Parameter Studies in Articulated Vehicle Handling

Eshleman, R. L.; Scopelite, T. M.; Desai, Sumit

doi:10.4271/730673

Cited by 11 publications

(4 citation statements)

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“…The lane change trajectory can be obtained by solving the abovementioned homogeneous linear equations. Finally, the expression in the X direction is substituted into the expression in the Y direction to obtain 30 :…”

Section: Lane Change Trajectory Planning Algorithm On Curvesmentioning

confidence: 99%

Research on the trajectory tracking of a curved road in an active lane change scenario based on model predictive control algorithm

Wang,

Qu,

Guo

et al. 2023

Advances in Mechanical Engineering

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In this paper, a control method for trajectory planning and tracking of an intelligent vehicle is proposed. In terms of trajectory planning, a trajectory planning method for curved lane changes is designed based on conventional lane change trajectory planning and considering the adaptive correction of road curvature. In addition, curve trajectory tracking control strategy based on model predictive control is designed. Model predictive control is suitable for multi-input and multi-output nonlinear models, and it has the advantage of considering model constraints. This type of control makes the model output more in line with vehicle dynamics characteristics and improves the trajectory tracking accuracy. Finally, the simulation shows that the method proposed in this paper can generate a reasonable curved lane-changing trajectory, and under the consideration of the vehicle dynamics constraints, the MPC algorithm is used to effectively follow the expected trajectory, so that the vehicle can change lanes smoothly.

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Section: Lane Change Trajectory Planning Algorithm On Curvesmentioning

confidence: 99%

Research on the trajectory tracking of a curved road in an active lane change scenario based on model predictive control algorithm

Wang,

Qu,

Guo

et al. 2023

Advances in Mechanical Engineering

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“…According to the research about the driver behaviors during lane change, 19–21 the lane change trajectories can be fitted by polynomial curves. Eshleman and Desai 22 and Nelson 23 both explored the use of a single polynomial to describe an ideal lane change maneuver path. The polynomial can be of any power, but high order polynomials increase complexity and computation time.…”

Section: Model-aided Bayesian Network Developmentmentioning

confidence: 99%

Driver intention prediction using model-added Bayesian network

Song

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The autonomous driving technology requires reliable detection and prediction of the surrounding environment. Predicting the lane change intention of the surrounding traffic is critical to evaluate the potential threat around the host vehicle. This paper develops a lane change maneuver prediction algorithm based on a newly proposed driver model combined with a Bayesian network. The innovation of the proposed algorithm is the utilization of the driver model while calibrating and executing the Bayesian network. The prediction algorithm can provide not only the driver’s intention but also the probability associated with the intention. The Next-Generation Simulation data sets are used to develop and validate the prediction model. In total, there are more than 2000 lane change events used in this paper. The result shows that the proposed prediction algorithm can provide an accurate prediction of the surrounding vehicle’s lane change maneuver.

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“…The procedure is straightforward. We assume a solution of the form Substituting in equation (5) and solving in the usual way yields Equation (9) indicates that the solution to equations (7) is always stable. This implies that, for the range of validity of these linear equations, all vehicles can be driven through any prescribed path at any speed with a bounded steer angle.…”

Section: James Bernard a N D M A R K P I C K E L M A N Nmentioning

confidence: 99%

An Inverse Linear Model of a Vehicle

Bernard

Pickelmann

1986

Vehicle System Dynamics

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It is common to use linear differential equations of vehicle motion to procure descriptions of vehicle performance. The solution to these equations describes the motion of the vehicle, usually given in terms of lateral acceleration, yaw rate, and sideslip angle, as a function of a steer angle. The equations many be termed open-loop in the sense that we give the steer angle and find out how the vehicle responds. This paper presents equations which we refer to as the inverse of the open loop procedure. In particular, the solution of the equations is steer angle, yaw rate and sideslip angle given as a function of lateral acceleration. The real part of the eigenvalues of these equations indicates that the model is always stable and well damped, and the imaginary part indicates that routine path-following tasks require only low frequency steer angles.

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Parameter Studies in Articulated Vehicle Handling

Cited by 11 publications

References 0 publications

Research on the trajectory tracking of a curved road in an active lane change scenario based on model predictive control algorithm

Research on the trajectory tracking of a curved road in an active lane change scenario based on model predictive control algorithm

Driver intention prediction using model-added Bayesian network

An Inverse Linear Model of a Vehicle

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