Stereo vision–based vehicle detection using a road feature and disparity histogram

Traffic congestion has been causing many setbacks and challenges in the major developing and developed smart cities in India and all over the world. The typical traffic light sequence works on the specific switching of Red, Green and Yellow lights in a particular way with stipulated time irrespective of peak traffic hour and situation, this makes typical traffic light sequence method inefficient for present situation where density of traffic is exponentially increasing over the time. In this work, we propose and develop a design of Intelligent Traffic Light Control System to manage the road traffic according to the real time traffic situation. This work is intended to propose methods of dynamic time allocation for traffic lights according to traffic density and situation. In this paper, we acquired real time traffic density using image sensors and obstacle detector, where according to the traffic density, traffic controller dynamically generates “ON” and “OFF” time of traffic light sequence. The EDGE Spartan 6 FPGA (Field Programmable Gate Array) development board is programmed as controller to implement the proposed work and MATLAB programming in used to capture real time traffic image and generate dynamic time for the traffic light sequence to control the traffic situation in real time.

Traffic systems are man-made systems in which the human component as a complex factor plays a significant part. [1] The aim of the research was to offer a method which is applicable for setting the real traffic situations at a signalized intersection against the simulated ones, using a traffic model with similar statistical features. The simplest technical apparatus has been used to monitor the real traffic situations. Only one video camera is needed for data collection and it is located in the observable area. Hence time-dependent parameters have been collected which are available for the analysis of the traffic situations. In the case of both traffic models the analysis is based on a behavior model of inhomogeneous driver-populations and an estimation method. The behavior model of inhomogeneous driver-populations handles the time-dependent parameters of driver behavior as the most important predictors, describes the movement and the behavior of the inhomogeneous driver-populations approaching a signalized intersection, passing the amber signal light. The estimation method developed using the model parameters opens the door to estimating the composition of drivers and measuring the capacity of an intersection on a more sensitive scale.

Automatic multi-vehicle tracking using video cameras: An improved CAMShift approach

Image sequences recorded with cameras mounted in a moving vehicle provide information about the vehicle's environment which has to be analysed in order to really support the driver in actual traffic situations. One type of information is the lane structure surrounding a vehicle. Therefore, we systematically developed and investigated driver assistance functions which make explicit use of the lane structure represented by lane borders and lane markings. With increasing computing power of standard PCs it was possible to realize more complex driver assistance with general purpose hardware. Investigations with a video-based lane departure warning system and a lane change assistant for highways will be discussed in detail. We integrated our lane keeping assistant in some experimental cars and performed systematic experiments in real traffic situations which enables the experience of video-based driver assistance on a high level—the action of a system. This allows us to assess whether a driver assistance system really understands the actual traffic situation which is the basis for reliable systems accepted by the user.

A New Human-Computer-Interface for High-Speed-Maglev Train Traffic Supervision

Although depth information of a scene is not stored while taking a picture, some depth information is retained in the captured image. A method is proposed for generating the depth map of a single image based on different scene categories. The image is first classified into a category based on color and texture information, and then the depth map of the image is generated according to the different scene categories. The depth map can be used to generate stereo binocular image pairs by left- and right-shifting the original image. Then, the stereoscopic image with three-dimensional (3-D) visual effect can be viewed through a 3-D stereo display. The experiments showed that the proposed method works well, producing a satisfactory stereoscopic effect.

Deciding the Behaviour of an Autonomous Mobile Road Vehicle

On the determination of the aerodynamic coefficients of highway tunnels

We report on simulations of chaotic traffic flow in freeways based on a fuzzy knowledge based model of driver behavior. Our simulation approach is a microscopic method similar to Q. Yang and H.N. Koutsopoulos (1996), where individual vehicles are separately modeled. The knowledge base consists of an extensive and exhaustive set of fuzzy IF-THEN rules depicting all possible traffic situations that a driver is likely to encounter in a highway. The principal motivation behind using a fuzzy knowledge based approach to model the driver behavior is because fuzzy modeling provides an effective means to break up any highly nonlinear system into IF-THEN rules (J.M. Mendel, 1995). In addition, fuzzy logic is well equipped to handle uncertainties that are present in real world traffic situations (P. Chakraborty and S. Kikuchi, 1992; S. Kikuchi and M. Pursula, 1998). Therefore using fuzzy rules, it is possible to incorporate linguistic descriptions of scenarios such as 'speed is moderate' or 'adjacent lane gap is quite acceptable'. Instead of quantifying variables into crisp classes as in a traditional expert system, this is the manner in which a driver is more likely to perceive any situation. Fuzzy logic techniques are receiving recent attention as important tools in transportation engineering studies and such approaches have met with a great deal of success.

