Готові списки джерел за темами / Multi-Lane trajectory

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Добірка наукової літератури з теми "Multi-Lane trajectory"

Автор: Grafiati

Опубліковано: 23 вересня 2022

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Статті в журналах з теми "Multi-Lane trajectory"

Luo, Yugong, Gang Yang, Mingchang Xu, Zhaobo Qin, and Keqiang Li. "Cooperative Lane-Change Maneuver for Multiple Automated Vehicles on a Highway." Automotive Innovation 2, no. 3 (September 2019): 157–68. http://dx.doi.org/10.1007/s42154-019-00073-1.

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Abstract With the development of vehicle-to-vehicle (V2V) communication, it is possible to share information among multiple vehicles. However, the existing research on automated lane changes concentrates only on the single-vehicle lane change with self-detective information. Cooperative lane changes are still a new area with more complicated scenarios and can improve safety and lane-change efficiency. Therefore, a multi-vehicle cooperative automated lane-change maneuver based on V2V communication for scenarios of eight vehicles on three lanes was proposed. In these scenarios, same-direction and intersectant-direction cooperative lane changes were defined. The vehicle that made the cooperative decision obtained the information of surrounding vehicles that were used to cooperatively plan the trajectories, which was called cooperative trajectory planning. The cooperative safety spacing model was proposed to guarantee and improve the safety of all vehicles, and it essentially developed constraints for the trajectory-planning task. Trajectory planning was treated as an optimization problem with the objective of maximizing safety, comfort, and lane-change efficiency under the constraints of vehicle dynamics and the aforementioned safety spacing model. Trajectory tracking based on a model predictive control method was designed to minimize tracking errors and control increments. Finally, to verify the validity of the proposed maneuver, an integrated simulation platform combining MATLAB/Simulink with CarSim was established. Moreover, a hardware-in-the-loop test bench was performed for further verification. The results indicated that the proposed multi-vehicle cooperative automated lane-change maneuver can achieve lane changes of multiple vehicles and increase lane-change efficiency while guaranteeing safety and comfort.

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Hou, Quanshan, Yanan Zhang, Shuai Zhao, Yunhao Hu, and Yongwang Shen. "Tracking Control of Intelligent Vehicle Lane Change Based on RLMPC." E3S Web of Conferences 233 (2021): 04019. http://dx.doi.org/10.1051/e3sconf/202123304019.

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Autonomous lane changing, as a key module to realize high-level automatic driving, has important practical significance for improving the driving safety, comfort and commuting efficiency of vehicles. Traditional controllers have disadvantages such as weak scene adaptability and difficulty in balancing multi-objective optimization. In this paper, combined with the excellent self-learning ability of reinforcement learning, an interactive model predictive control algorithm is designed to realize the tracking control of the lane change trajectory. At the same time, two typical scenarios are verified by PreScan and Simulink, and the results show that the control algorithm can significantly improve the tracking accuracy and stability of the lane change trajectory.

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Tian, Wei, Songtao Wang, Zehan Wang, Mingzhi Wu, Sihong Zhou, and Xin Bi. "Multi-Modal Vehicle Trajectory Prediction by Collaborative Learning of Lane Orientation, Vehicle Interaction, and Intention." Sensors 22, no. 11 (June 5, 2022): 4295. http://dx.doi.org/10.3390/s22114295.

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Accurate trajectory prediction is an essential task in automated driving, which is achieved by sensing and analyzing the behavior of surrounding vehicles. Although plenty of research works have been invested in this field, it is still a challenging subject due to the environment’s complexity and the driving intention uncertainty. In this paper, we propose a joint learning architecture to incorporate the lane orientation, vehicle interaction, and driving intention in vehicle trajectory forecasting. This work employs a coordinate transform to encode the vehicle trajectory with lane orientation information, which is further incorporated into various interaction models to explore the mutual trajectory relations. Extracted features are applied in a dual-level stochastic choice learning to distinguish the trajectory modality at both the intention and motion levels. By collaborative learning of lane orientation, interaction, and intention, our approach can be applied to both highway and urban scenes. Experiments on the NGSIM, HighD, and Argoverse datasets demonstrate that the proposed method achieves a significant improvement in prediction accuracy compared with the baseline.

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Yao, Handong, and Xiaopeng Li. "Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads." Transportation Research Part C: Emerging Technologies 129 (August 2021): 103182. http://dx.doi.org/10.1016/j.trc.2021.103182.

