Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs)has been receiving increasing attention due to enabling on-demand, intelligent traffic analysis and response to real-time traffic issues. One of these patterns, sequential patterns, are a type of behavioral patterns that describe the occurence of events in a timely-ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination, forming a vehicular path. Due to their predictable nature, undertaken vehicular paths can be exploited to extract the paths that are considered frequent. From the extracted frequent paths through data mining, the probability that a vehicular path will take a certain direction is obtained. However, in order to achieve this, samples of vehicular paths need to be initially collected over periods of time in order to be data-mined accordingly. In this thesis, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: Road Side Unit-Triggered (RSU-Triggered) and Vehicle-Triggered. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the e ciency and e ectiveness of the proposed schemes, extensive experimental analysis has been realized. From the results, two of the Vehicle-Triggered schemes, VTB-FP and VTRD-FP, have improved the vehicular path collection operation in terms of communication cost and latency over others. In terms of reliability, the Vehicle-Triggered schemes achieved a higher success rate than the RSU-Triggered scheme. Finally, frequent vehicular movement patterns have been effectively extracted from the collected vehicular paths according to a user-de ned threshold and the confidence of generated movement rules have been measured. From the analysis, it was clear that the user-de ned threshold needs to be set accordingly in order to not discard important vehicular movement patterns.

Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs)has been receiving increasing attention due to enabling on-demand, intelligent traffic analysis and response to real-time traffic issues. One of these patterns, sequential patterns, are a type of behavioral patterns that describe the occurence of events in a timely-ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination, forming a vehicular path. Due to their predictable nature, undertaken vehicular paths can be exploited to extract the paths that are considered frequent. From the extracted frequent paths through data mining, the probability that a vehicular path will take a certain direction is obtained. However, in order to achieve this, samples of vehicular paths need to be initially collected over periods of time in order to be data-mined accordingly. In this thesis, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: Road Side Unit-Triggered (RSU-Triggered) and Vehicle-Triggered. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the e ciency and e ectiveness of the proposed schemes, extensive experimental analysis has been realized. From the results, two of the Vehicle-Triggered schemes, VTB-FP and VTRD-FP, have improved the vehicular path collection operation in terms of communication cost and latency over others. In terms of reliability, the Vehicle-Triggered schemes achieved a higher success rate than the RSU-Triggered scheme. Finally, frequent vehicular movement patterns have been effectively extracted from the collected vehicular paths according to a user-de ned threshold and the confidence of generated movement rules have been measured. From the analysis, it was clear that the user-de ned threshold needs to be set accordingly in order to not discard important vehicular movement patterns.

Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs)has been receiving increasing attention due to enabling on-demand, intelligent traffic analysis and response to real-time traffic issues. One of these patterns, sequential patterns, are a type of behavioral patterns that describe the occurence of events in a timely-ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination, forming a vehicular path. Due to their predictable nature, undertaken vehicular paths can be exploited to extract the paths that are considered frequent. From the extracted frequent paths through data mining, the probability that a vehicular path will take a certain direction is obtained. However, in order to achieve this, samples of vehicular paths need to be initially collected over periods of time in order to be data-mined accordingly. In this thesis, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: Road Side Unit-Triggered (RSU-Triggered) and Vehicle-Triggered. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the e ciency and e ectiveness of the proposed schemes, extensive experimental analysis has been realized. From the results, two of the Vehicle-Triggered schemes, VTB-FP and VTRD-FP, have improved the vehicular path collection operation in terms of communication cost and latency over others. In terms of reliability, the Vehicle-Triggered schemes achieved a higher success rate than the RSU-Triggered scheme. Finally, frequent vehicular movement patterns have been effectively extracted from the collected vehicular paths according to a user-de ned threshold and the confidence of generated movement rules have been measured. From the analysis, it was clear that the user-de ned threshold needs to be set accordingly in order to not discard important vehicular movement patterns.

With an imminent future of fully autonomous heavy duty vehicles in offroad applications, the need for advanced control system will increase accordingly. By implementing a raster map with tiles and pixels, to which a vehicle can record slope and position data while driving, it is possible to map earth's surface. By adding a heat parameter to every pixel, i.e. the number of passings through it historically, it is possible in future visits of the same are to generate a route prediction made up by the "hottest" pixels. By fetching the slope values in the hottest pixels, it is possible to generate a slope horizon that can be utilized by control systems, e.g. when planning gear shifts in hilly offroad terrain. To mange the incrementally growing map, a memory management system was implemented. It buffers the relevant map data from the database, i.e. the vehicles closest surroundings, which is then used for route prediction and horizon generation. As the vehicle moves into other areas, new data is read from the database, and the recently passed area is written back to the database, however updated from the recent passing. The system is implemented so that it runs through another application in the telematics electrical controller unit (ECU) in a Scania vehicle. The ECU contains a GNSS module from which the vehicle fetches satellite positioning data. Slope data is fetched from a slope sensor mounted on the truck. Due to implications during testing and debugging of the resulting application developed and implemented during this thesis project, the application's performance couldn't be assessed properly. However, it is concluded that the background the application is built on is reliable, although tweaks to get the application fit for usage in offroad terrain had to be made. Mainly, the horizon length and map building techniques should not be the same as in similar applications for onroad driving.