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Research Papers

Vehicle Collision Avoidance Maneuvers With Limited Lateral Acceleration Using Optimal Trajectory Control

[+] Author and Article Information
Damoon Soudbakhsh

Postdoctoral Associate
Active Adaptive Control Lab,
Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: damoon@mit.edu

Azim Eskandarian

Professor and Director of Center for Intelligent Systems Research,
School of Engineering and Applied Sciences,
The George Washington University,
Washington, DC 20052
e-mail: eska@gwu.edu

David Chichka

Research Assistant Professor
Mechanical Engineering Department,
The George Washington University,
Washington, DC 20052
e-mail: chichka@gwu.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 7, 2011; final manuscript received February 27, 2013; published online May 13, 2013. Assoc. Editor: Marco P. Schoen.

J. Dyn. Sys., Meas., Control 135(4), 041006 (May 13, 2013) (12 pages) Paper No: DS-11-1034; doi: 10.1115/1.4023943 History: Received February 07, 2011; Revised February 27, 2013

In this paper, the possibility of performing severe collision avoidance maneuvers using trajectory optimization is investigated. A two degree of freedom vehicle model was used to represent dynamics of the vehicle. First, a linear tire model was used to calculate the required steering angle to perform the desired evasive maneuver, and a neighboring optimal controller was designed. Second, direct trajectory optimization algorithm was used to find the optimal trajectory with a nonlinear tire model. To evaluate the results, the calculated steering angles were fed to a full vehicle dynamics model. It was shown that the neighboring optimal controller was able to accommodate the introduced disturbances. Comparison of the resultant trajectories with other desired trajectories showed that it results in a lower lateral acceleration profile and a smaller maximum lateral acceleration; thus the time to perform an obstacle avoidance maneuver can be reduced using this method. A simulation case study of a limited lateral acceleration with constrained direct trajectory optimization shows that using the proposed trajectory optimization technique requires less time than that of trapezoidal acceleration profile for a lane change maneuver.

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References

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Figures

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Fig. 1

Acceleration profile of TAP and 5th order polynomial

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Fig. 3

Linear and nonlinear tire model

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Fig. 4

Neighboring optimal control diagram

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Fig. 11

Trajectories with δ0 = 10 deg for: (1) Open loop, (2) NOC, and (3) Optimal inputs

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Fig. 12

Steering inputs with δ0 = 10 deg for: (1) Open loop, (2) NOC, and (3) Optimal inputs

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Fig. 13

Trajectories with Y0 = − 30 cm for: (1) Open loop, (2) NOC, and (3) Optimal inputs

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Fig. 15

Trajectory using control parameterization

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Fig. 16

Steering input using control parameterization

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Fig. 17

Lateral acceleration using control parameterization

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Fig. 18

Front tire slip angle using control parameterization

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Fig. 5

Open loop control of the full vehicle dynamics model

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Fig. 7

Optimized trajectory using shooting method

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Fig. 8

Optimal steering input using shooting method

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Fig. 9

Lateral acceleration using shooting method

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Fig. 10

Front tire slip angle using shooting method

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Fig. 14

Steering inputs with Y0 = −30 cm for: (1) Open loop, (2) NOC, and (3) Optimal inputs

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Fig. 22

Steering input of control parameterization and ay < 5 m/s2

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Fig. 23

ay of control parameterization and ay < 5 m/s2

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Fig. 24

αf of control parameterization and ay < 5 m/s2

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Fig. 25

Full vehicle dynamics model trajectory with shooting method input

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Fig. 26

ay of the full vehicle dynamics model, shooting method input

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Fig. 27

Full vehicle dynamics model trajectory with control parameterization

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Fig. 28

ay of the full vehicle dynamics model with control parameterization input

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Fig. 19

Shooting method trajectory versus control parameterization method with nonlinear tire model

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Fig. 20

αf using linear tire model in shooting method versus nonlinear tire model in control parameterization

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Fig. 21

Trajectory of control parameterization and ay < 5 m/s2

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Fig. 29

CarSim simulation: trajectory of the vehicle at 25 m/s

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Fig. 30

Trajectory of full vehicle model of control parameterization and ay < 5 m/s2

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Fig. 31

ay of full vehicle model with control parameterization ay < 5 m/s2

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