Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

Zhang, Ziyu; Wang, Chunyan; Zhao, Wanzhong; Feng, Jian

doi:10.1177/09544070211024048

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…The physical input signals (signal builder) are valid only for the movement or longitudinal position of the vehicle body [54,55], considering that the steering wheel of the vehicle remains straight (parallel to the road) for the set of vehicle pedals, the acceleration is determined as the input only [56]. In addition, the minimum longitudinal force (𝐹 𝑥 ) required to brake the vehicle, can be calculated from Equation 1.…”

Section: Calibrations Parametersmentioning

confidence: 99%

Longitudinal Modeling of a Road Vehicle: 4-Wheel Traction

Manuel

Santos

Lenzi

et al. 2022

IJRCS

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This paper presents the longitudinal modeling of a 4-wheel traction vehicle represented in a block diagram using Matlab®/Simulink® software. The proposed modeling is suitable to be implemented in automatic parallel, oblique, or perpendicular parking systems considering speed cases between 5 km/h and 30 km/h. For the computational simulations, it was considered that the vehicle starts at rest and goes up a referenced or determined slope in degrees (°), with a sufficient rear reaction force to allow the vehicle to move until the engine produces sufficient torque. For the model of the tire variant, the magic formula (characterized by the sum of five vectors about an axis) was used. Three input signals were considered, slope, wind, and accelerator variation were considered in numerical simulations. The output signals are rear and normal front forces, vehicle speed, angular velocity, and engine acceleration. The longitudinal modeling proposed allows for easily reproducing the results and assigning new parameters to validate a Project, contributing positively both to the automotive industries and in innovation-based scientific research.

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Section: Calibrations Parametersmentioning

confidence: 99%

Longitudinal Modeling of a Road Vehicle: 4-Wheel Traction

Manuel

Santos

Lenzi

et al. 2022

IJRCS

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“…The vehicle active collision avoidance control system uses advanced information technology such as information processing technology and sensors to obtain external traffic environment information, such as relative speed and distance from pedestrians and front vehicles, and combines it with the driving conditions of self-propelled vehicles, so as to realize the identification of current vehicle safety risks. According to the degree of danger, corresponding control measures are automatically taken to ensure the safe operation of the car [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Comfort Optimization of the Active Collision Avoidance Control System of Electric Vehicles for Green Manufacturing

Liu

Zhang

et al. 2023

Processes

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The vehicle model was built based on MATLAB/Simulink and Carsim, and the multi-objective active collision avoidance control algorithm considering safety and comfort was established based on a model predictive control (MPC) algorithm. The vehicle active collision avoidance control system for comfort and safety was studied by simulation and experimentation. The results show that the active collision avoidance control system based on an MPC algorithm can follow the vehicle under different working conditions and ensure the safety and comfort in the process of following the vehicle while meeting the requirements of the active collision avoidance control system.

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“…Nakrani and Joshi [46] introduced a fuzzy-based obstacle avoidance controller with ulstrasonic sensor outputs as inputs to the FL to accomplish autonomous parallel parking in the presence of static and dynamic obstacles. In [47], the primary emphasis is on addressing the challenges of longitudinal and lateral control coupling to ensure safety and stability in collision avoidance scenarios. Another study referenced in [48] leverages the African Vulture Optimization Algorithm (AVOA), which was a new metaheuristic algorithm introduced in 2021 by Abdollahzadeh et al [49], inspired by the foraging and navigation behaviors of African vultures.…”

mentioning

confidence: 99%

A Hybrid HHO-AVOA for Path Planning of a Differential Wheeled Mobile Robot in Static and Dynamic Environments

Loganathan,

Ahmad

2024

IEEE Access

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This study presents a hybrid HHO-AVOA which is a novel optimization method that combines the strengths of Harris Hawks Optimization (HHO) and African Vulture Optimization Algorithm (AVOA) to address the path planning challenges encountered by differential wheeled mobile robots (DWMRs) navigating both static and dynamic environments, while accommodating kinematic constraints. By synergizing the strengths of both algorithms, the proposed hybrid method effectively mitigates the limitations of individual approaches, resulting in efficient and obstacle-avoiding navigation towards the target within reduced timeframes. To evaluate its efficiency, the proposed approach is compared against HHO and AVOA as well as other established methods which include whale optimization, grey wolf optimization and sine-cosine algorithms. Simulation results along with Monte Carlo analysis consistently demonstrate the superior performance of the hybrid method in both environments. In static scenarios, the hybrid algorithm achieves an average reduction of approximately 14% in path length and a 17% decrease in DWMR travel duration. In dynamic cases, it outperforms the rest with an average reduction of 27.6% in path length and a 27.2% decrease in travel duration. The algorithm's low computational complexity is also exhibited via its fast convergence during path optimization which is a crucial attribute for real-time implementation, particularly in dynamically changing environments that demand quick decision-making. The superiority of the proposed hybrid method to balance the exploration and exploitation is also affirmed through a Wilcoxon rank-sum test with a 95% confidence interval.

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Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

Cited by 9 publications

References 32 publications

Longitudinal Modeling of a Road Vehicle: 4-Wheel Traction

Longitudinal Modeling of a Road Vehicle: 4-Wheel Traction

Comfort Optimization of the Active Collision Avoidance Control System of Electric Vehicles for Green Manufacturing

A Hybrid HHO-AVOA for Path Planning of a Differential Wheeled Mobile Robot in Static and Dynamic Environments

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