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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"

1

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 (2019): 157–68. http://dx.doi.org/10.1007/s42154-019-00073-1.

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Анотація:

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 an

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2

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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Анотація:

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 verifi

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3

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 (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 i

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4

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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5

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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6

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 (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 author

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7

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 (2022): 4620. http://dx.doi.org/10.3390/su14084620.

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Анотація:

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 w

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8

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 (2021): 266–92. http://dx.doi.org/10.1080/21680566.2021.1989079.

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9

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 (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 ve

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10

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 (2021): 116. http://dx.doi.org/10.3390/drones5040116.

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Анотація:

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 V

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