CILQR for Autonomous Driving Motion Planning

This mini project implements a modified version of the Constrained iLQR for autonomous driving motion planning with soft constraints [1].

Results of Example Settings

Lane Change

initial_condition:
  state: [0., 3., 5., 0.]
obstacle:
  init_state: [2.5, 0., 3., 0.]

Overtaking

initial_condition:
  state: [0., 0., 5., 0.]

obstacle:
  init_state: [5., 1.2, 3., 0.]

Car Following

initial_condition:
  state: [0., 0., 4., 0.]

obstacle:
  init_state: [5., 0., 3., 0.]

References

[1] Chen, J., Zhan, W., & Tomizuka, M. (2017, October). Constrained iterative lqr for on-road autonomous driving motion planning. In 2017 IEEE 20th International conference on intelligent transportation systems (ITSC) (pp. 1-7). IEEE.