Bongchul Ko

TL;DR: In this paper, a hierarchical lane keeping assistance control algorithm for a vehicle is proposed, which consists of a supervisor, an upper-level controller and a lower-level control strategy.

TL;DR: An interacting multiple model (IMM) approach using extended Kalman filters (EKFs) to improve multitarget state estimation performance with utilization of automotive radars and a performance comparison with a model-switching algorithm shows that the target vehicle's overall behavior can be estimated by the proposed elaborated models, and the estimation performance can be significantly enhanced by the suggested IMM-based algorithm.

TL;DR: In this paper, a model predictive control (MPC) based motion planning controller for automated driving on a motorway using a vehicle traffic simulator is presented, where the desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon.

TL;DR: The simulation studies show that the controlled vehicle can secure additional vehicle-to-vehicle distance in severe lane change maneuvering for collision avoidance and has been shown that most of the test drivers can benefit from the proposed support system.

TL;DR: The optimal trajectory planning uses the dynamic drivable area as a safety constraint and computes a trajectory in which the vehicle stays in its safe bounds considering the driver?s pattern and characteristics based on predicted risk potential.