Vehicle sideslip estimation

Grip, Håvard Fjær; Imsland, Lars; Johansen, Tor Arne; Kalkkuhl, Jens; Suissa, Avshalom

doi:10.1109/mcs.2009.934083

Cited by 107 publications

(4 citation statements)

References 27 publications

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“…Figure 9c shows the simulation result of the vehiclebody-side-slip angle using the MBVD framework with clip #13's steering wheel angle and vehicle speed datasets under no braking condition. The vehicle-body-side-slip angle is defined as the angle between the vehicle orientation and travel direction at the CoG [26]. A distinct peak is identified in Figure 9c, where the peak reflected a noticeable change of vehicle-body-side-slip angle response occurred during the right-hand side lane departure event in-between time frame of 8 th second until 13 th second.…”

Section: Resultsmentioning

confidence: 98%

Lane Departure Warning Estimation Using Yaw Acceleration

2020

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Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

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Section: Resultsmentioning

confidence: 98%

Lane Departure Warning Estimation Using Yaw Acceleration

2020

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“…Indicator, vehicle and tireroad friction coefficient [12] Single-track model Extended Kalman Filter and Sliding Model Observer Tire forces and sideslip angle [13] Kinematics model Nonlinear observer Vehicle sideslip angle [14] Longitudinal dynamics Recursive Least Square Vehicle mass [15] Vehicle planar model, Vehicle wheel dynamics model and…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

2022

IJEM

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Estimation of vehicle dynamics, tire wear, and road profile are indispensable prefaces in the development of automobile manufacturing due to the growing demands for vehicle safety, stability, and intelligent control, economic and environmental protection. Thus, vehicle state estimation approaches have captured the great interest of researchers because of the intricacy of vehicle dynamics and stability control systems. Over the last few decades, great enhancement has been accomplished in the theory and experiments for the development of these estimation states. This article provides a comprehensive review of recent advances in vehicle dynamics, tire wear, and road profile estimations. Most relevant and significant models have been reviewed in relation to the vehicle dynamics, roll angle, tire wear, and road profile states. Finally, some suggestions have been pointed out for enhancing the performance of the vehicle dynamics models.

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“…Several works, e.g., [26][27][28][29][30][31][32], propose a basic application of the KF observer (typically an EKF) to a very complex seven DOF (Degrees of Freedom) vehicle model with a fully non-linear tyre model (Pacejka or Dugoff model). As previously mentioned, a compromise between vehicle model complexity, results, accuracy, and computational burden should be considered.…”

Section: Kalman Filtermentioning

confidence: 99%

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

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Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or "drifting" angle) is defined as the angle between the vehicle's longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.

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Vehicle sideslip estimation

Cited by 107 publications

References 27 publications

Lane Departure Warning Estimation Using Yaw Acceleration

Lane Departure Warning Estimation Using Yaw Acceleration

Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

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