Model Predictive Control Approach to Design Practical Adaptive Cruise Control for Traffic Jam

Takahama, Taku; Akasaka, Daisuke

doi:10.20485/jsaeijae.9.3_99

Cited by 62 publications

(31 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, they argued that traffic can be increased, and fuel consumption and exhaust gas emissions could be expected to decrease [28]. However, since the ACC system only judges the situation ahead, it does not operate during reckless cut-ins or on sharp curves [29]. Moreover, according to Ploeg J. et al, there is a limit that the V2V systems precede in order to implement CACC.…”

Section: Limitations Of the Acc System As An Adasmentioning

confidence: 99%

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Kim

Yu²,

Kim³

et al. 2021

Sensors

View full text Add to dashboard Cite

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

show abstract

Section: Limitations Of the Acc System As An Adasmentioning

confidence: 99%

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Kim

Yu²,

Kim³

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The value P k | k − 1 is the state estimation error covariance matrix at time k based on information available at time k − 1 . 27,29–33…”

Section: Mathematical Modelsmentioning

confidence: 99%

“…A, B, C, and D are the state-space matrices. 5,27,30 Adaptive model predictive control (AMPC). Model predictive control (MPC) is a feedback control algorithm that uses a dynamic model to make predictions about future outputs of a process.…”

Section: Lateral Dynamicmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and dynamic analysis of a 6 x 6 heavy military truck by adaptive model predictive control with application to NATO lane change test course

Acuña

Rodrigues

Queiroz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

In this paper, the computer-aided vehicle dynamic analysis of a 6x6 heavy military truck is presented and examined. For the analysis, a MATLAB/Simulink® platform is used to design and model a truck. The vehicle configuration taken into account for the analysis is the powertrain (engine, gear box, transfer gear, differential), suspension, steering system and tire model according to the Pacekja 89’ formulation. In addition, the effect of the rolling resistance and drag is considered, in order to represent the vehicle behavior as real as possible. The longitudinal dynamic and lateral dynamic are formulated. First, the longitudinal dynamic model is established by means of implementation of the weight transfer function. The vehicles are considered as rigid bodies with 1 degree of freedom. Second, the vehicular planar model with three wheels, well known as bicycle model, is applied following the North Atlantic Treaty Organization double line change maneuver test reaching 3 degree of freedom. The driver behavior is represented by using an adaptive model predictive control varying the longitudinal velocity. The forces for braking, inertia of the rotating components, the energy lost in the powertrain, and the effect of dive squat and rollover. The numerical simulation results are shown and compared with a full-vehicle model formed by using Mechanical Simulation Corporation’s truckSIM®. There were chosen simulation scenarios applied to the model to observe the effects of different parameters concerning the dynamic behavior, and also prepared in truckSIM® environment. The main contributions of this article are the development of the vehicular model, through the use of block diagrams in a reliable and relatively simple programming code such as MATLAB/Simulink®, with innovative tools used in the control of autonomous vehicle driving and the flexibility to adapt said model to different environmental conditions and different vehicle parameters.

show abstract

“…To increase computational efficiency, it is advisable to use a simple prediction model with lesser dimensions [38]. Integrating ̈ over the sampling time gives the velocity of the ego vehicle (̇).…”

Section: Prediction Modelmentioning

confidence: 99%

MPC-Based Autonomous Driving Control With Localized Path Planning for Obstacle Avoidance and Navigating Signalized Intersections

Devaragudi¹

View full text Add to dashboard Cite

Model Predictive Control Approach to Design Practical Adaptive Cruise Control for Traffic Jam

Cited by 62 publications

References 4 publications

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Modeling and dynamic analysis of a 6 x 6 heavy military truck by adaptive model predictive control with application to NATO lane change test course

MPC-Based Autonomous Driving Control With Localized Path Planning for Obstacle Avoidance and Navigating Signalized Intersections

Contact Info

Product

Resources

About