Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving

TL;DR

This paper introduces a novel behavior and trajectory planning method for autonomous overtaking that ensures safety, allows aborting maneuvers, and dynamically adapts to traffic conditions using model predictive control.

Contribution

The paper presents a new integrated framework combining finite state machines, safe set theory, and nonlinear model predictive control for safe, abortable overtaking in autonomous vehicles.

Findings

Successfully handles overtaking, following, and aborting within a unified framework.

Demonstrates safety and feasibility through simulation experiments.

Enables dynamic decision-making in complex traffic scenarios.

Abstract

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.