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Yong, Huang, Fang Daqing, Tan Fuliang, Tao Minglu, Si Daoguang, and Shu Yang. "Research on Vehicle Lane Changing Characteristics of Multi-lane Type Highway Maintenance Operation Area Based on Vehicle Trajectory." IOP Conference Series: Materials Science and Engineering 792 (June 3, 2020): 012011. http://dx.doi.org/10.1088/1757-899x/792/1/012011.

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Liang, Yang, Zhishuai Yin, and Linzhen Nie. "Shared Steering Control for Lane Keeping and Obstacle Avoidance Based on Multi-Objective MPC." Sensors 21, no. 14 (July 8, 2021): 4671. http://dx.doi.org/10.3390/s21144671.

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Анотація:

This paper presents a shared steering control framework for lane keeping and obstacle avoidance based on multi-objective model predictive control. One of the control objectives is to track the reference trajectory, which is updated continuously by the trajectory planning module; whereas the other is to track the driver’s current steering command, so as to consider the driver’s intention. By adding the two control objectives to the cost function of an MPC shared controller, a smooth combination of the commands of the driver and the automation can be achieved through the optimization. The authority of the driver and the automation is allocated by adjusting the weights of the objective terms in the cost function, which is determined by the proposed situation assessment method considering the longitudinal and lateral risks simultaneously. The results of the CarSim-Matlab/Simulink joint simulations show that the proposed shared controller can assist the driver to complete the tasks of lane keeping and obstacle avoidance smoothly while maintaining a good level of vehicle stability.

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Xia, Yulan, Yaqin Qin, Xiaobing Li, and Jiming Xie. "Risk Identification and Conflict Prediction from Videos Based on TTC-ML of a Multi-Lane Weaving Area." Sustainability 14, no. 8 (April 12, 2022): 4620. http://dx.doi.org/10.3390/su14084620.

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Crash risk identification and prediction are expected to play an important role in traffic accident prevention. However, most of the existing studies focus only on highways, not on multi-lane weaving areas. In this paper, a potential collision risk identification and conflict prediction model based on extending Time-to-Collision-Machine Learning (TTC-ML) for multi-lane weaving zone was proposed. The model can accurately learn various features, such as vehicle operation characteristics, risk and conflict distributions, and physical zoning characteristics in the weaving area. Specifically, TTC was used to capture the collision risk severity, and ML extracted vehicle trajectory features. After normalizing and dimensionality reduction of the vehicle trajectory dataset, Naive Bayes, Logistic Regression, and Gradient Boosting Decision Tree (GBDT) models were selected for traffic conflict prediction, and the experiments showed that the GBDT model outperforms two remaining models in terms of prediction accuracy, precision, false-positive rate (FPR) and Area Under Curve (AUC). The research findings of this paper help traffic management departments develop and optimize traffic control schemes, which can be applied to Intelligent Vehicle Infrastructure Cooperative Systems (IVICS) dynamic warning.

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Zong, Fang, Zhengbing He, Meng Zeng, and Yixuan Liu. "Dynamic lane changing trajectory planning for CAV: A multi-agent model with path preplanning." Transportmetrica B: Transport Dynamics 10, no. 1 (October 22, 2021): 266–92. http://dx.doi.org/10.1080/21680566.2021.1989079.

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Gaddam, Hari Krishna, and K. Ramachandra Rao. "Modelling vehicular behaviour using trajectory data under non-lane based heterogeneous traffic conditions." Archives of Transport 52, no. 4 (December 31, 2019): 95–108. http://dx.doi.org/10.5604/01.3001.0014.0211.

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Анотація:

The present study aims to understand the interaction between different vehicle classes using various vehicle attributes and thereby obtain useful parameters for modelling traffic flow under non-lane based heterogeneous traffic conditions. To achieve this, a separate coordinate system has been developed to extract relevant data from vehicle trajectories. Statistical analysis results show that bi-modal and multi-modal distributions are accurate in representing vehicle lateral placement behaviour. These distributions help in improving the accuracy of microscopic simulation models in predicting vehicle lateral placement on carriageway. Vehicles off-centeredness behaviour with their leaders have significant impact on safe longitudinal headways which results in increasing vehicular density and capacity of roadway. Another interesting finding is that frictional clearance distance between vehicles influence their passing speed. Analysis revealed that the passing speeds of the fast moving vehicles such as cars are greatly affected by the presence of slow moving vehicles. However, slow moving vehicles does not reduce their speeds in the presence of fast moving vehicles. It is also found that gap sizes accepted by different vehicle classes are distributed according to Weibull, lognormal and 3 parameter log logistic distributions. Based on empirical observations, the study proposed a modified lateral separation distance factor and frictional resistance factor to model the non-lane heterogeneous traffic flow at macro level. It is anticipated that the outcomes of this study would help in developing a new methodology for modelling non-lane based heterogeneous traffic.

