Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving
This addresses safe overtaking for autonomous vehicles, but it is incremental as it builds on existing planning and control techniques.
The paper tackles the problem of autonomous overtaking by developing a method that optimizes trajectories for safety and minimal lane intrusion while allowing abortable maneuvers. Simulation results show the method can handle multiple behaviors like following, overtaking, and aborting within a single framework.
Overtaking is one of the most challenging tasks in driving, and the current solutions to autonomous overtaking are limited to simple and static scenarios. In this paper, we present a method for behaviour and trajectory planning for safe autonomous overtaking. The proposed method optimizes the trajectory by simultaneously enforcing safety and minimizing intrusion onto the adjacent lane. Furthermore, the method allows the overtaking to be aborted, enabling the autonomous vehicle to merge back in the lane, if safety is compromised, because of e.g. traffic in opposing direction appearing during the maneuver execution. A finite state machine is used to select an appropriate maneuver at each time, and a combination of safe and reachable sets is used to iteratively generate intermediate reference targets based on the current maneuver. A nonlinear model predictive controller then plans dynamically feasible and collision-free trajectories to these intermediate reference targets. Simulation experiments demonstrate that the combination of intermediate reference generation and model predictive control is able to handle multiple behaviors, including following a lead vehicle, overtaking and aborting the overtake, within a single framework.