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With more and more trajectory traces available, conducting analysis and mining on those trajectories can obtain valuable information. Although the published traces are often made anonymous by substituting the true identities of mobile nodes with random identifiers, the privacy concern remains. In this paper, we propose a new de-anonymization attack based on the movement pattern of moving objects....
Big trajectory data introduces severe challenges for data storage and communication. In this paper, we propose a novel compression framework called Clockwise Compression Framework (CCF) for big trajectory data compression under road network constraints. In CCF, we design several new methods: 1) a spatial compression algorithm called Enhanced Clockwise Encoding (ECE), 2) a temporal compression algorithm...
Recent advances in vehicular networks, GPS and smartphone technologies have changed the paradigm of intelligent taxicab systems. Indeed, taxicab trajectories and online calling information have enabled us to provide more efficient and personalized services. However, existing approaches are not sufficient in exploiting cooperative scheduling techniques and utilizing real time calling information. To...
Acquiring accurate location information of vehicles is of great importance. Global Positioning System (GPS) has been widely deployed and used to be the most convenient solution to outdoor localization. As more and more infrastructure such as elevated roads, tunnels and tall buildings is built, however, the ever-increasing complexity of urban environments makes vehicle localization especially in those...
Since GPS signal is not applicable indoors, vehicle tracking has proven a hassle in underground parking structures. Recent solutions highly rely on floor map to constraint inertial sensors noises. In this paper, we propose VeMap, a road map construction system using only smartphones inside vehicles. It saves effort-intensive and time-consuming business negotiations with building operators, and expensive...
A trajectory is a polygonal line consisting of the positions that a moving object occupies as time passes, and as such, it can be derived by periodically sampling the positions of the object. In this manner, and due to the proliferation of location-sensing devices, it has been possible to create large datasets of trajectories. Using these datasets it is possible to derive much information about the...
Trajectories contain considerable routing information, and trajectory-based routing gains the attention of researchers recently. This paper argues the problem of searching time period Longest Frequent Path(TPLFP). The TPLFP, as one of the reasonable alternative Most Frequent Path (MFP), is close to MFP enough and maximizes the number of frequent route segments. First, we define TPLFP formally. To...
Recent advances in location-acquisition and mobile sensing technologies have enabled tracking of vehicle movements (i.e., trajectory data). Massive trajectory datasets are processed routinely (often in real-time) to provide support for many new types of IoV (Internet of Vehicles) applications (e.g., traffic congestion management, and load-coordination across electric vehicle charging stations). High-volume,...
While research on time-varying graphs has attracted recent attention, the research community has limited or no access to real datasets to develop effective algorithms and systems. Using noisy and sparse GPS traces from vehicles, we develop a time-varying road network data set where edge weights differ over time. We present our methodology and share this dataset, along with a graph manipulation tool...
This article presents a large scale and long duration experiment as part of the French FUI VipaFleet project. A driverless shuttle has been operated for three month on an industrial site, totaling nearly 1500 km of autonomous travel and 300 passengers transported. The localization relies mainly on a multi camera system and a visual SLAM algorithm. Besides the vision algorithms themselves, this article...
Map-matching is the problem consisting of matching a sequence of geographic coordinates with the roads on a digital map, aiming to discover the actual path traveled by that trajectory. Common uses of map-matching include traffic analysis and flow density extraction, which rely on such algorithms as a primary stage of their processing. Here we present DMM, a distributed solution for large-scale trajectory...
Real-time path planning constitutes one of the hot topics when developing automated driving. Different path planning techniques have been studied in both the robotics and automated vehicles fields trying to improve the trajectory generation and its tracking. In this paper, a novel local path planning algorithm combining both off-line and real-time generation for automated vehicles in urban environments...
Recently, a number of promising path planning algorithms, including sampling-based RRT, grid-based Hybrid-A*, have been proposed to navigate autonomous vehicles in complex environments. However, the drawbacks of these algorithms make them insufficient for path planning when used separately in real-world traffic scenarios. Thus, it is crucial to build a unified framework that can combine the merits...
The paper at hand proposes a real-time capable approach to combined trajectory planning and control. One single prediction model is used to plan a feasible trajectory and to perform lateral guidance of the vehicle at the same time. Nonlinear model predictive control (NMPC) methods are applied to solve the optimal control problem, which incorporates environmental constraints leading to a model predictive...
We propose a method for ego-lane estimation that can robustly determine the currently used lane as required by future lane-precise navigation systems. It employs a lane-level map-matching on a digital road map through least-squares optimization and only requires sensors available in current production vehicles, such as GPS, odometry, visual lane-marking detection and radars. Radar data is used in...
In many traffic situations there are times where interaction with other drivers is necessary and unavoidable in order to safely progress towards an intended destination. This is especially true for merge manoeuvres into dense traffic, where drivers sometimes must be somewhat aggressive and show the intention of merging in order to interact with the other driver and make the driver open the gap needed...
A framework for the discretization of navigable space within and around a cross intersection is proposed in this paper. The purpose of our approach is to capture the manner in which drivers manoeuvre in an intersection in order to facilitate and understand the decision-making tasks. Gaussian processes are used to learn and predict the most likely trajectories taken by multiple drivers in different...
The research community has shown significant improvements in both vision-based detection and tracking of vehicles, working towards a high level understanding of on-road maneuvers. Behaviors of surrounding vehicles in a highway environment is found as an interesting starting point, of why this dataset is introduced along with its challenges and evaluation metrics. A vision-based multi-perspective dataset...
Global Positioning System (GPS) which is a part of the Geographical Information System(GIS) is used for the efficient mapping of the traffic trajectories in the real world scenario.It uses GPS logs of roadways which is useful in the routing applications like Smart Maps. The historical GPS logs which is a collection of numerous GPS points are provided to find the landmark graph. Beyond that the live...
The paper addresses the operational reliability and safety of automated guided vehicles along roadways. These operations most often require very tight integration of environmental sensing and vehicle dynamics control. This contribution relies on determining the relevant lane boundaries using conventional camera sensor and proposes a constrained longitudinal and lateral control strategy for fully automated...
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