scholarly journals Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

2021 ◽  
Author(s):  
Jiangchen Li ◽  
Liqun Peng ◽  
Tony Z. Qiu
Keyword(s):  
Real Time ◽  
Connected Vehicle ◽  
Platoon Dispersion

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

scholarly journals RVE-CV2X: A Scalable Emulation Framework for Real-Time Evaluation of CV2X-based Connected Vehicle Applications

2020 ◽  
Author(s):  
Ghayoor Shah ◽  
Md Saifuddin ◽  
Yaser P. Fallah ◽  
Somak Datta Gupta
Keyword(s):  
Real Time ◽  
Connected Vehicle ◽  
Time Evaluation

Real-Time Detector-Free Adaptive Signal Control with Low Penetration of Connected Vehicles

2018 ◽  
Vol 2672 (18) ◽  
pp. 35-44 ◽  
Author(s):  
Yiheng Feng ◽  
Jianfeng Zheng ◽  
Henry X. Liu
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Penetration Rate ◽  
Traffic Volume ◽  
Signal Control ◽  
Connected Vehicle ◽  
Estimation Model ◽  
Loop Detectors ◽  
Adaptive Signal Control ◽  
Adaptive Signal

Most of the existing connected vehicle (CV)-based traffic control models require a critical penetration rate. If the critical penetration rate cannot be reached, then data from traditional sources (e.g., loop detectors) need to be added to improve the performance. However, it can be expected that over the next 10 years or longer, the CV penetration will remain at a low level. This paper presents a real-time detector-free adaptive signal control with low penetration of CVs ([Formula: see text]10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function. If no CV is observed during one signal cycle, historical traffic volume is used to generate signal timing plans. The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates. Results show that the new model outperforms well-tuned actuated control regarding delay reduction, in all scenarios under only 10% penetrate rate. The results also suggest that the accuracy of historical traffic volume plays an important role in the performance of the algorithm.

Real-Time Powertrain Optimization Strategy for Connected Hybrid Electrical Vehicle

2016 ◽  
Author(s):  
Mohd Azrin Mohd Zulkefli ◽  
Zongxuan Sun
Keyword(s):  
Real Time ◽  
Numerical Solutions ◽  
Computation Time ◽  
Computational Effort ◽  
Linear Functions ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Optimization Strategy ◽  
Separable Programming

Connected Vehicle (CV) technology, which allows traffic information sharing, and Hybrid Electrical Vehicles (HEV) can be combined to improve vehicle fuel efficiency. However, transient traffic information in CV environment necessitates a fast HEV powertrain optimization for real-time implementation. Model Predictive Control (MPC) with Linearization is proposed, but the computational effort is still prohibitive. The Equivalent Consumption Minimization Strategy (ECMS) and Adaptive-ECMS are proposed to minimize computation time, but unable to guarantee charge-sustaining-operation (CS). Fast analytical result from Pontryagin’s Minimum Principles (PMP) is possible but the input has to be unconstrained. Numerical solutions with Linear Programming (LP) are proposed, but over-simplifications of the cost and constraint functions limit the performance of such methods. In this paper, a nonlinear CS constraint is transformed into linear form with input variable change. With linear input and CS constraints, the problem is solved with Separable Programming by approximating the nonlinear cost with accurate linear piecewise functions which are convex. The piecewise-linear functions introduce new dimensionless variables which are solved as a large-dimension constrained linear problem with efficient LP solvers. Comparable fuel economy with Dynamic Programming (DP) is shown, with maximum fuel savings of 7% and 21.4% over PMP and Rule-Based (RB) optimizations. Simulations with different levels of vehicle speed prediction uncertainties to emulate CV settings are presented.

scholarly journals A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

2021 ◽  
Author(s):  
M Sabbir Salek ◽  
Sakib Mahmud Khan ◽  
Mizanur rahman ◽  
Hsien-wen Deng ◽  
Mhafuzul islam ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Communication Networks ◽  
Future Research ◽  
Wireless Communication Networks ◽  
Connected Vehicle ◽  
Transportation Services ◽  
Viable Solution ◽  
Exchange Data

In an internet-of-things (IoT) environment, cloud computing is emerging as a technologically feasible and economically viable solution for supporting real-time and non-real-time connected vehicle (CV) applications due to its unlimited storage, enormous computing capabilities, and cost advantage, i.e., cloud computing costs less than owning such systems. However, maintaining cybersecurity is a major challenge in cloud-supported CV applications as it requires CVs and various transportation or non-transportation services to exchange data with the cloud via multiple wired and wireless communication networks, such as long-term evolution (LTE) and Wi-Fi. In this paper, we review the cybersecurity requirements of cloud-supported CV applications, such as confidentiality, integrity, availability, authentication, accountability, and privacy. Our review also identifies the associated cybersecurity challenges that might impact cloud-supported CV applications and corresponding solutions to these challenges. In addition, we present future research opportunities to prevent and mitigate cybersecurity issues in cloud computing for CV-related applications.

scholarly journals Real-Time Hardware-In-the-Loop Emulation Framework for DSRC-Based Connected Vehicle Applications

2019 ◽  
Author(s):  
Ghayoor Shah ◽  
Rodolfo Valiente ◽  
Nitish Gupta ◽  
S. M. Osman Gani ◽  
Behrad Toghi ◽  
...  
Keyword(s):  
Real Time ◽  
Connected Vehicle ◽  
Hardware In The Loop

scholarly journals Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2059 ◽  
Author(s):  
Kai Gao ◽  
Farong Han ◽  
Pingping Dong ◽  
Naixue Xiong ◽  
Ronghua Du
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Queue Length ◽  
Penetration Rate ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Mixed Traffic ◽  
Model Based ◽  
Sensing Model

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

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