Hierarchical model of road network for route planning in vehicle navigation systems

2009 ◽  
Vol 1 (2) ◽  
pp. 20-24 ◽  
Author(s):  
Qingquan Li ◽  
Zhe Zeng ◽  
Bisheng Yang
Keyword(s):  
Hierarchical Model ◽  
Road Network ◽  
Route Planning ◽  
Vehicle Navigation ◽  
Navigation Systems

scholarly journals The Use of Quick Response (QR) Codes in Landmark-Based Pedestrian Navigation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Anahid Basiri ◽  
Pouria Amirian ◽  
Adam Winstanley
Keyword(s):  
Mobile Device ◽  
Road Network ◽  
Vehicle Navigation ◽  
Qr Code ◽  
Navigation Systems ◽  
Quick Response ◽  
Qr Codes ◽  
Pedestrian Navigation

Vehicle navigation systems usually simply function by calculating the shortest fastest route over a road network. In contrast, pedestrian navigation can have more diverse concerns. Pedestrians are not constrained to road/path networks; their route may involve going into buildings (where accurate satellite locational signals are not available) and they have different priorities, for example, preferring routes that are quieter or more sheltered from the weather. In addition, there are differences in how people are best directed: pedestrians noticing landmarks such as buildings, doors, and steps rather than junctions and sign posts. Landmarks exist both indoors and outdoors. A system has been developed that uses quick response (QR) codes affixed to registered landmarks allowing users to localise themselves with respect to their route and with navigational instructions given in terms of these landmarks. In addition, the system includes images of each landmark helping users to navigate visually in addition to through textual instructions and route maps. The system runs on a mobile device; the users use the device’s camera to register each landmark’s QR code and so update their position (particularly indoors) and progress through the route itinerary.

scholarly journals Personalized Route Planning System Based on Driver Preference

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 11
Author(s):  
Ren Wang ◽  
Mengchu Zhou ◽  
Kaizhou Gao ◽  
Ahmed Alabdulwahab ◽  
Muhyaddin J. Rawa
Keyword(s):  
Road Network ◽  
Route Planning ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Planning System ◽  
Search Process ◽  
Navigation Systems ◽  
Optimal Route ◽  
Computation Cost ◽  
Route Navigation

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

QARTA

2021 ◽  
Vol 14 (11) ◽  
pp. 2273-2282
Author(s):  
Mashaal Musleh ◽  
Sofiane Abbar ◽  
Rade Stanojevic ◽  
Mohamed Mokbel
Keyword(s):  
Machine Learning ◽  
Road Network ◽  
Contextual Information ◽  
Time Of Day ◽  
Food Delivery ◽  
Machine Learning Techniques ◽  
Navigation Systems ◽  
Query Result ◽  
Learning Techniques ◽  
Map Service

Maps services are ubiquitous in widely used applications including navigation systems, ride sharing, and items/food delivery. Though there are plenty of efforts to support such services through designing more efficient algorithms, we believe that efficiency is no longer a bottleneck to these services. Instead, it is the accuracy of the underlying road network and query result. This paper presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to construct its own highly accurate map, not only in terms of map topology but more importantly, in terms of edge weights. QARTA also employs machine learning techniques to calibrate its query answers based on contextual information, including transportation modality, location, and time of day/week. QARTA is currently deployed in all Taxis and the third largest food delivery company in the State of Qatar, replacing the commercial map service that was in use, and responding in real-time to hundreds of thousands of daily API calls. Experimental evaluation of QARTA shows its comparable or higher accuracy than commercial services.

SatNav or SatNag?

2011 ◽  
pp. 314-322
Author(s):  
G. E. Burnett
Keyword(s):  
Full Range ◽  
Future Research ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Computing Systems ◽  
Positive Direction ◽  
Driving Experience ◽  
Wide Range ◽  
Driving Task

A wide range of in-car computing systems are either already in existence or under development which aim to improve the safety, efficiency and the comfort/pleasure of the driving experience. Several unique forces act on the design process for this technology which must be understood by HCI researchers. In particular, this is an area in which safety concerns dominate perspectives. In this position paper, I have used a case study system (vehicle navigation) to illustrate the evolution of some key HCI design issues that have arisen in the last twenty years as this in-car technology has matured. Fundamentally, I argue that, whilst HCI research has had an influence on current designs for vehicle navigation systems, this has not always been in a wholly positive direction. Future research must take a holistic viewpoint and consider the full range of impacts that in-car computing systems can have on the driving task.

A Simplified Real-Time Road Network Model Considering Intersection Delay and its Application on Vehicle Navigation

2011 ◽  
Vol 58-60 ◽  
pp. 1959-1965 ◽  
Author(s):  
Zheng Yu Zhu ◽  
Wei Liu ◽  
Lin Liu ◽  
Ming Cui ◽  
Jin Yan Li
Keyword(s):  
Real Time ◽  
Network Model ◽  
Road Network ◽  
Intelligent Transportation System ◽  
Optimal Path ◽  
Network Models ◽  
Traffic Information ◽  
Vehicle Navigation ◽  
Traffic Data ◽  
New Model

The complexity of a real road network structure of a city and the variability of its real traffic information make a city’s intelligent transportation system (ITS) hard to meet the needs of the city’s vehicle navigation. This paper has proposed a simplified real-time road network model which can take into account the influence of intersection delay on the guidance for vehicles but avoid the calculation of intersection delay and troublesome collection of a city’s traffic data. Based on the new model, a navigation system has been presented, which can plan a dynamic optimal path for a vehicle according to the real-time traffic data received periodically from the city’s traffic center. A simulated experiment has been given. Compared with previous real-time road network models, the new model is much simpler and more effective on the calculation of vehicle navigation.

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