Vehicles enlisted with computing, sensing and communicating devices can create vehicular networks, a subset of cooperative systems in heterogeneous environments, aiming at improving safety and entertainment in traffic...Vehicles enlisted with computing, sensing and communicating devices can create vehicular networks, a subset of cooperative systems in heterogeneous environments, aiming at improving safety and entertainment in traffic. In vehicular networks, a vehicle＇s identity is associated to its owner＇s identity as a unique linkage. Therefore, it is of importance to protect privacy of vehicles from being possibly tracked. Obviously, the privacy protection must be scalable because of the high mobility and large population of vehicles. In this work, we take a non-trivial step towards protecting privacy of vehicles. As privacy draws public concerns, we firstly present privacy implications of operational challenges from the public policy perspective. Additionally, we envision vehicular networks as geographically partitioned subnetworks （cells）. Each subnetwork maintains a list of pseudonyms. Each pseudonym includes the cell＇s geographic id and a random number as host id. Before starting communication, vehicles need to request a pseudonym on demand from pseudonym server. In order to improve utilization of pseudonyms, we address a stochastic model with time-varying arrival and departure rates. Our main contribution includes： 1） proposing a scalable and effective algorithm to protect privacy; 2） providing analytical results of probability, variance and expected number of requests on pseudonym servers. The empirical results confirm the accuracy of our analytical predictions.展开更多
One of the main drivers for intelligent transportation systems is safety. Adaptive cruise control, as a common solution for traffic safety, lias extended from radars to cameras. Due to high mobility of vehicles and un...One of the main drivers for intelligent transportation systems is safety. Adaptive cruise control, as a common solution for traffic safety, lias extended from radars to cameras. Due to high mobility of vehicles and unevenness of roads, the picture quality of cameras has been great challenges for camera-based adaptive cruise control. In this paper, an image distortion correction algorithm is addressed. Our method is based on optical flow technology which is normally applied in motion estimation and video compression research. We are the first to attempt to adapt it in image distortion correction. Two optical flow approaches, the Lucas-Kanade method and the Horn-Schunck method, are selected and compared. The procedure of image distortion correction using the optical flow method has been tested by both synthetic test images and camera images. The experimental results show that the Lucas-Kanade method is more suitable in the correction of image distortion.展开更多
文摘Vehicles enlisted with computing, sensing and communicating devices can create vehicular networks, a subset of cooperative systems in heterogeneous environments, aiming at improving safety and entertainment in traffic. In vehicular networks, a vehicle＇s identity is associated to its owner＇s identity as a unique linkage. Therefore, it is of importance to protect privacy of vehicles from being possibly tracked. Obviously, the privacy protection must be scalable because of the high mobility and large population of vehicles. In this work, we take a non-trivial step towards protecting privacy of vehicles. As privacy draws public concerns, we firstly present privacy implications of operational challenges from the public policy perspective. Additionally, we envision vehicular networks as geographically partitioned subnetworks （cells）. Each subnetwork maintains a list of pseudonyms. Each pseudonym includes the cell＇s geographic id and a random number as host id. Before starting communication, vehicles need to request a pseudonym on demand from pseudonym server. In order to improve utilization of pseudonyms, we address a stochastic model with time-varying arrival and departure rates. Our main contribution includes： 1） proposing a scalable and effective algorithm to protect privacy; 2） providing analytical results of probability, variance and expected number of requests on pseudonym servers. The empirical results confirm the accuracy of our analytical predictions.
文摘One of the main drivers for intelligent transportation systems is safety. Adaptive cruise control, as a common solution for traffic safety, lias extended from radars to cameras. Due to high mobility of vehicles and unevenness of roads, the picture quality of cameras has been great challenges for camera-based adaptive cruise control. In this paper, an image distortion correction algorithm is addressed. Our method is based on optical flow technology which is normally applied in motion estimation and video compression research. We are the first to attempt to adapt it in image distortion correction. Two optical flow approaches, the Lucas-Kanade method and the Horn-Schunck method, are selected and compared. The procedure of image distortion correction using the optical flow method has been tested by both synthetic test images and camera images. The experimental results show that the Lucas-Kanade method is more suitable in the correction of image distortion.
