A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Hu, Luojia; Ou, Jian; Huang, Jing; Chen, Yimin; Cao, Dongpu

doi:10.1109/access.2020.2970164

Cited by 83 publications

(39 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the same time, the data from VISSIM is transferred to MATLAB, where the proposed algorithm is working and gives the result about the queue length. As shown in [12], the penetration rates may impose a great effect on the estimate accuracy and can be estimated by V2X [51]. Therefore, several penetration rate levels are validated in the simulation.…”

Section: Resultsmentioning

confidence: 99%

“…It seems that the connected vehicles can provide some service for traffic states sensing when travelling on the road [11]. The connected vehicle can act as a mobile traffic information sensor [12,13]. In a mixed traffic scenario, the information from connected vehicle is used in delicate traffic models to estimate the traffic volume at the intersection [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

View full text Add to dashboard Cite

Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Comfort and safety are important considerations in the automotive industry, 1,2 and the ride comfort and handling stability are direct manifestations of these two properties. Suspension systems are used to transfer forces and moments between the car body and the ground.…”

Section: Introductionmentioning

confidence: 99%

Semi‐active control of air suspension with auxiliary chamber subject to parameter uncertainties and time‐delay

Zhang

Wang

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Suspension systems are critical to the ride comfort and handling stability of vehicles, but traditional passive suspensions fail to achieve optimal performance in the two aspects. Semi-active suspension controller changes the suspension stiffness or damping to improve the ride comfort and handling stability, which has the potential advantages of low energy consumption and high reliability. In this paper, the model of an air spring with an auxiliary chamber and a variable throttle orifice is proposed and the semi-active control strategy of air suspension is carried out. The parameters of the air spring with auxiliary chamber are optimized to improve the performance when the control system fails and to increase the basic performance for semi-active control. Based on the linear matrix inequality approach to robust H ∞ control, the semi-active control strategy of the air suspension is studied, in which parameter uncertainties and time delay are considered to improve control robustness. An inverse model of the air spring is then established to calculate the area of the variable throttle orifice, by which the desired control force is tracked accurately. Finally, the effectiveness of the semi-active control system of the air suspension is verified by numerical simulation. Comparing with the passive suspension, not only under nominal parameters, but also with parameter uncertainties, time delay, and both parameter uncertainties and time delay, the semi-active control system proposed in this paper has good control performance as well as is strong robustness to parameter uncertainties and time delay. K E Y W O R D S air suspension, parameter uncertainties, robust control, semi-active control, time delay 1 INTRODUCTION Comfort and safety are important considerations in the automotive industry, 1,2 and the ride comfort and handling stability are direct manifestations of these two properties. Suspension systems are used to transfer forces and moments between the car body and the ground. Thus, the suspension design must be improved to enhance the ride comfort and handling stability of vehicles. However, traditional passive suspensions fail to achieve optimal performance in both ride comfort and handling stability. 3,4 In recent years, in-wheel-motored electric vehicles have become a research hotspot because of their compact structure, high transmission efficiency, and 7130

show abstract

“…In recent years, one of the most important goals in the automotive industry has been to offer passengers the highest level of safety, comfort, and efficiency by partially or completely removing driving duties from humans. Advanced Driver Assistant System (ADAS) has become a research hotspot in the field of intelligent transportation; it not only improves the road capacity [1], but also ensures the safety of drivers and vulnerable road users to some extent [2,3]. Studies have shown that the active safety systems, such as adaptive cruise control, electronic stability control, or lane keeping assistant, which are already on the automotive market, can improve safety by decreasing the number of traffic accidents, among which the ACC helps a lot to reduce the driver's work intensity; an ACC equipped vehicle uses radar or other sensors that detect the distance and speed to other preceding vehicles (downstream vehicles) on the highway.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise Control Strategy Design with Optimized Active Braking Control Algorithm

Zou

et al. 2020

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

The braking quality is considered as the most important performance of the adaptive control system that influences the vehicle safety and ride comfort remarkably. This research is aimed at designing an adaptive cruise control (ACC) system based on active braking algorithm using hierarchical control. Taking into account the vehicle with safety and comfort, the upper decision-making controller is designed based on model predictive control algorithm. Throttle controller and braking controller are designed with feedforward and feedback algorithms as the bottom controller, where the braking controller is designed based on the hydraulic braking model. The whole model is simulated collaboratively with Amesim, Carsim, and Simulink. By comparison with the full deceleration model, the results show that the proposed algorithm can not only make the vehicle maintain a safe distance under the premise of following the target vehicle ahead effectively but also provide favorable driving comfort.

show abstract

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Cited by 83 publications

References 84 publications

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

Semi‐active control of air suspension with auxiliary chamber subject to parameter uncertainties and time‐delay

Adaptive Cruise Control Strategy Design with Optimized Active Braking Control Algorithm

Contact Info

Product

Resources

About