Browsing by Author "Ruan, Keyu"

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    Assessing the Effectiveness of In-Vehicle Highway Back-of-Queue Alerting System
    (The National Academies of Sciences, Engineering, and Medicine, 2021-01) Shen, Dan; Zhang, Zhengming; Ruan, Keyu; Tian, Renran; Li, Lingxi; Li, Feng; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
    This paper proposes an in-vehicle back-of-queue alerting system that is able to issue alerting messages to drivers on highways approaching traffic queues. A prototype system was implemented to deliver the in-vehicle alerting messages to drivers via an Android-based smartphone app. To assess its effectiveness, a set of test scenarios were designed and implemented on a state-of-the-art driving simulator. Subjects were recruited and their testing data was collected under two driver states (normal and distracted) and three alert types (no alerts, roadside alerts, and in-vehicle auditory alerts). The effectiveness was evaluated using three parameters of interest: 1) the minimum Time-to-Collision (mTTC), 2) the maximum deceleration, and 3) the maximum lateral acceleration. Statistical models were utilized to examine the usefulness and benefits of each alerting type. The results show that the in-vehicle auditory alert is the most effective way for delivering alerting messages to drivers. More specifically, it significantly increases the mTTC (30% longer than that of 'no warning') and decreases the maximum lateral acceleration (60% less than that of 'no warning'), which provides drivers with more reaction time and improves driving stability of their vehicles. The effects of driver distraction significantly decrease the efficiency of roadside traffic sign alert. More specifically, when the driver is distracted, the roadside traffic sign alert performs significantly worse in terms of mTTC compared with that of normal driving. This highlights the importance of the in-vehicle auditory alert when the driver is distracted.
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    Highway Traffic Modeling Using Probabilistic Petri Net Models
    (IEEE, 2020-09) Ruan, Keyu; Li, Lingxi; Chen, Yaobin; Electrical and Computer Engineering, School of Engineering and Technology
    In this paper, we propose a novel method for modeling the highway traffic using Probabilistic Petri nets (PPNs). More specifically, the highway has been partitioned into discrete segments and probabilistic measures are derived based on the traffic data considering the vehicle movements. The proposed model is validated through the study of a publicly available dataset called Active Transportation Demand Management (ATDM) Trajectory Level Validation, which provides the real traffic data from different driving scenarios. The proposed method will generate graphical structures of the PPN as well as all important attributes related to the real traffic data. The output model can be used for path planning and collision avoidance for highway traffic.
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    Identification of unknown petri net structures from growing observation sequences
    (2015-06-08) Ruan, Keyu; Li, Lingxi; King, Brian; Chien, Stanley Yung-Ping
    This thesis proposed an algorithm that can find optimized Petri nets from given observation sequences according to some rules of optimization. The basic idea of this algorithm is that although the length of the observation sequences can keep growing, we can think of the growing as periodic and algorithm deals with fixed observations at different time. And the algorithm developed has polynomial complexity. A segment of example code programed according to this algorithm has also been shown. Furthermore, we modify this algorithm and it can check whether a Petri net could fit the observation sequences after several steps. The modified algorithm could work in constant time. These algorithms could be used in optimization of the control systems and communication networks to simplify their structures.
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    Minimum Initial Marking Estimation in Labeled Petri Nets With Unobservable Transitions
    (IEEE, 2019-01) Ruan, Keyu; Li, Lingxi; Wu, Weimin; Electrical and Computer Engineering, School of Engineering and Technology
    In the literature, researchers have been studying the minimum initial marking (MIM) estimation problem in the labeled Petri nets with observable transitions. This paper extends the results to labeled Petri nets with unobservable transitions (with certain special structure) and proposes algorithms for the MIM estimation (MIM-UT). In particular, we assume that the Petri net structure is given and the unobservable transitions in the net are contact-free. Based on the observation of a sequence of labels, our objective is to find the set of MIM(s) that is(are) able to produce this sequence and has(have) the smallest total number of tokens. An algorithm is developed to find the set of MIM(s) with polynomial complexity in the length of the observed label sequence. Two heuristic algorithms are also proposed to reduce the computational complexity. An illustrative example is also provided to demonstrate the proposed algorithms and compare their performance.
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    A Novel Method for Ground-truth Determination of Lane Information through a Single Web Camera
    (IEEE, 2020-10) Ruan, Keyu; Li, Lingxi; Song, Guobiao; Xia, Jing; Pang, Hongyu; Electrical and Computer Engineering, School of Engineering and Technology
    The high-definition (HD) map is critical for the localization and motion planning of connected and automated vehicles (CAVs). With all the road and lane information pre-scanned in a certain area, the vehicles can know its position with respect to the lane marks and roadside, and hence make better decisions on planning future trajectories. A common issue, however, is the accuracy of the scanned outputs from different data sources. Because of the limitations of online maps (e.g., zooming and stretching in their image layers), visualizing the data in the bird's eye view on maps cannot satisfy the accuracy requirement of being the ground-truth system. To this end, a feasible method that can combine sensing data from different sources and obtain reliable ground-truth information is necessary. In this paper, we develop a novel method to transform the data points from the bird's eye view to the view angle of a web camera installed on the windshield of the ego vehicle. In such a case, the position of landmarks from the captured frames of the camera can be used as the ground-truth. In particular, we take the lane marking detection outputs from the Mobileye system as the reference for a better accuracy. We evaluate the proposed method using the field data on highway I-75 in Michigan, USA. The results show that this method has achieved a very good accuracy of over 90% for location determination of lane information. The main contribution of this paper is that the proposed method can be more intuitive and reliable than using the traditional maps in bird's eye view.