Predictive Maneuver Planning for an Autonomous Vehicle in Public Highway Traffic

This paper outlines a predictive maneuver-planning method for autonomous vehicle navigating public highway traffic. The method integrates discrete maneuvering decisions, i.e., lane and reference speed selection automata, with a model predictive control-based motion trajectory-planning scheme. A key notion is to apply a predictive reference speed pre-planning for each lane at each time step of a selected prediction horizon. This is done based on the predicted likely motion of the autonomous vehicle and other object vehicles subject to sensor noise and environmental disturbances. Then, an optimization problem is configured that computes safe, sub-optimal plans for the trajectories of both the motion states (and inputs) and maneuver references for the prediction horizon to accomplish maneuvers like lane keeping, lane change, or obstacle avoidance. While a first formulation of the problem results in a mixed-integer nonlinear programming problem, it is shown that a relaxation can be adopted that reduces the computational complexity to a low-order polynomial time nonlinear program that can be solved more efficiently. Through simulation of a series of multi-lane highway scenarios and comparison with one-maneuver planning approach and an adaptive cruise control approach, the proposed predictive maneuver planning is illustrated to better accommodate the traffic environment with feasible execution time. Also, the reference speed pre-planning improves the optimality and the robustness of the maneuver decision in trajectory planning without adding computational complexity to the optimization problem.