Evaluating the Effects of Switching Period of Communication Topologies and Delays on Electric Connected Vehicles Stream With Car-Following Theory

Zhao, Hang; Li, Yongfu; Hao, Wei; Peeta, Srinivas; Wang, Yibing

doi:10.1109/tits.2020.3006122

Cited by 23 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each episode iteration round number T, training batch size, and neural network parameter rotation cycle transfer_cycle. (3) for an episode in Episodes do (4) Initial traffic state s t � s 0 (5) For t from 0 to T: (6) Selection behavior. (Output an integer with a range of 0 to 2 n features− 1 ): Select a t � argmax a Q(s, a, θ) with a probability of 1-epsilon, and randomly select the behavior a t with a probability of epsilon.…”

Section: Parameter Impact Analysismentioning

confidence: 99%

“…According to the existing traffic states, the changing trend in a short time is predicted, and then, the information platform is used to issue an early warning to divert the traffic to avoid or ease congestion [2][3][4]. erefore, how to establish a long-term model for timely warning of traffic congestion is the research focus of urban intelligent transportation system optimization [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

Hao

Rong

et al. 2020

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.

show abstract

Section: Parameter Impact Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

Hao

Rong

et al. 2020

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, connected and automated vehicles (CAVs) including vehicle to vehicle (V2V) communication and vehicle to infrastructure (V2I) communication have emerged to improve driving safety and traffic efficiency [4][5][6][7][8]. Lane changing behavior is extensive but complex work while driving.…”

Section: Introductionmentioning

confidence: 99%

The Cooperative Car-Following Model With Consideration of the Stimulatory Effect of Lane-Changing Types

et al. 2021

View full text Add to dashboard Cite

Lane changing behavior is one of the most important tasks in driving. Cooperative lane change based on vehicle-vehicle (V2V) communication technology is getting extensive attention due to its potential to improve traffic safety and increase efficiency. In order to explore the internal mechanism of cooperative lane change behavior, the cooperative car-following model considering the stimulatory effect of lane-changing types is proposed in this paper. We investigate three lane changing types (free lane change, forced lane change, and cooperative lane change) to impact cooperative lane changing behavior. Based on this analysis, cooperative level function is presented. By using linear stability theory, the stability criterion of the proposed model is obtained. Simulation scenarios are set in three typical situations (starting process, stopping process and perturbation process) to analyze FVD model, cooperative driving for non-lane mode and cooperative driving for lane-changing mode. Simulation results indicate that cooperative driving of lane-changing mode has larger stable region compared to FVD model, cooperative driving for non-lane mode. Furthermore, the higher the level of cooperation, the greater the area of stability. Meanwhile, the simulation experiment also shows that response capability and smoothness of cooperative driving for lanechanging mode is be effectively promoted to the acceleration, velocity evolution. INDEX TERMSCooperative car-following model, Lane-changing types, Stimulatory effect, Driving behavior.

show abstract

“…Longitudinal control, as an essential part of the vehicle motion control system, will benefit the development of ADAS and autonomous driving. Therefore, the stability, accuracy, and efficiency of the longitudinal control strategy and algorithm are more emphasised, and definitely can contribute to the safety, riding comfort, fuel consumption reduction, and high traffic efficiency, while the existing subfunctions of ADAS, such as adaptive cruise control (ACC), autonomous emergency braking, will be strengthened [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Multi‐mode switching‐based model predictive control approach for longitudinal autonomous driving with acceleration estimation

Zhao

Gao

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

This study proposes a multi-mode switching longitudinal autonomous driving system based on model predictive control (MPC) with acceleration estimation of proceeding vehicle. A hierarchical control framework composed of three layers is utilised. In the first layer, five longitudinal driving scenarios are defined based on emergency degree. In the second layer, the MPC for longitudinal autonomous driving is designed and serving as the upper controller. Among which a non-linear tracking differentiator is used for acceleration estimation of preceding vehicle. In the third layer, the inverse longitudinal vehicle system dynamic model with strong non-linearity is considered in the lower controller. Proportional-integral-derivative feedback and feedforward control are combined to track the desired acceleration. Simulation and hardware-in-loop test results show that the multi-mode switching longitudinal autonomous driving system is feasible and effective, and has important value for engineering application.

show abstract

Evaluating the Effects of Switching Period of Communication Topologies and Delays on Electric Connected Vehicles Stream With Car-Following Theory

Cited by 23 publications

References 31 publications

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

The Cooperative Car-Following Model With Consideration of the Stimulatory Effect of Lane-Changing Types

Multi‐mode switching‐based model predictive control approach for longitudinal autonomous driving with acceleration estimation

Contact Info

Product

Resources

About