Ungulate behavioral responses to the heterogeneous road-network of a touristic protected area in Africa

Understanding how wildlife responds to road and traffic is essential for effective conservation. Yet, not many studies have evaluated how roads influence wildlife in protected areas, particularly within the large iconic African National Parks where tourism is mainly based on sightings from motorized vehicles with the consequent development and intense use of roads. To reduce this knowledge gap, we studied the behavioral response and local spatial distribution of impala Aepyceros melampus along the heterogeneous (with variation in road surface type and traffic intensity) road-network of Kruger National Park (KNP, South Africa). We surveyed different types of roads (paved and unpaved) recording the occurrence of flight responses among sighted impala and describing their local spatial distribution (in relation to the roads). We observed relatively few flight responses (19.5% of 118 observations), suggesting impalas could be partly habituated to vehicles in KNP. In addition, impala local distribution is apparently unaffected by unpaved roads, yet animals seem to avoid the close proximity of paved roads. Overall, our results suggest a negative, albeit small, effect of traffic intensity, and of presence of pavement on roads on the behavior of impala at KNP. Future studies would be necessary to understand how roads influence other species, but our results show that even within a protected area that has been well-visited for a long time, wildlife can still be affected by roads and traffic. This result has ecological (e.g., changes in spatial distribution of fauna) and management implications (e.g., challenges of facilitating wildlife sightings while minimizing disturbance) for protected areas where touristic activities are largely based on driving.

Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of Sao Paulo, Brazil, ozone and particulate matter ( PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors ( nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in Sao Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 mu g km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in Sao Paulo tunnels are higher than those found in other cities of the world.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

At home on the road? Can drivers' relationships with their cars be associated with territoriality?

The current research explores the relationship between people and their cars within the framework of Altman's theory of human territoriality. It further develops the research of Sandqvist by examining the descriptions given by people with differing ownership and uses of their cars and exploring the congruence between these and the characteristics used to describe human territories. Thirteen focus groups were held with young drivers between the ages of 18 and 25 years, drivers over the age of 25 who are parents of pre-license age children, drivers over the age of 25 who do not regularly transport children, and drivers of work vehicles. Analyses of discussions revealed that drivers’ descriptions of the relationship with their car could be matched with Brown and Altman's descriptions of territory types. However, variations existed both between and within individuals as to the application of the labels ‘primary’, ‘secondary’ and ‘public’ territory to the car. Implications for the understanding of road user behaviour and the further development of theory on the car as a place or an object in terms of territoriality are discussed.

Visual tracking of vehicles using multiresolution analysis and neural network

This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.

Detection of vehicles with monolithic classifier vis-à-vis a boosted cascaded classifier

This paper describes the comparison of accuracy and performance of two machine learning approaches for visual object detection and tracking vehicles, from an on-road image sequence. The first is a neural network based approach. Where an algorithm of multi resolution technique based on Haar basis functions was used to obtain an image with different scales. Thereafter a classification was carried out with the multilayer feed forward neural network. Principle Component Analysis (PCA) technique was used as a dimension reduction technique to make the classification process much more efficient. The second approach is based on boosting which also yields very good detection rates. In general, boosting is one of the most important developments in classification methodology. It works by sequentially applying a classification algorithm to reweighed versions of the training data, followed by taking a weighted majority vote of the sequence of classifiers thus produced. For this work, a strong classifier was trained by the adaboost algorithm. The results of comparing the two methodologies visà-vis shows the effectiveness of the methods that have been used.

A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.

A look-ahead road grade determination method for HEVs

This paper presents a look-ahead road grade determination method for use in energy management of hybrid electric vehicles. Data that is gathered from a digital map and vehicle sensors is used to predict the future road grade and longitudinal forces. The predicted information is employed to specify the near future traction force demand. A simulation is carried out using data associated with a 50 km section of a real highway for a typical vehicle. The results are presented and discussed.

Adaptive cruise control look-ahead system for energy management of vehicles

Cruise control in motor vehicles enhances safe and efficient driving by maintaining a constant speed at a preset level. Adaptive Cruise Control (ACC) is the latest development in cruise control. It controls engine throttle position and braking to maintain a safe distance behind a vehicle in front by responding to the speed of this vehicle, thus providing a safer and more relaxing driving environment. ACC can be further developed by including the look-ahead method of predicting environmental factors such as wind speed and road slope. The conventional analytical control methods for adaptive cruise control can generate good results; however they are difficult to design and computationally expensive. In order to achieve a robust, less computationally expensive, and at the same time more natural human-like speed control, intelligent control techniques can be used. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on ACC systems that reduces the energy consumption of the vehicle and improves its efficiency. The Adaptive Cruise Control Look-Ahead (ACC-LA) system works as follows: It calculates the energy consumption of the vehicle under combined dynamic loads like wind drag, slope, kinetic energy and rolling friction using road data, and it includes a look-ahead strategy to predict the future road slope. The cruise control system adaptively controls the vehicle speed based on the preset speed and the predicted future slope information. By using the ANFIS method, the ACC-LA is made adaptive under different road conditions (slope angle and wind direction and speed). The vehicle was tested using the adaptive cruise control look-ahead energy management system, the results compared with the vehicle running the same test but without the adaptive cruise control look-ahead energy management system. The evaluation outcome indicates that the vehicle speed was efficiently controlled through the look-ahead methodology based upon the driving cycle, and that the average fuel consumption was reduced by 3%.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Stochastic models of road geometry and wind condition for vehicle energy management and control

Modeling and simulation is commonly used to improve vehicle performance, to optimize vehicle system design, and to reduce vehicle development time. Vehicle performances can be affected by environmental conditions and driver behavior factors, which are often uncertain and immeasurable. To incorporate the role of environmental conditions in the modeling and simulation of vehicle systems, both real and artificial data are used. Often, real data are unavailable or inadequate for extensive investigations. Hence, it is important to be able to construct artificial environmental data whose characteristics resemble those of the real data for modeling and simulation purposes. However, to produce credible vehicle simulation results, the simulated environment must be realistic and validated using accepted practices. This paper proposes a stochastic model that is capable of creating artificial environmental factors such as road geometry and wind conditions. In addition, road geometric design principles are employed to modify the created road data, making it consistent with the real-road geometry. Two sets of real-road geometry and wind condition data are employed to propose probability models. To justify the distribution goodness of fit, Pearson's chi-square and correlation statistics have been used. Finally, the stochastic models of road geometry and wind conditions (SMRWs) are developed to produce realistic road and wind data. SMRW can be used to predict vehicle performance, energy management, and control strategies over multiple driving cycles and to assist in developing fuel-efficient vehicles.

Intelligent control and look-ahead energy management of hybrid electric vehicles

A review of the state of knowledge in the field of control and energy management in HEVs is carried out. The key innovation of the project is the development of a model of a PHEV using the real road data with an intelligent look-ahead online controller. Another novelty of this work is the method of route planning. It combines the information of vehicle sensors such as accelerometer and speedometer with the data of a GPS to create a road grade map for use within the look-ahead energy management strategy in the vehicle. For the PHEV, an adaptive cruise controller is modelled and an optimisation method is applied to obtain the best speed profile during a trajectory. Finally, the nonlinear model of the vehicle is applied with the sliding mode controller. The effect of using this controller is compared with the universal cruise controller. The stability of the system is studied and proved.

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.