Vehicle Stability Control Through Pre-Emptive Braking

Guastadisegni, Giuseppe; So, Kai Man; Parra, Alberto; Tavernini, Davide; Montanaro, Umberto; Gruber, Patrick; Soria, Leonardo; Mantriota, Giacomo; Sorniotti, Aldo

doi:10.1007/s12239-023-0029-2

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…The speed limit was proportional to the available tire-road friction and inversely proportional to the reference yaw rate [9]. In addition, Guastadisegni et al proposed a real-time nonlinear model predictive control (NMPC) formulation based on a double track vehicle prediction model [10]. Zhou et al proposed a vehicle speed preview control method that utilizes road curvature information from high-definition maps [11].…”

Section: State Of the Artmentioning

confidence: 99%

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Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Lai,

Wang

2023

Applied Sciences

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A pre-emptive braking control method is proposed to improve the stability of autonomous vehicles during emergency collision avoidance, aiming to imitate the realistic human driving experience. A linear model predictive control is used to derive the front wheel steering angle to track a predefined fifth-degree polynomial trajectory. Based on a two-degrees-of-freedom (DOF) vehicle dynamics model, the maximum stable vehicle speed during collision avoidance can be determined. If the actual vehicle speed exceeds the maximum stable vehicle speed, braking action will be applied to the vehicle. Furthermore, four-wheel steering (4WS) control and direct yaw moment control (DYC) are employed to further improve the stability of the vehicle during collision avoidance. Simulation results under a double lane change scenario demonstrate that the control system incorporating pre-emptive braking, 4WS, and DYC can enhance the vehicle stability effectively during collision avoidance. Compared to the 2WS system without pre-emptive braking control, the maximum stable vehicle speed of the integrated control system can be increased by at least 56.9%. The proposed integrated control strategy has a positive impact on the safety of autonomous vehicles, and it can also provide reference for the research and development of autonomous driving systems.

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Section: State Of the Artmentioning

confidence: 99%

“…The torque is then transmitted to the motor through the torque distribution. The differential equations of the PID controller are expressed as follows in Equations ( 9) and (10).…”

Section: Dual Pid Longitudinal Motion Controlmentioning

confidence: 99%

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Lai,

Wang

2023

Applied Sciences

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On Antilock Braking Systems With Road Preview Through Nonlinear Model Predictive Control

Tavolo,

So,

Tavernini

et al. 2024

IEEE Trans. Ind. Electron.

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State-of-the-art antilock braking systems (ABS) are reactive, i.e., they activate after detecting that wheels tend to lock in braking. With vehicle-to-everything (V2X) connectivity becoming a reality, it will be possible to gather information on the tire-road friction conditions ahead, and use these data to enhance wheel slip control performance, especially during abrupt friction level variations. This study presents a nonlinear model predictive controller (NMPC) for ABS with preview of the tire-road friction profile. The potential benefits, optimal prediction horizon, and robustness of the preview algorithm are evaluated for different dynamic characteristics of the brake actuation system, through an experimentally validated simulation model. Proof-of-concept experiments with an electric vehicle prototype highlight the real-time capability of the proposed NMPC ABS, and the associated wheel slip control performance improvements in braking maneuvers with high-to-low friction transitions.

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Vehicle Stability Control Through Pre-Emptive Braking

Cited by 2 publications

References 25 publications

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

On Antilock Braking Systems With Road Preview Through Nonlinear Model Predictive Control

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