Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle

Ni, Jie; Han, Jingwen; Dong, Fei

doi:10.1155/2020/8672928

Cited by 29 publications

(12 citation statements)

References 14 publications

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“…e vision-based processing algorithm is embedded in the vehicle to detect lane lines from captured video clips. en the results of lane detection will be applied to subsequent tasks including lane keeping [2], trajectory planning, and behavior prediction. erefore, lane detection is an integral part of automatic driving and ADAS.…”

Section: Introductionmentioning

confidence: 99%

“…e primary reasons come from two aspects: (1) In terms of accuracy, the segmentation of the boundary is less precise, especially when the lines are in distance and suffering occlusion. (2) In terms of computing resources, excessive computation and parameter overheads lead to large memory usage and deficient real-time performance, which limits the practicability of algorithm. In this paper, our motivation is to design an improved neural network architecture to compensate for the aforementioned dilemmas and execute a trade-off between high accuracy and low memory consumption.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network

Yao

Chen

2022

Journal of Advanced Transportation

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For self-driving vehicles, detecting lane lines in changeable scenarios is a fundamental yet challenging task. The rise of deep learning in recent years has contributed to the thriving of autonomous driving. However, existing methods of lane detection based on deep learning have high requirements on computing environment, so their applicability is further restricted. This paper proposed an improved attention deep neural network (DNN), a lightweight semantic segmentation architecture catering for efficient computation in low memory, which contains two branches worked in different resolution. The proposed network integrates fine details captured by local interaction of pixels at high resolution into global contexts at low resolution, computing dense feature maps for prediction task. Based on the attributes of disparate feature resolution characteristics, different attention mechanisms are adopted to guide the network to effectively exploit the model parameters. The proposed network achieves comparable results with state-of-the-art methods on two popular lane detection benchmarks (TuSimple and CULane), with faster calculation efficiency at 259 frames-per-second (FPS) on CULane dataset, and the total number of model parameters only requires 1.57 M. This study provides a practical and meaningful reference for the application of lane detection in memory constrained devices.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network

Yao

Chen

2022

Journal of Advanced Transportation

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“…Ni et al. (2020) developed a multivehicle LC strategy for CAVs with a gain function based on an incentive model. Ni et al.…”

Section: Introductionmentioning

confidence: 99%

Simulation of mixed traffic with cooperative lane changes

Huang

et al. 2021

Computer aided Civil Eng

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This study proposes a mixed traffic simulation framework that integrates vehicle car‐following (CF) and lane‐changing (LC) with connected and automated vehicles (CAVs) of different cooperation behaviors. This framework is centered at a CAV LC model incorporated with CF dynamics in mixed traffic. The model was calibrated and validated using data collected from small‐scale field experiments in a previous study. To demonstrate a large‐scale application of this framework, PTV Vissim was used to implement the framework on a segment of Interstate 75 highway. Sensitivity analyses were conducted to investigate the impacts of key parameters on traffic mobility and stability performance. The results show that traffic performance degraded as the traffic demand and vehicle diverging rate increased. As the CAV penetration rate increased, traffic performance fluctuated when CAVs were more conservative. As the CAV cooperation rate, incentive criterion threshold, and incentive criterion bias increased, mobility and stability performance first improved and then degraded. When CAV platooning was considered, traffic performance was enhanced. These findings shed light on mixed traffic management from the perspectives of both transportation operators (e.g., facilities and policies to promote vehicle cooperation) and automakers (e.g., tuning parameters in their LC models) to achieve the best traffic performance.

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“…established a multi‐objective optimization function of the cooperative lane change, which divided the lane change process into the changing stage and the headway adjustment stage. Based on the US NGSIM open‐source traffic flow database, the proposed strategy's accuracy and feasibility were verified [43].…”

Section: Introductionmentioning

confidence: 99%

A cooperative lane change approach for heterogeneous platoons under different communication topologies

Nie

Xie

et al. 2021

IET Intelligent Trans Sys

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To improve the platoon's passability and traffic efficiency, a cooperative lane change approach is proposed when the platoon encounters a slow obstacle vehicle ahead in this paper. The algorithm is designed for the heterogeneous platoon, that is, the vehicles are dynamically decoupled and have different parameters. The vehicles in the platoon can have different communication topologies. To complete the lane change process quickly, all vehicles in the platoon perform simultaneously. The approach is mainly divided into two parts: trajectory planning and cooperative control. In the trajectory planning part, combined with the polynomial method and the optimization method, the leading vehicle plans an optimal lane change trajectory according to its own states and the obstacle vehicle's states. A distributed model predictive control algorithm is designed in the cooperative control part to meet the requirement that all vehicles should change lanes simultaneously. Besides, the sufficient conditions to achieve the asymptotic stability are also given, and the simulation results can show the effectiveness of the algorithm.

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Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle

Cited by 29 publications

References 14 publications

Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network

Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network

Simulation of mixed traffic with cooperative lane changes

A cooperative lane change approach for heterogeneous platoons under different communication topologies

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