation for global traffic optimization. This model provides each vehicle with a navigation path that has minimum traffic congestion in the target road network. Using the simulation with a realistic road network, it is shown that our SAINT outperforms the legacy navigation scheme, which is based on Dijkstra's algorithm with a real-time road traffic snapshot. On a road map of Manhattan in New York City, our SAINT can significantly reduce the travel delay during rush hours by 19%.

Extracting landmarks for car navigation systems using existing gis databases and laser scanning

Abstract: Today’s car navigation systems provide driving instructions in the form of maps, pictograms, and spoken language. However, they are so far not able to support landmark-based navigation, which is the most natural navigation concept for humans and which also plays an important role for upcoming personal navigation systems. In order to provide such a navigation, the first step is to identify appropriate landmarks – a task that seems to be rather easy at first sight but turns out to be quite pretentious considering the challenge to deliver such information for databases covering huge areas of Europe, Northern America and Japan. In this paper, we show approaches to extract landmarks from existing GIS databases. Since these databases in general do not contain information on building heights and visibility, we show how this can be derived from laser scanning data.