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Gharibi, Mirmojtaba, Zahra Gharibi, Raouf Boutaba, and Steven L. Waslander. "A Density-Based and Lane-Free Microscopic Traffic Flow Model Applied to Unmanned Aerial Vehicles." Drones 5, no. 4 (October 12, 2021): 116. http://dx.doi.org/10.3390/drones5040116.

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In this work, we introduce a microscopic traffic flow model called Scalar Capacity Model (SCM) which can be used to study the formation of traffic on an airway link for autonomous Unmanned Aerial Vehicles (UAVs) as well as for the ground vehicles on the road. Given the 3D trajectory of UAV flights (as opposed to the 2D trajectory of ground vehicles), the main novelty in our model is to eliminate the commonly used notion of lanes and replace it with a notion of density and capacity of flow, but in such a way that individual vehicle motions can still be modeled. We name this a Density/Capacity View (DCV) of the link capacity and how vehicles utilize it versus the traditional One/Multi-Lane View (OMV). An interesting feature of this model is exhibiting both passing and blocking regimes (analogous to multi-lane or single-lane) depending on the set scalar parameter for capacity. We show the model has linear local (platoon) and asymptotic linear stability. Additionally, we perform numerical simulations and show evidence for non-linear stability. Our traffic flow model is represented by a nonlinear differential equation which we transform into a linear form. This makes our model analytically solvable in the blocking regime and piece-wise analytically solvable in the passing regime. Finally, a key advantage of using our model over an OMV model for representing UAV’s flights is the removal of the artificial restriction on passing via only adjacent lanes. This will result in an improved and more realistic traffic flow for UAVs.

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Більше джерел

Дисертації з теми "Multi-Lane trajectory"

Mohamed, Ahmed Mohamed Mahmoud. "Contrôle et commande d'une flotte de véhicules autonomes." Electronic Thesis or Diss., Aix-Marseille, 2021. http://www.theses.fr/2021AIXM0626.

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Les travaux de cette thèse sont consacrés au contrôle et à la commande d'une flotte de plusieurs véhicules (4 à 10 véhicules). Une commande longitudinale est proposée fondée sur l'approche globale décentralisée, pour laquelle les informations du leader et du prédécesseur sont supposées accessibles pour calculer la loi de contrôle en utilisant une commande linéarisante par la dynamique inverse. Ce concept de contrôle permet de suivre une vitesse de référence imposée par le véhicule de tête, tout en respectant une distance de sécurité (variable et constante) pour éviter les collisions. La commande longitudinale est couplée avec la commande latérale qui fait appel à une approche par mode de glissement pour suivre la trajectoire désirée du leader. En outre, des différents observateurs par mode de glissement sont développés. Ces observateurs sont destinés à calculer la dynamique non linéaire dans les commandes de chaque véhicule. La flotte est traitée dans un second temps dans les trajectoires à plusieurs voies (configuration ligne). Deux approches de contrôle sont proposées pour contrôler les véhicules dans les différentes voies (trois voies : i, j et k). Les véhicules sont contrôlés dans la première stratégie pour suivre la vitesse du leader. Cependant, dans la seconde approche, la vitesse désirée du leader est modifiée lors de la présence d'un mouvement latéral de façon à respecter la notion de flotte. Les véhicules sont également contrôlés pour éviter les obstacles et passer à la voie suivante en générant une trajectoire d'évitement de l'obstacle qui tient en compte la distance de sécurité entre les véhicules et l'obstacle, et entre les véhicules eux-mêmes
The works of this thesis are focused on the control and command of a fleet of many vehicles (4 to 10 vehicles). A longitudinal control is proposed based on the decentralized global approach, for which the information of the leader and the predecessor are assumed to be available to compute the control law using a linearization control by inverse dynamics. This control concept allows to follow a reference speed imposed by the leading vehicle, while respecting a safety distance (variable and constant) to avoid collisions. The longitudinal control is coupled with the lateral control that uses a sliding mode approach to follow the leader's desired trajectory. In addition, different sliding mode observers are developed. These observers are intended to calculate the nonlinear dynamics in the controls of each vehicle. The fleet is treated secondly in the multi-lane trajectories (line configuration). Two control approaches are proposed to control the vehicles in the different lanes (three lanes: i, j and k). The vehicles are controlled in the first strategy to follow the speed of the leader. However, in the second approach, the desired speed of the leader is modified when a lateral movement is present in order to respect the fleet notion. The vehicles are also controlled to avoid obstacles and switch to the next lane by generating an obstacle avoidance trajectory that takes into account the safety distance between the vehicles and the obstacle, and between the vehicles themselves

