Bongchul Ko

Coordinated Control of Motor-Driven Power Steering Torque Overlay and Differential Braking for Emergency Driving Support

This paper describes a coordinated control of motor-driven power steering (MDPS) torque overlay and differential braking for emergency driving support (EDS). The coordinated control algorithm is designed to assist drivers to overcome hazardous situations. Electrically controllable MDPS and brake system are used as actuators, and a radar and a camera are used as a sensor system. Using environmental and vehicle information obtained from the sensor system, a risk of collision and drivers intention are determined, and a collision avoidance trajectory is generated, incorporating the drivers intention. Based on the generated collision avoidance trajectory, the MDPS overlay torque is determined to assist the drivers speed of response, and differential braking is determined to maximize the minimum vehicle-to-vehicle distance to avoid collision. The performance of the proposed algorithm has been investigated via computer simulations and real-time (RT) human-in-the-loop simulations. The simulation studies show that the controlled vehicle can secure additional vehicle-to-vehicle distance in severe lane change maneuvering for collision avoidance. The success rate of collision avoidance has been investigated for eight test drivers using the human-in-the-loop simulations. It has been shown that most of the test drivers can benefit from the proposed support system.

Vehicle sensor and actuator fault detection algorithm for automated vehicles

This paper presents a vehicle sensor and actuator fault detection algorithm for automated vehicles. The diagnostic system is designed to monitor steering wheel angle, yaw-rate, and wheel speed sensors and steering, throttle, and brake actuators used by the lateral and longitudinal controllers of the vehicle. Different combinations of the observer estimates, the sensor measurements, and the control commands are used to construct a bank of residuals. A fault in any of the vehicle sensors and actuators leads to increase of the unique subset of residuals. The adaptive threshold is used to enable exact identification of the abnormal increase of residual. The fault detection performance and its reliability of the proposed algorithm have been investigated via computer simulation studies and real-time vehicle tests. The enhancement of the fault detection allows for realization of autonomous driving vehicle which uses actuation by embedded computer.

Design of Integrated Risk Management-Based Dynamic Driving Control of Automated Vehicles

This paper describes the design of a fully automated driving algorithm for automated driving in complex urban scenarios and motorways with a satisfactory safety level. The proposed algorithm consists of the following three steps: surround recognition, motion planning, and vehicle control. The surround recognition system consists of three main modules: object classification, vehicle/non-vehicle tracking and dynamic drivable area determination. All system modules utilize information from potentially commercializable sensors such as vision sensors, radars, lidar and vehicle sensors. The objective of the motion planning module is to derive an optimal trajectory as a function of time and the surround recognition results. A dynamic drivable area is represented as a complete driving corridor that leads to the destination while making sure all objects are outside the left or right corridor bounds. In the case of moving objects such as other traffic participants, their behaviors are anticipated within the dynamic drivable area. 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. The developed algorithm has been evaluated by computer simulation and vehicle tests on urban roads and motorways.

Risk management algorithm for rear-side collision avoidance using a combined steering torque overlay and differential braking

This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase drivers safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuators control inputs and vehicle dynamics to safe values for the assurance of the drivers comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.