4.7 Article

Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3194571

关键词

Trajectory; Autonomous vehicles; Optimization; Planning; Convex functions; Vehicle dynamics; Safety; Autonomous driving; iterative linear quadratic regulator (iLQR); differential dynamic programming (DDP); alternating direction method of multipliers (ADMM); motion planning; model predictive control (MPC); nonlinear system; non-convex optimization

资金

  1. Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone [HZQB-KCZYB-2020083]

向作者/读者索取更多资源

In this paper, a method using iterative linear quadratic regulator (iLQR) and alternating direction method of multipliers (ADMM) for motion planning in the context of autonomous driving is proposed. The method achieves high computation efficiency under various constraints and enables real-time computation and implementation, providing additional safety to on-road driving tasks.
In the context of autonomous driving, the iterative linear quadratic regulator (iLQR) is known to be an efficient approach to deal with the nonlinear vehicle model in motion planning problems. Particularly, the constrained iLQR algorithm has shown noteworthy advantageous outcomes of computation efficiency in achieving motion planning tasks under general constraints of different types. However, the constrained iLQR methodology requires a feasible trajectory at the first iteration as a prerequisite when the logarithmic barrier function is used. Also, the methodology leaves open the possibility for incorporation of fast, efficient, and effective optimization methods to further speed up the optimization process such that the requirements of real-time implementation can be successfully fulfilled. In this paper, a well-defined motion planning problem is formulated under nonlinear vehicle dynamics and various constraints, and an alternating direction method of multipliers (ADMM) is utilized to determine the optimal control actions leveraging the iLQR. The approach is able to circumvent the feasibility requirement of the trajectory at the first iteration. An illustrative example of motion planning for autonomous vehicles is then investigated. A noteworthy achievement of high computation efficiency is attained with the proposed development; comparing with the constrained iLQR algorithm based on the logarithmic barrier function, our proposed method reduces the average computation time by 31.93%, 38.52%, and 44.57% in the three driving scenarios; compared with the optimization solver IPOPT, our proposed method reduces the average computation time by 46.02%, 53.26%, and 88.43% in the three driving scenarios. As a result, real-time computation and implementation can be realized through our proposed framework, and thus it provides additional safety to the on-road driving tasks.

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