Seungwuk Moon

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.

Multi-vehicle target selection for adaptive cruise control

This paper presents a target selection strategy for adaptive cruise control (ACC) in multiple vehicle traffic situations. Since there are many vehicles in a real road and various transitions between the subject vehicle and the neighbouring vehicles occurred, it is important to establish a target selection and a control strategy for applying the ACC system to multiple vehicle traffic scenes in order to improve the driver acceptance and the vehicle safety. For this purpose, it is necessary to determine which neighbouring vehicle is an important target for the adaptive cruise control and prevention of collision depending on a driving situation. A primary target selection algorithm decides an in-lane target and provides the information to a longitudinal controller in order to drive a subject vehicle smoothly and to ensure safety in multi-vehicle traffic situations. The proposed selection algorithm consists of an in-lane target detection, a motion-based analysis and an integration process. The performance and safety benefits of a multi-vehicle ACC system with proposed target selection strategy are investigated via simulations using several driving scenarios. Simulation results show that the system response is smooth and safe even in multiple vehicle driving situations.

Design and Testing of a Controller for Autonomous Vehicle Path Tracking Using GPS/INS Sensors

Abstract This paper describes a steering controller integrated with speed controller for autonomous path tracking using GPS and INS sensors. The steering controller for path tracking is developed based on the finite preview optimal control method. The steering control input is computed using the road information within preview distance. The speed controller determines the speed command necessary to maintain a lateral acceleration limit and improve vehicle safety. The vehicle model for simulation study is validated using vehicle test data. Finally, the controller is implemented on a by-wire vehicle, P1, to validate the performance of the steering controller integrated with speed controller.

Intelligent vehicle safety control strategy in various driving situations

This paper describes a safety control strategy for intelligent vehicles with the objective of optimally coordinating the throttle, brake, and active front steering actuator inputs to obtain both lateral stability and longitudinal safety. The control system consists of a supervisor, control algorithms, and a coordinator. From the measurement and estimation signals, the supervisor determines the active control modes among normal driving, longitudinal safety, lateral stability, and integrated safety control mode. The control algorithms consist of longitudinal and lateral stability controllers. The longitudinal controller is designed to improve the drivers comfort during normal, safe-driving situations, and to avoid rear-end collision in vehicle-following situations. The lateral stability controller is designed to obtain the required manoeuvrability and to limit the vehicle bodys side-slip angle. To obtain both longitudinal safety and lateral stability control in various driving situations, the coordinator optimally determines the throttle, brake, and active front steering inputs based on the current status of the subject vehicle. Closed-loop simulations with the driver–vehicle-controller system are conducted to investigate the performance of the proposed control strategy. From these simulation results, it is shown that the proposed control algorithm assists the driver in combined severe braking/large steering manoeuvring so that the driver can maintain good manoeuvrability and prevent the vehicle from crashing in vehicle-following situations.

Design, Tuning and Evaluation of Integrated ACC/CA Systems

Abstract This paper describes design, tuning and evaluation of integrated Adaptive Cruise Control with Collision Avoidance (ACC/CA). The control scheme is designed to control the vehicle so that it would feel natural to the human driver during normal safe driving situations and to completely avoid rear-end collision in vehicle following situations. Driving situations are divided into safe, warning and dangerous mode and three different control strategies have been used depending on driving situations. The driving situations are determined using a non-dimensional warning index and time-to-collision. A confusion matrix method based on manual driving data is used to tune the control parameters of the integrated ACC/CA system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared to real-world manual driving radar sensor data. A Hardware-in-the-loop Simulation (HiLS) was developed and used for an evaluation of integrated ACC/CA System. Finally the integrated ACC/CA system is implemented in a real vehicle and has been tested in both safe traffic and the severe braking situation. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.

