Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model

Polack, Philip; Altché, Florent; d’Andréa-Novel, Brigitte; Fortelle, Arnaud de La

doi:10.23919/acc.2018.8430886

Cited by 18 publications

(9 citation statements)

References 20 publications

(26 reference statements)

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“…While kinetic models provide accurate prediction of the vehicle motion, they might increase the complexity in the design of collision avoidance systems, and require, during the deployment, high computation time and small sampling interval. It has been shown in the literature that the kinematic model can provide reasonable accuracy by adding constraints on the rate of change of the inputs to be consistent with the low level controllers of the vehicle [14,15]. Therefore, the state space representation of the nonlinear kinematic bicycle model [16]…”

Section: Vehicle Modelingmentioning

confidence: 99%

“…where (16b) represents the initial condition, Equation (16c) represents the continuity condition between each two consecutive subintervals, Equations (16d) and (16e) represent the discretized constraints, andx i (t i+1 ) = s i+1 is the solution of the IVP (15). The NLP (16) is solved using sequential quadratic programming (SQP), with the aid of the method of Lagrange multipliers where the Hessian matrix is approximated using Gauss-Newton [25].…”

Section: Optimization Problem Implementationmentioning

confidence: 99%

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Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Abdelaal

Schön

2020

Algorithms

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This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bézier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bézier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.

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Section: Vehicle Modelingmentioning

confidence: 99%

Section: Optimization Problem Implementationmentioning

confidence: 99%

Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Abdelaal

Schön

2020

Algorithms

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“…The speed limit a kinematic bicycle model can reach in a curve of radius R is given by Equation ( 12) where µ = 1 is the road friction coefficient and g the gravity constant [4]. This corresponds to 9.9m/s (R = 20m) in road section n • 2 and 7.0m/s (R = 10m) in road section n • 6.…”

Section: Coupling Between Longitudinal and Lateral Dynamicsmentioning

confidence: 99%

“…However, precisely modeling this coupling involves complex non-linear relations between state variables, and using the resulting model is usually too costly for real-time applications. For this reason, most references in the field of motion planning mainly focus on simpler models, such as point-mass or kinematic bicycle (single track), which are constrained to avoid highly coupled dynamics [4]. Similarly, research on automotive control usually treats the longitudinal and lateral dynamics separately in order to simplify the problem [5].…”

Section: Introductionmentioning

confidence: 99%

Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning

Devineau

Polack

Altché

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference trajectory, using a dataset based on high-fidelity simulations of vehicle dynamics. In this study, control inputs are chosen as the steering angle of the front wheels, and the applied torque on each wheel. The performance of both models, namely a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), is evaluated based on their ability to drive the vehicle on a challenging test track, shifting between long straight lines and tight curves. A comparison to conventional decoupled controllers on the same track is also provided.In this section, we present the 9 Degrees of Freedom (9 DoF) vehicle model which is used both to generate the training and testing dataset, and as a simulation model to evaluate the performance of the deep-learning-based controllers.The Degrees of Freedom comprise 3 DoF for the vehicle's motion in a plane (V x ,V y ,ψ), 2 DoF for the carbody's rotation (θ ,φ ) and 4 DoF for the rotational speed of each wheel (ω f l , ω f r , ω rl , ω rr ). The model takes into account both the coupling of longitudinal and lateral slips and the load transfer between tires. The control inputs of the model are the torques T ω i applied at each wheel i and the steering angle δ of the front wheel. The low-level dynamics of the engine and brakes are not considered here. The notations are given in Table 1 and illustrated in Figure 1.Remark: the subscript i = 1..4 refers respectively to the front left ( f l), front right ( f r), rear left (rl) and rear right (rr) wheels.Several assumptions were made for the model:• Only the front wheels are steerable.• The roll and pitch rotations happen around the center of gravity.

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“…Being a complex control problem, two strategies have been proposed when implementing the motion controllers. First, a coupled strategy in which the lateral and longitudinal control are considered in a single strategy [8], [9], which implies a more complex control problem. Second, a decoupled strategy, which allows to simplify the control problem by designing a controller for each task: one maintaining the lateral position of the vehicle along the road, and other following a speed reference [10], [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models

Matute

Lattarulo

Pérez

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.

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Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model

Cited by 18 publications

References 20 publications

Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning

A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models

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