Kinematic and dynamic vehicle models for autonomous driving control design

Kong, Jason; Pfeiffer, Mark G.; Schildbach, Georg; Borrelli, Francesco

doi:10.1109/ivs.2015.7225830

Cited by 618 publications

(336 citation statements)

References 6 publications

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“…Following the same reasoning, a control architecture based on LTV-MPC is presented in [49] to address the lane keeping and obstacle avoidance problems for a passenger car driving on low curvature and low friction roads. A study about the use of kinematic and dynamic vehicle models for MPC control design is detailed in [50]. The authors show that the kinematic model has better forecast errors when discretized at 200 ms, compared to 100 ms.…”

Section: Motion Controllermentioning

confidence: 99%

“…On the other hand, simple models are less computationally demanding but are unable to provide good predictions. The movement of a car-like nonholonomic vehicle, such as a truck that performs curvy paths at low speeds (i.e., when the lateral dynamics have little influence) and drives straight at highspeeds can be described by its kinematics without significant loss of prediction accuracy [50].…”

Section: B Control Designmentioning

confidence: 99%

“…The vehicle model (2) is discretized and linearized around the reference path using the sample interval ∆s = vT s , where v is the current vehicle speed and T s = 200 ms is the sampling rate of the prediction model (motivated by the results from [50]). …”

Section: B Controllersmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck

Lima

Nilsson

Trincavelli

et al. 2017

IEEE Trans. Intell. Veh.

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Section: Motion Controllermentioning

confidence: 99%

Section: B Control Designmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck

Lima

Nilsson

Trincavelli

et al. 2017

IEEE Trans. Intell. Veh.

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“…The movement of a car-like nonholonomic vehicle, such as a truck, a car or a bus, at low speeds (i.e., when the lateral dynamics have little influence) can approximately be described by its kinematics without significant loss of prediction accuracy [10]. Therefore, the mathematical equations describing the vehicle's position (x, y), orientation θ and curvature κ over the traveled distance s are…”

Section: A Space-based Kinematic Vehicle Modelmentioning

confidence: 99%

“…There, the discrete time linear system is based on successive online linearizations of the nonlinear vehicle model, and the stability conditions for LTV-MPC controllers are also presented. A study about the use of kinematic and dynamic vehicle models for MPC control design is detailed in [10]. The authors show that the kinematic model has better forecast errors when discretized at 200 ms, compared to 100 ms.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of economic model predictive control for an autonomous truck

Lima

Trincavelli

Nilsson

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this paper, we propose a controller for smooth autonomous path following. The controller is formulated as an economic model predictive controller. The economic cost introduced in the objective function leads to a smooth driving, since we minimize the first and second derivatives of the curvature function (i.e., we encourage linear curvature profiles). Since the curvature in clothoids varies linearly with the path arc-length, we use the smoothness and comfort characteristics of clothoid-driving to obtain a compact and intuitive controller formulation. We enforce convergence of the controller to the reference path with soft constraints that avoid deviations from the reference path. Finally, we present real life experiments where the controller is deployed on a Scania construction truck that show that the proposed controller outperforms a purepursuit controller. Moreover, we detail how the few tuning parameters can affect the obtained solution in practice.

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An adaptive cascade predictive control strategy for connected and automated vehicles

Landolfi

Natale

2023

Adaptive Control & Signal

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SummaryConnectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

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Kinematic and dynamic vehicle models for autonomous driving control design

Cited by 618 publications

References 6 publications

Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck

Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck

Experimental evaluation of economic model predictive control for an autonomous truck

An adaptive cascade predictive control strategy for connected and automated vehicles

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