Social on the road: enabling secure and efficient social networking on highways

 This article presents SOR, a vehicular social network to enable social communications and interactions among users on the road during their highway travels. Motivated by the limited connection to Internet contents and services, the essential goal of SOR is to encourage distributed users on the road to spontaneously contribute as the information producer, assembler, and distributer in order to provide timely and localized infotainments to each other through low-cost inter-vehicle communications. To be specific, SOR enables individual users to maintain a personal blog, similar to one on Facebook and Twitter, over which users can create and share personal content information to the public such as travel blogs with pictures and videos. By accessing each other's SOR blogs and commenting on interesting topics, passengers can exchange messages and initiate social interactions. In the specific highway environment, SOR addresses two challenges in the context of vehicular social communications. First, vehicular social communications tend to be frequently interrupted by diverse vehicle mobility and intermittent intervehicle connections, which is annoying to users. To address this issue, SOR adopts a proactive mechanism by estimating the connection time between peer vehicles, and recommending vehicles with relatively long-lasting and stable intervehicle connections for social communications. Second, as users on the road are typically strangers to each other, they are reluctant to disclose personal information to others. This makes it challenging to identify users of shared interests and accordingly restricts the scale of users' social interactions. To remedy that, SOR provides a secured solution to protect sensitive user information during social communications. Lastly, we use simulations to verify the performance of SOR. © 2015 IEEE.

Fast road detection and tracking in aerial videos

 We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.

Do the numbers and locations of road-killed anuran carcasses accurately reflect impacts of vehicular traffic?

Road-killed animals are easy and inexpensive to survey, and may provide information about species distributions, abundances, and mortality rates. As with any sampling method, however, we need to explore methodological biases in such data. First, how does an animal's behavior (e.g., use of the center vs. periphery of the road) influence its vulnerability to vehicular traffic? Second, how rapidly do post-mortem processes (scavenging by other animals, destruction or displacement by subsequent vehicles) change the numbers and locations of roadkills? Our surveys of anurans on a highway in tropical Australia show that different anuran species are distributed in different ways across the width of the road, and that locations of live versus dead animals sometimes differ within a species. Experimental trials show that location on the road affects the probability of being hit by a vehicle, with anurans in the middle of the road begin hit 35% more often than anurans on the edges; thus, center-using species are more likely to be hit than edge-using taxa. The magnitude of post-mortem displacement and destruction by subsequent vehicles depended on anuran species and body size. The mean parallel displacement distance was 122.7 cm, and carcasses of thin-skinned species exhibited greater post-mortem destruction. Scavenging raptors removed 73% of carcasses, most within a few hours of sunrise. Removal rates were biased with respect to size and species. Overall, our studies suggest that investigators should carefully evaluate potential biases before using roadkill counts to estimate underlying animal abundances or mortality rates.