Augmented Reality Vehicle system: Left-turn maneuver study

Moussa, Ghada S.; Radwan, Essam; Hussain, Khaled F.

doi:10.1016/j.trc.2011.08.005

Cited by 49 publications

(19 citation statements)

References 19 publications

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“…Some cutting-edge technologies also pay attention to left-turn problems. Augmented reality (AR) technology can offer a very realistic environment for enhancing driver reaction to different road design and traffic operations scenarios [ 30 ]. The optic technique uses an optical combiner for combining real and virtual objects, and the video technique uses a computer or a video mixer to combine the video of the real world, from video cameras, with virtual images (computer-generated) [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Signalization and Channelization Selections at Intersections Based on an Entropy Method

Shao

Claudel

2019

Entropy

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Direct left turns (DLTs) could cause traffic slowdown, delay, stops, and even accidents on intersections, especially on no-median roads. Channelization and signalization can significantly diminish negative impact of DLTs. In China, a total of 56 large and medium-sized cities, including 17 provincial capitals, have adopted vehicle restriction policies due to traffic congestion, vehicle energy conservation and emission reduction, which cause travel inconvenience for citizens. This paper mainly studies signalization and channelization selections at intersections based on an entropy method. Based on the commonly used three evaluation indexes, the number of vehicles, CO emissions and fuel consumption have been added. The entropy evaluation method (EEM) method is innovatively used to objectively calculate the weight of the six indexes, which carry out the optimal traffic volume combinations for intersections of present situation, channelization and signalization. A VISSIM simulation is also used to evaluate the operating status of three conditions. The results show that EEM could help enormously in choosing different methods at a certain intersection. With the EEM, six indexes decrease by 20–70% at most.

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Section: Introductionmentioning

confidence: 99%

Evaluating Signalization and Channelization Selections at Intersections Based on an Entropy Method

Shao

Claudel

2019

Entropy

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“…However, it is agreed that the turning maneuver of vehicles is a further complex phenomenon whose variability extends to be related to highly dynamic factors [31,32]. For instance, the turning behavior was observed to depend on inter-and intra-subject factors concerning drivers such as the perception of traffic environment, information processing, and the ability to react correctly and to cooperate with others [33,34]. Other factors such as the waiting time of the turning vehicles [35], the relative speed of the vehicles in conflict [36], and gaps [37] were observed to impact on the decision behavior of turning vehicles.…”

Section: Modeling Of Vehicle Turning Trajectorymentioning

confidence: 99%

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

2020

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This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

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“…Recently, on pace with the great development of AR technologies, AR-based surgical simulation has been widely explored for better surgical training and navigation. AR aligns and then seamlessly combines computergenerated virtual objects and the real environment to provide more immersive and realistic visual effects [17]. Even with these advantages, developing an AR-based surgical navigation system is a challenging task.…”

Section: Related Workmentioning

confidence: 99%

Assessing performance of augmented reality-based neurosurgical training

Liao

Qian

et al. 2019

Vis. Comput. Ind. Biomed. Art

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This paper presents a novel augmented reality (AR)-based neurosurgical training simulator which provides a very natural way for surgeons to learn neurosurgical skills. Surgical simulation with bimanual haptic interaction is integrated in this work to provide a simulated environment for users to achieve holographic guidance for pre-operative training. To achieve the AR guidance, the simulator should precisely overlay the 3D anatomical information of the hidden target organs in the patients in real surgery. In this regard, the patient-specific anatomy structures are reconstructed from segmented brain magnetic resonance imaging. We propose a registration method for precise mapping of the virtual and real information. In addition, the simulator provides bimanual haptic interaction in a holographic environment to mimic real brain tumor resection. In this study, we conduct AR-based guidance validation and a user study on the developed simulator, which demonstrate the high accuracy of our AR-based neurosurgery simulator, as well as the AR guidance mode's potential to improve neurosurgery by simplifying the operation, reducing the difficulty of the operation, shortening the operation time, and increasing the precision of the operation.

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Augmented Reality Vehicle system: Left-turn maneuver study

Cited by 49 publications

References 19 publications

Evaluating Signalization and Channelization Selections at Intersections Based on an Entropy Method

Evaluating Signalization and Channelization Selections at Intersections Based on an Entropy Method

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

Assessing performance of augmented reality-based neurosurgical training

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