7,348 research outputs found
A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry
The demand for autonomous vehicles is increasing gradually owing to their
enormous potential benefits. However, several challenges, such as vehicle
localization, are involved in the development of autonomous vehicles. A simple
and secure algorithm for vehicle positioning is proposed herein without
massively modifying the existing transportation infrastructure. For vehicle
localization, vehicles on the road are classified into two categories: host
vehicles (HVs) are the ones used to estimate other vehicles' positions and
forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV
transmits modulated data from the tail (or back) light, and the camera of the
HV receives that signal using optical camera communication (OCC). In addition,
the streetlight (SL) data are considered to ensure the position accuracy of the
HV. Determining the HV position minimizes the relative position variation
between the HV and FV. Using photogrammetry, the distance between FV or SL and
the camera of the HV is calculated by measuring the occupied image area on the
image sensor. Comparing the change in distance between HV and SLs with the
change in distance between HV and FV, the positions of FVs are determined. The
performance of the proposed technique is analyzed, and the results indicate a
significant improvement in performance. The experimental distance measurement
validated the feasibility of the proposed scheme
An Edge Assisted Robust Smart Traffic Management and Signalling System for Guiding Emergency Vehicles During Peak Hours
Congestion in traffic is an unavoidable circumstance in many cities in India
and other countries. It is an issue of major concern. The steep rise in the
number of automobiles on the roads followed by old infrastructure, accidents,
pedestrian traffic, and traffic rule violations all add to challenging traffic
conditions. Given these poor conditions of traffic, there is a critical need
for automatically detecting and signaling systems. There are already various
technologies that are used for traffic management and signaling systems like
video analysis, infrared sensors, and wireless sensors. The main issue with
these methods is they are very costly and high maintenance is required. In this
paper, we have proposed a three-phase system that can guide emergency vehicles
and manage traffic based on the degree of congestion. In the first phase, the
system processes the captured images and calculates the Index value which is
used to discover the degree of congestion. The Index value of a particular road
depends on its width and the length up to which the camera captures images of
that road. We have to take input for the parameters (length and width) while
setting up the system. In the second phase, the system checks whether there are
any emergency vehicles present or not in any lane. In the third phase, the
whole processing and decision-making part is performed at the edge server. The
proposed model is robust and it takes into consideration adverse weather
conditions such as hazy, foggy, and windy. It works very efficiently in low
light conditions also. The edge server is a strategically placed server that
provides us with low latency and better connectivity. Using Edge technology in
this traffic management system reduces the strain on cloud servers and the
system becomes more reliable in real-time because the latency and bandwidth get
reduced due to processing at the intermediate edge server.Comment: Accepted at the Doctoral Symposium on Human Centered Computing
(Human-2023), February 25, 2023. To be published in "Springer Tracts in
Human-Centered Computing
Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data
The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values
Design and Simulation of a Smart Traffic System in a Campus Community.
Road traffic within campus communities has increased tremendously. More persons are now moving around campuses with vehicles than previously recorded. This development will pose a major traffic challenge if it is not addressed urgently. Standard technologies for traffic management in campus communities do not have a computerized framework that can control traffic based on detected level of congestion. The main purpose of this research is to propose a more efficient and effective system for road traffic management in a campus community. The system is completely automated and can manage the ever mounting traffic in campus
communities. The proposed campus traffic management system was simulated using Proteus®. Tests carried out on the simulated reallife campus traffic scenario confirmed that the proposed campus traffic management system was better than conventional traffic control systems in existence on campuses
Promoting Bicycle Commuter Safety, Research Report 11-08
We present an overview of the risks associated with cycling to emphasize the need for safety. We focus on the application of frameworks from social psychology to education, one of the 5 Es—engineering, education, enforcement, encouragement, and evaluation. We use the structure of the 5 Es to organize information with particular attention to engineering and education in the literature review. Engineering is essential because the infrastructure is vital to protecting cyclists. Education is emphasized since the central focus of the report is safety
A Vision-Based Driver Nighttime Assistance and Surveillance System Based on Intelligent Image Sensing Techniques and a Heterogamous Dual-Core Embedded System Architecture
This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system
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