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Тези доповідей конференцій з теми "Multi-Lane trajectory"

Luo, Chenxu, Lin Sun, Dariush Dabiri, and Alan Yuille. "Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341034.

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Tan, Liquan, and Zhaocheng Yang. "Bidirectional multi-lane vehicle counting approach based on trajectory features using MIMO radar." In Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), edited by Yi Xie, Qingli Li, and Kezhi Mao. SPIE, 2022. http://dx.doi.org/10.1117/12.2631434.

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Goli, Mohammad, and Azim Eskandarian. "A Systematic Multi-Vehicle Platooning and Platoon Merging: Strategy, Control, and Trajectory Generation." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6336.

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Problem of autonomous vehicle platooning in an automated highway setting has drawn many attentions, both in academia and industry, during last two decades. This paper studies the problem of vehicle platooning with a particular focus on merging control algorithm when one or several vehicle(s) merge(s) from the adjacent lane into the main vehicle platoon under longitudinal control. Different longitudinal controllers have been compared. A practical novel multi-vehicle merge-in strategy and an adaptive lateral trajectory generation method have been proposed. The proposed approach is then tested and verified in our newly developed simulation platform SimPlatoon.

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Wang, Yangyang, Rong Feng, Ding Pan, Zhiguang Liu, Nan Wu, and Wei Li. "The Trajectory Planning of the Lane Change Assist Based on the Model Predictive Control with Multi-Objective." In Intelligent and Connected Vehicles Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2017. http://dx.doi.org/10.4271/2017-01-2004.

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Nie, Linzhen, Zhishuai Yin, and Haoran Huang. "Decision Making and Trajectory Planning of Intelligent Vehicle’ s Lane-Changing Behavior on Highways under Multi-Objective Constrains." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2020. http://dx.doi.org/10.4271/2020-01-0124.

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Sindha, Jigneshsinh, Basab Chakraborty, and Debashish Chakravarty. "Simulation Based Trajectory Analysis for the Tilt Controlled High Speed Narrow Track Three Wheeler Vehicle." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85087.

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Анотація:

Small sized three wheeler electric vehicles (EVs) are gaining popularity in many developing countries because of its low cost operation and excellent manoeuvrability. However, usage of such a 3Ws usage is limited to low speed application such as last mile public transport. Vehicles with such configuration are not well accepted for personal mobility. If the safe speed of such a vehicles are improved, such a vehicles can also become viable to personal transport. Active tilt control (ATC) systems are seen as one of the possible solution to improve safe speed of narrow track 3Ws.Literature indicates that many attempts have been made for establishing active tilt control system on 3W vehicles for enhancing stability of ATC vehicles and promising results were obtained. This paper presents simulation based analysis of the ATC 3W electric vehicle. This work is part of full scale experimental prototype development for the narrow track ATC 3W vehicle with one wheel in front configuration. The primarily focus of this work is to address vehicle dynamics and trajectory related issue of the tilting 3Ws. A multi-body model of ATC 3W vehicle using single track lateral dynamic model with nonlinear tire characteristics was prepared in SimMechanics. The lateral dynamic outputs in terms of the trajectory followed by vehicle were compared for the constant steering inputs given to non-tilting vehicle, tilting vehicle with direct tilt control (DTC) system and tilting vehicle with Steering direct tilt control (SDTC) system. Two critical driving scenarios of U-turn and Lane change manoeuvre are analyzed. It is observed from the results that there is certain trade-off in selecting a tilt actuator and controller so as to minimize the jerks in the perceived acceleration due to high gain and minimize the tilt angle error to ensure proper stability improvement. It is also identified that the controller must be tuned to the predictable trajectory control, in addition to the main task of reducing the load transfer across the rear wheel axle. The model presented in the paper is used to understand the performance of DTC and SDTC control strategies during potentially dangerous manoeuvres. The desired path following ability of the vehicle is the main measures considered for the analysis.

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