Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Hidalgo, Carlos; Lattarulo, Ray; Flores, Carlos; Rastelli, Joshué Pérez

doi:10.3390/s21082626

Cited by 23 publications

(5 citation statements)

References 46 publications

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“…Therefore, a balance between accuracy and complexity should be struck. The first two points affect the parameters in Equations ( 2) and ( 4)- (6). There are specific patterns of change over a short period of time.…”

Section: Vehicle Longitudinal Dynamics Modelmentioning

confidence: 99%

“…Nevertheless, conventional ACC systems have limitations in terms of energy efficiency, driver comfort, and tracking stability. To address these challenges, researchers have proposed various ACC extensions, including cooperative adaptive cruise control (CACC) [6][7][8], personalized adaptive cruise control (PACC) [9][10][11], and energy-optimal adaptive cruise control (EACC) [1,[12][13][14][15]. CACC enables multi-vehicle cooperation through vehicle communication systems, while PACC tailor sand simulates individual driving habits.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Optimal Adaptive Control Based on Model Predictive Control

Hao

2023

Sensors

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Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN). The proposed MPC improves safety and tracking performance while further reducing energy consumption. The final simulation results show that the proposed algorithm has a stronger energy-saving capability compared to previous studies and always maintains an appropriate relative distance and relative speed to the vehicle in front, verifying the effectiveness of the proposed algorithm.

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Section: Vehicle Longitudinal Dynamics Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Optimal Adaptive Control Based on Model Predictive Control

Hao

2023

Sensors

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“…From literature reviews, much research has been conducted on mixed traffic flow modeling to study its characteristics under normal weather conditions. Some studies focused on the headway of the CAVs during the car-following process to describe the longitudinal dynamics of the mixed traffic [7][8][9]. Some studies focused on the possible impact of autonomous vehicles on traffic flow and found that the traffic throughput increases at a high CAV penetration rate [10,11].…”

Section: Introductionmentioning

confidence: 99%

Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model

et al. 2022

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Mixed traffic composed of human-driven vehicles (HDVs) and CAVs will exist for an extended period before connected and autonomous vehicles (CAVs) are fully employed on the road. There is a consensus that dense fog can cause serious traffic accidents and reduce traffic efficiency. In order to enhance the safety, mobility, and efficiency of highway networks in adverse weather conditions, it is necessary to explore the characteristics of mixed traffic. Therefore, we develop a novel cellular automata model for mixed traffic considering the limited visual distance and exploring the influence of visibility levels and CAV market penetration on traffic efficiency. We design acceleration, deceleration, and randomization rules for different car-following scenes. For lane-changing, considering the interaction of CAVs and surrounding vehicles, we introduce game theory (GT) to lane-changing policies for CAVs. This paper presents the following main findings. In reduced visibility conditions, the introduction of CAVs is beneficial to improve mixed traffic efficiency on metrics such as free-flow speed and traffic capacity (e.g., 100% CAVs could increase the traffic capacity up to around 182% in environments of dense fog). In addition, the critical density increases as the proportion of CAVs increases, which is more pronounced in conditions of dense fog according to the simulation results. In addition, we compared the proposed GT-based lane-changing strategy to the traditional STCA lane-changing strategy. The results showed that the average speed is significantly improved under the proposed lane-changing strategy. The model presented in this paper can evaluate the overall performance and provide a reference for future management and control of mixed traffic flow in fog conditions.

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“…Nowadays, in different industrial applications, the end effector is utilized by the robot manipulator to permit via the preferred curve to avoid collision with an obstacle in the robot environment (Qian et al, 2020). This issue is most challenging, still, scholars have established particular comparatively efficient solutions (Hidalgo et al, 2021; Liu et al, 2019). Moreover, in robotic applications like painting, welding, and part assembly, online procedures for continuous paths or tracking control can be found (Reiter et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Singh

Banga

2022

Journal of Field Robotics

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In this paper, an optimal collision-free trajectory is developed based on the hybrid optimization algorithms for industrial robotic manipulators (IRMs). Three IRMs such as PUMA 560 (six degrees of freedom-6DOF), KUKA LBR iiwa 14 R820 (7DOF), and ABB IRB 140 (6DOF) are considered. The key objective is to enhance the smoothness and efficiency of manufacturing robots by optimum joint trajectory design using the seventh-order polynomial function. The proposed approach is to solve both kinematics and trajectory planning problems by using the different combinations of the hybrid meta-heuristic algorithms. The kinematic parameters including jerk, acceleration, and velocity mostly impact the travel smoothness of the robot end-effector on the trajectory path. Therefore, these parameters are to be constrained for generating the collision-free path. The endurance of velocity and acceleration can be obtained by reducing the jerk which leads to smooth robotic motion. The proposed work is executed using a robotic toolbox in MATLAB with a graphical user interface. The values of acceleration, velocity, and jerk are computed for the robot joints. Each robot obtained the minimum traveling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. From the experimental outcomes, the proposed scheme of the hybrid optimization algorithms is more effective for the trajectory planning of IRMs than that of other works.

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Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Cited by 23 publications

References 46 publications

Energy-Optimal Adaptive Control Based on Model Predictive Control

Energy-Optimal Adaptive Control Based on Model Predictive Control

Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model

Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

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