Decision-Making and Path Planning for Head-On Collision Avoidance on Curved Roads

Abstract

Deviating to the left on two-way roads can result in fatal head-on collisions. This article presents an intelligent decision-making and path-planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path-planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path-planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision-making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision-making process. Once the maneuver direction is determined, the collision-free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left- and right-hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.