Study on control strategy for personalised lane‐change on highway

Cheng, Shuying; Wang, Chuanhai; Zhang, Shuangteng; Zong, Ruixue; Deng, Xinli

doi:10.1049/joe.2018.8269

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Hongil An et al [14] proposed a collaborative lane-change architecture and protocol considering V2V communication delay for CAVs. Cheng et al [15] took samples from the actual lane-change process of a freeway and formulated a lane-change control strategy based on personalized driving. The simulation results showed that the proposed personalized driving strategy not only respects the personalized operating styles of different drivers but also effectively controls driving risks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Collaborative Merging Strategy with Lane Changing in Multilane Freeway On-Ramp Area with V2X Network

Shi

Guo

et al. 2021

Future Internet

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The merging area of the freeway is an area with a high incidence of traffic accidents. With the development of connected and automated vehicles (CAVs) and V2X technology, the traffic efficiency of freeway ramp areas has been significantly improved. However, current research mostly focuses on merging a single mainline lane and ramp, and there are few cases of multiple lanes. In this paper, we present a collaborative merging model with a rule-based lane-changing strategy in a V2X environment. First, the vehicle selects the appropriate gap to change lanes safely without affecting other vehicles. Meanwhile, we established a linear time discrete model to optimize the trajectory of vehicles in real-time. Finally, the proposed model and strategy were implemented in SUMO and Python. The simulation results showed that the merging model we proposed based on the lane-changing strategy had good performance in terms of the number of stops, average delay, and average speed.

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Section: Literature Reviewmentioning

confidence: 99%

A Collaborative Merging Strategy with Lane Changing in Multilane Freeway On-Ramp Area with V2X Network

Shi

Guo

et al. 2021

Future Internet

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“…In order to make driver assistance systems more and more user-friendly, research on lane change assistance systems combined with driver characteristics is also being widely carried out. Cheng et al 14 established a standard for personalized driving models based on the measured data of vehicle dynamics and personalized driving characteristics. He introduced longitudinal adjustment parameters to comprehensively describe the longitudinal adjustment frequency and amplitude, while combined the lateral maximum acceleration, both of which serve as personalized identification labels.…”

Section: Introductionmentioning

confidence: 99%

A self-learning lane change motion planning system considering the driver’s personality

Gao

Zhu

Gao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Nowadays, with more and more attention being paid to the characteristics and experience of drivers, a large number of driver classification algorithms have emerged. However, these methods basically cannot be adjusted independently to each driver. Therefore, this paper proposes a self-learning lane change motion planning system considering the driver’s personality. Firstly, the method of driver data acquisition and processing is determined to obtain and extract the lane change data. Then, the planning system built in this paper is explained from two aspects: lane change trigger and lane change trajectory. According to the artificial potential field theory, an obstacle driving risk field is established to evaluate the acceptance of environmental risks of different drivers, and to achieve personalized lane change triggers through online statistics. At the same time, the safety of lane change is ensured by establishing the safety distance model of the target lane. On the other hand, the driver characteristic coefficient Jc and the driver reaction and operation time td are introduced into the traditional Gaussian-distributed model to establish a personalized lane change trajectory planning model, in which the parameters are obtained from offline and online learning. Offline learning is based on DTW for trajectory matching, and uses AP clustering to obtain the generalized parameters; Online learning uses LSTM to achieve personalized updates. Finally, this paper selected 15 drivers for verification, and the results show that the motion planning system can well reproduce the lane change behavior of the driver.

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Study on control strategy for personalised lane‐change on highway

Cited by 2 publications

References 7 publications

A Collaborative Merging Strategy with Lane Changing in Multilane Freeway On-Ramp Area with V2X Network

A Collaborative Merging Strategy with Lane Changing in Multilane Freeway On-Ramp Area with V2X Network

A self-learning lane change motion planning system considering the driver’s personality

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