Suppression of phonon transport in multiple Si/PtSi heterostructures

Using a Green function method based on an atomic vibration model, herein we report the results from our investigation of phonon transport through multiple Si/PtSi layered structures. In contrast with values predicted using elastic wave theory and an impedance mismatch method, we find that a detailed atomic-vibration approach exhibits significantly suppressed phonon transport and leads to a 30-times reduction of the thermal conductance, compared to that of Si bulk. We attribute the origin of the suppression to the lack of PtSi phonon modes in the energy range of 20–30 meV, and to the effects of interface scattering between Si and PtSi layers.

Design of a Path Tracking Scheme and Collision Avoidance Controller for Autonomous Vehicles

Abstract This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. The obstacle avoidance problem is treated using a nonlinear model predictive framework in which simplified dynamics are used to predict the state of the actual vehicle over the look-ahead horizon. Due to the slight dissimilarity between the simplified model used for trajectory generation and the actual vehicle trajectory, a separate tracking controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle is controlled using the inverse dynamics of the vehicle power-train model and the lateral controller is designed based on the linear quadratic regulator. In the nonlinear model predictive framework, the threat of local obstacles is augmented into the performance index using a parallax-based method. The simulation results show that the presented model-predictive-control-based trajectory generation and tracking controller, together, give satisfactory performance in terms of obstacle avoidance when applied to the full nonlinear vehicle model.

Design of a full-range ACC with collision avoidacne/mitigation braking

Abstract This paper describes design of a full-range ACC (Adaptive Cruise Control) with collision avoidance braking. The control scheme is designed to control the vehicle so that it would feel natural to the human driver and passengers during normal safe driving situations, to completely avoid collision in vehicle following situations and to reduce the severity of collisions in unexpected emergency situations. Driving situations are determined by using a non-dimensional warning index and time-to-collision (TTC). The proposed control scheme can provide natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent vehicle collision in dangerous traffic situations such as severe braking of the preceding vehicle during a vehicle following situation.

Design of a Longitudinal Driving/Safety Control Algorithm in Multi-Vehicle Traffic Situations

Abstract This paper presents a longitudinal driving/safety control algorithm in multiple vehicle traffic situations. Since there are many vehicles in a real road and various transitions between the subject vehicle and the neighboring vehicles are occurred, it is important to establish an integrated longitudinal control algorithm which provides comfort vehicle behavior and uses a large deceleration to maintain safe vehicle-to-vehicle clearance in multi-vehicle traffic situations. For this purpose, it is necessary to determine which the neighboring vehicle is an important target for the adaptive cruise control and prevention of collision depending on a driving situation. In addition, it is required to calculate the desired acceleration in order to improve the driver acceptance and the vehicle safety. The performance and safety benefits of the proposed longitudinal driving/safety control algorithm are investigated via simulations using several driving scenarios. Simulation results show that the system response is smooth and safe even in multiple vehicle driving situations.

Multi-Vehicle Adaptive Cruise Control with Collision Avoidance in Multiple Transitions

Abstract This paper presents a longitudinal control algorithm for an adaptive cruise control (ACC) with collision avoidance (CA) in multiple transitions by a driver and traffic conditions. The objective is for smooth and safe transition between various driving situations to achieve drivers comfort and prevention of collision. The proposed algorithm consists of a multi-target tracking filter, a primary target selection algorithm and an integrated ACC/CA controller. The multi-target tracking filter is used to smooth the sensor signal, and makes it possible to apply to a control system. The primary target selection algorithm decides an important target which has an effect on the subject vehicle. In addition, it provides the information to an integrated ACC/CA controller in order to drive a subject vehicle smoothly and improve safety in various traffic transitions. Finally, the integrated ACC/CA controller computes the desired acceleration. In order to reduce the effects of discontinuous change by drivers such as time gap, set speed, and automation switching, an input-shaping filter technique is applied to integrated ACC/CA controller. The performance and safety benefits of the multi-vehicle ACC/CA system is investigated via simulations using real data on driving. Simulation results show that the response of multi-vehicle ACC/CA system is more smooth and safer in multiple transitions.