2,303 research outputs found
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
A traffic monitoring system is an integral part of Intelligent Transportation
Systems (ITS). It is one of the critical transportation infrastructures that
transportation agencies invest a huge amount of money to collect and analyze
the traffic data to better utilize the roadway systems, improve the safety of
transportation, and establish future transportation plans. With recent advances
in MEMS, machine learning, and wireless communication technologies, numerous
innovative traffic monitoring systems have been developed. In this article, we
present a review of state-of-the-art traffic monitoring systems focusing on the
major functionality--vehicle classification. We organize various vehicle
classification systems, examine research issues and technical challenges, and
discuss hardware/software design, deployment experience, and system performance
of vehicle classification systems. Finally, we discuss a number of critical
open problems and future research directions in an aim to provide valuable
resources to academia, industry, and government agencies for selecting
appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
The Feasibility of Quantitatively Characterizing the Vehicle Motion Environment (VME)
https://deepblue.lib.umich.edu/bitstream/2027.42/154108/1/ervin1990.pd
Efficient Ego Lane Detection for Various LaneTypes
In this work, we present an ego lane detector de-signed for the use in automotive vision systems for personallight electric vehicles like electric bicycles, tricycles or scoot-ers. The approach is based on a combination of gradient-based line detection, color-based segmentation and geomet-rical rules, making the ego lane detector fast, but also robustto different scenes, including curves. Qualitative evaluationon over fifty traffic scenes show that the lane detector is ableto find a suitable approximation of the road area with an IoUof 75.71%
Fully automated urban traffic system
The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible
Real time lane detection for autonomous vehicles
An increasing safety and reducing road
accidents, thereby saving lives are one of great interest
in the context of Advanced Driver Assistance Systems.
Apparently, among the complex and challenging tasks
of future road vehicles is road lane detection or road
boundaries detection. It is based on lane detection
(which includes the localization of the road, the
determination of the relative position between vehicle
and road, and the analysis of the vehicleโs heading
direction). One of the principal approaches to detect
road boundaries and lanes using vision system on the
vehicle. However, lane detection is a difficult problem
because of the varying road conditions that one can
encounter while driving. In this paper, a vision-based
lane detection approach capable of reaching real time
operation with robustness to lighting change and
shadows is presented. The system acquires the front
view using a camera mounted on the vehicle then
applying few processes in order to detect the lanes.
Using a pair of hyperbolas which are fitting to the
edges of the lane, those lanes are extracted using
Hough transform. The proposed lane detection system
can be applied on both painted and unpainted road as
well as curved and straight road in different weather
conditions. This approach was tested and the
experimental results show that the proposed scheme
was robust and fast enough for real time requirements.
Eventually, a critical overview of the methods were
discussed, their potential for future deployment were
assis
Regional Data Archiving and Management for Northeast Illinois
This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor
and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the
regional transportation agencies, from both technical and business perspectives, about building such a comprehensive
transportation information system. Several implementation alternatives are identified and analyzed. This research is carried
out in three phases.
In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a
thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect
information on all data elements that they store, including the format, system, and granularity. Their perception of a data
archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the
database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and
examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We
estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the
hardware, software, labor, and resource requirements. We also identify possible revenue opportunities.
A few implementation options for the archive system are summarized in this report; namely:
1. System hosted by a partnering agency
2. System contracted to a university
3. System contracted to a national laboratory
4. System outsourced to a service provider
The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe
Vision based road lane detection system for vehicles guidance
Driver support system is one of the most important feature of the modern vehicles to ensure driver safety and decrease vehicle accident on roads. Apparently, the road lane detection or road boundaries detection is the complex and most challenging tasks. It is includes the localization of the road and the determination of the relative position between vehicle and road. A vision system using on-board camera looking outwards from the windshield is presented in this paper. The system acquires the front view using a camera mounted on the vehicle and detects the lanes by applying few processes. The lanes are extracted using Hough transform through a pair of hyperbolas which are fitted to the edges of the lanes. The proposed lane detection system can be applied on both painted and unpainted roads as well as curved and straight road in different weather conditions. The proposed system does not require any extra information such as lane width, time to lane crossing and offset between the center of the lanes. In addition, camera calibration and coordinate transformation are also
not required. The system was investigated under various situations of changing illumination, and shadows effects in various road types without speed limits. The system has demonstrated a robust performance for detecting the road lanes under different conditions
- โฆ