학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2014. 2. 이경수. ; In recent years, automakers have been trying to help drivers to avoid or mitigate collision with active safety system. Practical applications have become possible due to recent advances in exterior sensors and electronically controllable actuators. These advances have opened up many possibilities for active safety systems like lane keeping assistance system (LKAS), advanced emergency braking system (AEBS) and blind spot detection (BSD). The further enhancement of these technologies will lead to an automated driving system that requires a collision avoidance function using by automatic braking and even automatic throttle and steering. This dissertation focuses on automated collision avoidance using automated steering control and control of motor-driven power steering (MDPS) torque overlay and differential braking for emergency driving support (EDS). A robust Model Predictive Control (MPC) method is used in order to guarantee safety constraints despite of disturbances and model uncertainty. A minimum Robust Positively Invariant (RPI) set of vehicle state error is calculated and the robust MPC calculates the appropriate collision avoidance steering. The performance of the proposed algorithm has been investigated via computer simulations. The simulation studies show that the controlled vehicle can achieve safe collision avoidance maneuver using small lateral acceleration in a long distance preceding vehicle avoidance scenario, and it can achieve safe collision avoidance maneuver using high lateral acceleration in a sudden appeared vehicle avoidance scenario. Electrically controllable actuators, MDPS and differential brake system are used as actuators instead of automated steering and a radar and camera are used as a sensor system for the EDS algorithm. Using environment and vehicle information obtained from the sensor system, a risk of collision and driver’s intention are determined. A trapezoidal acceleration profile (TAP) is generated incorporating the driver’s intention and based on the TAP, the MDPS overlay torque is determined in order to assist the driver’s speed of response. The differential braking is determined to maximize the minimum vehicle-to-vehicle distance to avoid collision. From the non-linear optimal control problem, the rule-based control algorithm is designed for real-time application. The performance of the proposed algorithm has been investigated via computer simulations and real-time 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 8 test drivers using the human-in-the-loop simulations. It has been shown that the most of the test drivers can benefit from the proposed support system. ; Abstract i List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Backgraound and Motivation 1 1.2 Previous Researches 5 1.3 Thesis Objectives 8 1.4 Thesis Outline 10 Chapter 2 Automated Collision Avoidance using Robust Model Predictive Control 11 2.1 Model Predictive Control Problem 12 2.1.1 Vehicle model 13 2.1.2 Constraint design 17 2.1.3 Model predictive control formulation 21 2.2 Robustness Analysis 24 2.2.1 Disturbance analysis 24 2.2.1.1 Tire force disturbance 24 2.2.1.2 Vehicle parameter uncertainties 29 2.2.2 Linear state feedback 30 2.2.3 Robust Positively Invariant set computation 31 2.3 Simulation 33 Chapter 3 Emergency Driving Support 41 3.1 Overview of Emergency Driving Support 43 3.1.1 Danger area estimation 43 3.1.2 Index module 46 3.1.2.1 Lane change intention index 46 3.1.2.2 Collision risk index 48 3.1.2 State manager 50 3.2 Motor Driven Power Steering Overlay Torque Control 52 3.2.1 Trapezoidal Acceleration Profile 53 3.2.2 Motor Driven Power Steering overlay torque control 58 3.2.2.1 Linear model analysis 59 3.2.2.2 Non-linear vehicle simulation 63 3.2.2.3 Map-based overlay torque control 65 3.3 Differential Braking Control 66 3.3.1 Optimal control problem 67 3.3.2 Rule-based differential braking control 71 3.3.3 Non-linear vehicle simulation 77 3.4 Evaluation 80 3.4.1 Simulation 80 3.4.2 Best case scenario evaluation 82 3.4.2 Evaluation on a Virtual Test Track 84 Chapter 4 Conclusions and Future Works 89 Bibliography 92 국문초록 100 ; Doctor
Emergency Driving Support Algorithm for Collision Avoidance ; 충돌 회피를 위한 긴급 주행 보조 알고리즘
2014-01-01
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |
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