Browsing by Subject "Vehicle dynamics"

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    Distributed Nonlinear Model Predictive Control for Heterogeneous Vehicle Platoons Under Uncertainty
    (IEEE Xplore, 2021-09) Shen, Dan; Yin, Jianhua; Du, Xiaoping; Li, Lingxi; Electrical and Computer Engineering, School of Engineering and Technology
    This paper presents a novel distributed nonlinear model predictive control (DNMPC) for minimizing velocity tracking and spacing errors in heterogeneous vehicle platoon under uncertainty. The vehicle longitudinal dynamics and information flow in the platoon are established and analyzed. The algorithm of DNMPC with robustness and reliability considerations at each vehicle (or node) is developed based on the leading vehicle and reference information from nodes in its neighboring set. Together with the physical constraints on the control input, the nonlinear constraints on vehicle longitudinal dynamics, the terminal constraints on states, and the reliability constraints on both input and output, the objective function is defined to optimize the control accuracy and efficiency by penalizing the tracking errors between the predicted outputs and desirable outputs of the same node and neighboring nodes, respectively. Meanwhile, the robust design optimization model also minimizes the expected quality loss which consists of the mean and standard deviation of node inputs and outputs. The simulation results also demonstrate the accuracy and effectiveness of the proposed approach under two different traffic scenarios.
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    Implementation and Performance Evaluation of In-vehicle Highway Back-of-Queue Alerting System Using the Driving Simulator
    (IEEE Xplore, 2021-09) Zhang, Zhengming; Shen, Dan; Tian, Renran; Li, Lingxi; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
    This paper proposes a prototype in-vehicle highway back-of-queue alerting system that is based on an Android-based smartphone app, which is capable of delivering warning information to on-road drivers approaching traffic queues. To evaluate the effectiveness of this alerting system, subjects were recruited to participate in the designed test scenarios on a driving simulator. The test scenarios include three warning types (no alerts, roadside alerts, and in-vehicle auditory alerts), three driver states (normal, distracted, and drowsy), and two weather conditions (sunny and foggy). Driver responses related to vehicle dynamics data were collected and analyzed. The results indicate that on average, the drowsy state decreases the minimum time-to-collision by 1.6 seconds compared to the normal state. In-vehicle auditory alerts can effectively increase the driving safety across different combinations of situations (driver states and weather conditions), while roadside alerts perform better than no alerts.