  Constrained Iterative Linear Quadratic Regulator (CILQR), a variant of ILQR,
has been recently proposed for motion planning problems of autonomous vehicles
to deal with constraints such as obstacle avoidance and reference tracking.
However, the previous work considers either deterministic trajectories or
persistent prediction for target dynamical obstacles. The other drawback is
lack of generality - it requires manual weight tuning for different scenarios.
In this paper, two significant improvements are achieved. Firstly, a two-stage
uncertainty-aware prediction is proposed. The short-term prediction with safety
guarantee based on reachability analysis is responsible for dealing with
extreme maneuvers conducted by target vehicles. The long-term prediction
leveraging an adaptive least square filter preserves the long-term optimality
of the planned trajectory since using reachability only for long-term
prediction is too pessimistic and makes the planner over-conservative.
Secondly, to allow a wider coverage over different scenarios and to avoid
tedious parameter tuning case by case, this paper designs a scenario-based
analytical function taking the states from the ego vehicle and the target
vehicle as input, and carrying weights of a cost function as output. It allows
the ego vehicle to execute multiple behaviors (such as lane-keeping and
overtaking) under a single planner. We demonstrate safety, effectiveness, and
real-time performance of the proposed planner in simulations.