Due to blockage by large obstacles, perception of detection sensors such as lidar, radar, and stereo vision can have visibility restrictions. The assessment of collision risk against moving vehicles in the occluded area is difficult because vehicle motion information for perception is not available. In order to overcome this difficulty, this paper presents a probabilistic approach of collision risk assessment for the potential collision of vehicles in the occluded area. The probabilistic assessment consists of three steps: a field of view (FoV) modeling of perception, a motion prediction of potential collision, and a probabilistic collision risk assessment. The first step is a FoV modeling of perception to classify the occluded region of perception visibility. The second step is motion predictions of the potential collision vehicles by applying the modeled FoV to the roadway geometry in the precise map. The final step is a probabilistic assessment of the collision risk for the occluded vehicles based on the motion predictions and probabilistic modeling of vehicle speed on the road. The proposed collision risk assessment was evaluated using various occlusion scenarios in real traffic situations, such as an alleyway.

The evaluation of the electrooculogram as a psychophysiological measuring instrument in the driver study of driver behaviour.

We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)—vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4%~ 10% for our three tested videos in the experimental results of vehicle counting.

Of the range of wireless communication technologies, wireless sensor networks (WSN) will be one of the most appropriate technologies for the Microgrid and Grid-enabled Vehicles in the Smartgrid. To ensure the security of WSN, the detection of attacks is more efficient than their prevention because of the lack of computing power. Malicious packet drops are the easiest means of attacking WSNs. Thus, the sensors used for constructing a WSN require a packet drop monitoring agent, such as Watchdog. However, Watchdog has a partial drop problem such that an attacker can manipulate the packet dropping rate below the minimum misbehaviour monitoring threshold. Furthermore, Watchdog does not consider real traffic situations, such as congestion and collision, and so it has no way of recognizing whether a packet drop is due to a real attack or network congestion. In this paper, we propose a malicious packet drop monitoring agent, which considers traffic conditions. We used the actual traffic volume on neighbouring nodes and the drop rate while monitoring a sending node for specific period. It is more effective in real network scenarios because unlike Watchdog it considers the actual traffic, which only uses the Pathrater. Moreover, our proposed method does not require authentication, packet encryption or detection packets. Thus, there is a lower likelihood of detection failure due to packet spoofing, Man-In-the Middle attacks or Wormhole attacks. To test the suitability of our proposed concept for a series of network scenarios, we divided the simulations into three types: one attack node, more than one attack nodes and no attack nodes. The results of the simulations meet our expectations.

The manuscript discusses the investigation of vehicle flow in a predesignated junction by an appropriate traffic flow management with an effort to minimize fuel consumption, the production of CO2, an essential greenhouse gas (hereinafter referred to as GHG), and related transport costs. The particular research study was undertaken in a frequented junction in the city of Celje, located in the eastern part of Slovenia. The results obtained summarize data on consumed fuel and produced CO2 amounts depending on the type of vehicle, traffic flow mixture, traffic light signal plan, and actual vehicle velocity. These values were calculated separately for three different conditions of traffic flow management. Amounts of fuel consumed were experimentally investigated in real traffic situations, whereas CO2 production was calculated by applying the actual European standard entitled EN 16258:2012 associated with a guideline for measuring emission values, as well as by examining specific traffic flow parameters. The key objective of the manuscript is to present multiple scenarios towards striving to minimize environmental impacts and improve transport operation’s economic consequences when implementing proper traffic flow management. As for crucial findings, we quantified fuel consumption and CO2 emissions based on real data on the number and type of vehicles crossing the examined intersection and traffic light switching intervals. The results show that most of the CO2 was produced while waiting and in the accelerating phase in front of traffic lights, whereby in the running phase through the intersection, significantly less fuel was used. This study represents a mosaic fragment of research addressing endeavors to reduce CO2 production in urban transport. Following the experiments conducted, we can see a notable contribution towards reducing CO2 production with known and tested interventions in the existing transport infrastructure. A procedure embracing individual research steps may be deemed as an approach methodology dealing with traffic flow management with an aim to decrease the environmental and economic impacts of traffic and transport operation; this is where the novelty of the research lies.

The work described in this paper is focused on an approach for implementing in real working contexts the guidelines of user-centred design contained in formal standards and in many research studies. The application concerns the EUCLIDE project (enhanced human–machine interface for on vehicle integrated driving support system), which aimed at developing a driving support system to avoid collisions with obstacles in reduced visibility conditions. The design of the system followed a user-centred approach which started by identifying the model of cognition to be applied throughout the whole design process. The definition of the warning strategies of the system was firstly analysed with the aim to achieve the highest balance between a totally supportive system and a non-disturbing system. Then an initial set of design solutions for the human–machine interface was tested in a static driving simulator. A second set of possible interfaces was evaluated in a dynamic simulator before developing a final design. This solution was implemented in two real vehicles and tested in real traffic situations. This paper describes the whole design process and concentrates on the final step of “in-vehicle” integration process. The road tests performed at the end of the whole process are discussed in detail focusing on the safety implications associated with the design solution finally selected and implemented.