937 research outputs found
Traffic Danger Recognition With Surveillance Cameras Without Training Data
We propose a traffic danger recognition model that works with arbitrary
traffic surveillance cameras to identify and predict car crashes. There are too
many cameras to monitor manually. Therefore, we developed a model to predict
and identify car crashes from surveillance cameras based on a 3D reconstruction
of the road plane and prediction of trajectories. For normal traffic, it
supports real-time proactive safety checks of speeds and distances between
vehicles to provide insights about possible high-risk areas. We achieve good
prediction and recognition of car crashes without using any labeled training
data of crashes. Experiments on the BrnoCompSpeed dataset show that our model
can accurately monitor the road, with mean errors of 1.80% for distance
measurement, 2.77 km/h for speed measurement, 0.24 m for car position
prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based
Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383,
IEE
Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning
In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle's distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates
A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding
Driver gaze plays an important role in different gaze-based applications such
as driver attentiveness detection, visual distraction detection, gaze behavior
understanding, and building driver assistance system. The main objective of
this study is to perform a comprehensive summary of driver gaze fundamentals,
methods to estimate driver gaze, and it's applications in real world driving
scenarios. We first discuss the fundamentals related to driver gaze, involving
head-mounted and remote setup based gaze estimation and the terminologies used
for each of these data collection methods. Next, we list out the existing
benchmark driver gaze datasets, highlighting the collection methodology and the
equipment used for such data collection. This is followed by a discussion of
the algorithms used for driver gaze estimation, which primarily involves
traditional machine learning and deep learning based techniques. The estimated
driver gaze is then used for understanding gaze behavior while maneuvering
through intersections, on-ramps, off-ramps, lane changing, and determining the
effect of roadside advertising structures. Finally, we have discussed the
limitations in the existing literature, challenges, and the future scope in
driver gaze estimation and gaze-based applications
Efficient Data Collection in Multimedia Vehicular Sensing Platforms
Vehicles provide an ideal platform for urban sensing applications, as they
can be equipped with all kinds of sensing devices that can continuously monitor
the environment around the travelling vehicle. In this work we are particularly
concerned with the use of vehicles as building blocks of a multimedia mobile
sensor system able to capture camera snapshots of the streets to support
traffic monitoring and urban surveillance tasks. However, cameras are high
data-rate sensors while wireless infrastructures used for vehicular
communications may face performance constraints. Thus, data redundancy
mitigation is of paramount importance in such systems. To address this issue in
this paper we exploit sub-modular optimisation techniques to design efficient
and robust data collection schemes for multimedia vehicular sensor networks. We
also explore an alternative approach for data collection that operates on
longer time scales and relies only on localised decisions rather than
centralised computations. We use network simulations with realistic vehicular
mobility patterns to verify the performance gains of our proposed schemes
compared to a baseline solution that ignores data redundancy. Simulation
results show that our data collection techniques can ensure a more accurate
coverage of the road network while significantly reducing the amount of
transferred data
Review of traffic data collection methods for drivers’ car – following behaviour under various weather conditions
Adverse weather conditions have considerable impact on traffic operation and safety as it affects drivers’ car-following behaviour. However, the quality of traffic data and its related methodologies to address these effects are under continuous enhancement. This paper intends to provide an overview of various empirical traffic data collection methodologies widely used to investigate drivers car-following behaviour under various weather conditions. These methodologies include video cameras, pneumatic tubes, floating car data, instrumented vehicle and driving simulator. Moreover, the advantages and disadvantages related to methodologies have been discussed with emphasis on their suitability to work under adverse weather conditions. Furthermore, conclusion also comprises on table format of comparative review of facilities concerned with the methodologies
Monocular visual traffic surveillance: a review
To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined
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Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles
With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag
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