Cooperative controllers for highways based on human experience

Pérez, Joshue; Milanés, Vicente; Godoy, Jorge; Villagrá, Jorge; Onieva, Enrique

doi:10.1016/j.eswa.2012.08.011

Cited by 26 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 15 samples are divided into three groups according to their velocity, and the comparison results of the fitted values and actual values in different velocity ranges are shown in Table 5. It can be seen that there is certain difference of the fitted results for different velocity ranges, of which the trajectory planning for the velocity range [10,20] has the worst fitted performance.…”

Section: Model Validationmentioning

confidence: 92%

“…A cooperative driving architecture was presented to enable direct vehicles to adapt their motion to surrounding traffic situation by utilizing information obtained from other vehicles and infrastructure in the vicinity [19]. Considering driver experience, a cooperative controller for lane-changing maneuver was proposed to perform cooperative adaptive cruise control by communicating with several involved vehicles [20]. In addition, Feng et al [5] also designed a lane change trajectory planning and tracking control model based on the cooperative vehicle infrastructure system.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Structured trajectory planning of collision-free lane change using the vehicle-driver integration data

Zhang

Yan

2016

Sci. China Technol. Sci.

View full text Add to dashboard Cite

In this paper, the structured trajectory planning of lane change in collision-free road environment is studied and validated using the vehicle-driver integration data, and a new trajectory planning model for lane change is proposed based on linear offset and sine function to balance driver comfort and vehicle dynamics. The trajectory curvature of the proposed model is continuous without mutation, and the zero-based curvature at the starting and end points during lane change assures the motion direction of end points in parallel with the lane line. The field experiment are designed to collect the vehicle-driver integration data, such as steering angle, brake pedal angel and accelerator pedal angel. The correction Correlation analysis of lane-changing maneuver and influencing variables is conducted to obtain the significant variables that can be used to calibrate and test the proposed model. The results demonstrate that vehicle velocity and Y-axis acceleration have significant effects on the lane-changing maneuver, so that the model recalibrated by the samples of different velocity ranges and Y-axis accelerations has better fitted performance compared with the model calibrated by the sample trajectory. In addition, the proposed model presents a decreasing tendency of the lane change trajectory fitted MAE with the increase of time span of calibrating samples at the starting stage. intelligent vehicle, lane change, trajectory planning, vehicle-driver integration Citation: Wang J F, Zhang Q, Zhang Z Q, et al. Structured trajectory planning of collision-free lane change using the vehicle-driver integration data. Sci China Tech Sci, 2016, 59: 825−831,

show abstract

Section: Model Validationmentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Structured trajectory planning of collision-free lane change using the vehicle-driver integration data

Zhang

Yan

2016

Sci. China Technol. Sci.

View full text Add to dashboard Cite

show abstract

“…-Adaptive cruise control for the vehicles controlled by the driver; road following control for automated driving; collision prevention systems [80][81][82], -Processing sensor information; recognition of driving environment parameters; vehicle localization [83][84][85][86][87][88][89], -Coordination of road vehicle platoon systems [90][91][92], -Control architecture of autonomous vehicles [93][94][95][96]; -Specific problems of brake, traction and steering control systems of autonomous vehicles [97][98][99]; -Automated parking systems [100][101][102].…”

Section: Fuzzy Methods and Vehicle-environment Interactionmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Purpose The paper addresses evolution of fuzzy systems for core applications of automotive engineering. Methods The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles. A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving environment identification, ride comfort control, and energy management of electric vehicles. Results The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain. In particular, it concerns driver identification, humanmachine interface, recognition of road conditions, and controllers of vehicle chassis and powertrain systems. It is found out that fuzzy methods have the primary use most of all for tasks requiring identification and forecasting procedures, especially in conditions of limited informational space. Additional observation that can be also derived from the presented survey points to reasonable integration of fuzzy technique with other control engineering methods to improve the performance of automotive control systems. Conclusions In the aggregate the performed review indicates that fuzzy computing can be considered as a versatile tool for automotive engineering applications of different nature.

show abstract

“…In fact, intelligent vehicles (IVs)/autonomous vehicles (UVs) have been regarded as the sixth disruptive technology, and in a close future, IVs/UVs will coexist with conventional vehicles in one's daily life [4] . Advanced driver assistance system (ADAS) is the most important component/development for intelligent vehicles [5] . ADAS involves with three key techniques, vehicle sensing, vehicle position system, and intelligent control, to achieve unmanned control for IVs.…”

Section: Introductionmentioning

confidence: 99%

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Zhang

et al. 2018

IEEE Intell. Transport. Syst. Mag.

View full text Add to dashboard Cite

This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the γ-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the step-by-step reduction of the value of γ. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of γ and speed adaption under different road geometries.

show abstract

Cooperative controllers for highways based on human experience

Cited by 26 publications

References 24 publications

Structured trajectory planning of collision-free lane change using the vehicle-driver integration data

Structured trajectory planning of collision-free lane change using the vehicle-driver integration data

A review of fuzzy methods in automotive engineering applications

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Contact Info

Product

Resources

About