2 research outputs found

    ๊ตํ†ต์•ฝ์ž ๋Œ€์ƒ ๊ฐ•๊ฑด ๋น„์ƒ์ œ๋™์žฅ์น˜ ๊ฐœ๋ฐœ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 8. ์ด๊ฒฝ์ˆ˜.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตํ†ต์•ฝ์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ์ž๋™๋น„์ƒ์ œ๋™ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ์ง„ํ–‰๋œ ์—ฐ๊ตฌ์ด๋‹ค. ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜๋ž€ ์„ผ์„œ๋กœ๋ถ€ํ„ฐ ์–ป์€ ํ™˜๊ฒฝ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์šด์ „์ž๊ฐ€ ์˜ˆ์ƒํ•˜์ง€ ๋ชปํ•œ ์‚ฌ๊ณ ๋ฅผ ํšŒํ”ผํ•˜๊ฑฐ๋‚˜ ์‚ฌ๊ณ ์˜ ํ”ผํ•ด๋ฅผ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ฐจ๋Ÿ‰์„ ์ œ๋™ํ•ด์ฃผ๋Š” ์žฅ์น˜์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜๊ฐ€ ์ ์ฐจ ์–‘์‚ฐ๋˜๊ณ  ๋ณด๊ธ‰๋˜๊ธฐ ์‹œ์ž‘ํ•œ ์ดํ›„ ์‚ฌ๋žŒ๋“ค์€ ์ด๋Ÿฌํ•œ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ตํ†ต ์•ฝ์ž์™€ ๊ด€๋ จ๋œ ์‚ฌ๊ณ ๊นŒ์ง€ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ๋“ค์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ๊ตํ†ต ์•ฝ์ž๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ๋ณดํ–‰์ž, ์ž์ „๊ฑฐ ๋“ฑ์˜ ์›๋™๊ธฐ๋ฅผ ์žฅ์ฐฉํ•˜์ง€ ์•Š์€ ๋„๋กœ ์‚ฌ์šฉ์ž๋กœ ์ •์˜๋œ๋‹ค. ๊ตํ†ต ์•ฝ์ž๋Š” ๋น„๋ก ๊ทธ ์†๋„๊ฐ€ ์ฐจ๋Ÿ‰์— ๋น„ํ•ด ๋Š๋ฆฌ์ง€๋งŒ, ์‹ค์ œ ์‚ฌ๊ณ ๊ฐ€ ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ๊ทธ ํ”ผํ•ด๊ฐ€ ์ปค์งˆ ์šฐ๋ ค๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ๊ตํ†ต ์•ฝ์ž์™€ ๊ด€๋ จ๋œ ์‚ฌ๊ณ ๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค. ์‚ฌ๊ณ ๊ฐ€ ๋ฐœ์ƒํ•˜๊ธฐ ์ด์ „์— ์œ„ํ—˜์„ ์ธ์ง€ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ž์ฐจ๋Ÿ‰ ๋ฐ ๋Œ€์ƒ ๊ตํ†ต ์•ฝ์ž์˜ ๊ฑฐ๋™์„ ์˜ˆ์ธกํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ž์ฐจ๋Ÿ‰ ๋ฐ ๊ตํ†ต ์•ฝ์ž์˜ ๊ฑฐ๋™์„ ๋ชจ์‚ฌํ•  ์ˆ˜ ์žˆ๋Š” ๋™์—ญํ•™ ๋ชจ๋ธ์ด ํ•„์š”ํ•˜๋‹ค. ์ฐจ๋Ÿ‰์˜ ๊ฒฝ์šฐ ์šด์ „์ž๊ฐ€ ์‚ฌ๊ณ ๋ฅผ ํšŒํ”ผํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‹ค์ œ๋กœ ์šด์ „์ž๊ฐ€ ์‚ฌ๊ณ ๋ฅผ ํšŒํ”ผํ•  ๋•Œ ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ํšŒํ”ผ ๊ฑฐ๋™์— ๋Œ€ํ•œ ๋ชจ์‚ฌ ์—ญ์‹œ ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ์ž์ฐจ๋Ÿ‰์˜ ๊ฑฐ๋™์€ ๋“ฑ๊ฐ€์†๋„ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ํ‘œํ˜„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ตํ†ต ์•ฝ์ž์˜ ๊ฒฝ์šฐ ๋ณดํ–‰์ž์™€ ์ž์ „๊ฑฐ๋ฅผ ๊ตฌ๋ถ„ํ•˜๋Š”๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€์ƒ ๊ตํ†ต ์•ฝ์ž์˜ ์ข…๋ฅ˜ ๊ตฌ๋ถ„ ์—†์ด ์•ˆ์ „ ์„ฑ๋Šฅ์„ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณดํ–‰์ž ๋ฐ ์ž์ „๊ฑฐ์˜ ๊ฑฐ๋™์€ ๋™์ผํ•œ ๋“ฑ์† ์ง์„  ์šด๋™ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ํ‘œํ˜„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ์˜ˆ์ธก๋œ ์ •๋ณด๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ์šด์ „์ž๊ฐ€ ์‚ฌ๊ณ ๋ฅผ ํšŒํ”ผํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ํŒ๋‹จํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋งŒ์•ฝ ์šด์ „์ž๊ฐ€ ์‚ฌ๊ณ ๋ฅผ ํšŒํ”ผํ•˜๊ณ ์ž ํ•  ๋•Œ ์ผ์ • ์ˆ˜์ค€์˜ ์•ˆ์ „๊ฑฐ๋ฆฌ๋ฅผ ํ™•๋ณดํ•˜์ง€ ๋ชปํ•  ๊ฒฝ์šฐ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜๊ฐ€ ์ž‘๋™ํ•˜์—ฌ ์ฐจ๋Ÿ‰์„ ์ œ๋™ํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ์ด ๋•Œ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜์˜ ๊ฐ•๊ฑด ์„ฑ๋Šฅ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ธก์ • ์‹œ์— ๋ฐœ์ƒํ•˜๋Š” ๋ถˆํ™•์‹ค์„ฑ ๋ฐ ์ •๋ณด ์˜ˆ์ธก ์‹œ์— ๋ฐœ์ƒํ•˜๋Š” ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์•ˆ์ „ ๊ฑฐ๋ฆฌ๋ฅผ ์ •์˜ํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ๊ฐœ๋ฐœ๋œ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜์˜ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ฐจ๋Ÿ‰ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํˆด์ธ Carsim๊ณผ MATLAB/Simulink๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด ๋•Œ ๊ฐœ๋ฐœํ•œ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜์˜ ๊ฐ•๊ฑด ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๋™์ผ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•ด 100ํšŒ ๋ฐ˜๋ณต ์ˆ˜ํ–‰ ํ•˜์˜€์œผ๋ฉฐ, ๋น„๊ต๋ฅผ ์œ„ํ•˜์—ฌ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ์ž๋™๋น„์ƒ์ œ๋™์žฅ์น˜๋ฅผ ํ•จ๊ป˜ ํ‰๊ฐ€ํ•˜์˜€๋‹ค.A robust autonomous emergency braking (AEB) algorithm for vulnerable road users (VRU) is studied. Autonomous emergency braking (AEB) is a system which helps driver to avoid or mitigate a collision using sensor information. After many kinds of AEB system is produced by automakers, researchers and automakers are currently focusing on VRU-related collisions. Vulnerable road users (VRU) usually defined as non-motorized road users such as pedestrian and cyclist. Although VRU are relatively slower than vehicle, VRU related collisions should be prevented due to their fatalities. Therefore, many researchers are trying to develop a VRU-AEB. In order to assess the risk of collision before it occurs, the motion of host vehicle and target VRU should be predicted. For this, dynamic models of host vehicle and target VRU is required. In the case of host vehicle, in order to judge whether a driver can avoid a collision or not, drivers evasive maneuver also should be predicted as well as normal driving maneuver. For this, the motion of the host vehicle is predicted using constant acceleration model. In the case of target VRU, since the identification between pedestrian and cyclist is difficult, safety performance of AEB should be guaranteed even if the type of the target is unclear. Therefore, the behavior of pedestrian and cyclist is described using a single constant velocity model. These predicted information is then used to judge whether a collision is inevitable or not. If a driver cannot avoid a collision with pre-defined limits and safety margin, then the proposed AEB system is activated to decelerate the vehicle. To guarantee the robust safety performance of AEB system, measurement uncertainty and prediction uncertainty are also considered while defining the safety margin. To evaluate the safety performance of proposed AEB system, simulation study is conducted via vehicle simulation tool Carsim and MATLAB/Simulink. To investigate the robust safety performance of the proposed AEB system, simulation study is repeated 100 times with same traffic scenario with uncertainties. Performance of the proposed AEB system is compared with the deterministic AEB which is introduced in this work.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Autonomous Emergency Braking System โ€“ Global Trend 4 1.3 Thesis Objectives and Outline 9 Chapter 2 Previous Researches 10 Chapter 3 Autonomous Emergency Braking Algorithm for Vulnerable Road Users 17 Chapter 4 Host Vehicle Motion Prediction 19 4.1 Host Vehicle State Estimation 20 4.2 Host Vehicle Evasive Maneuver Prediction 24 Chapter 5 Target VRU Motion Prediction 28 5.1 Target VRU State Estimation 29 5.2 Target VRU Motion Prediction 34 Chapter 6 Threat Assessment 35 6.1 Collision Judgement 35 6.2 Safety Boundary for Collision Judgement 39 6.3 Emergency Braking Mode Decision 42 Chapter 7 Simulation Result 43 Chapter 8 Conclusion 50 Bibliography 51 ๊ตญ๋ฌธ์ดˆ๋ก 59Maste

    Performance and Safety Enhancement Strategies in Vehicle Dynamics and Ground Contact

    Get PDF
    Recent trends in vehicle engineering are testament to the great efforts that scientists and industries have made to seek solutions to enhance both the performance and safety of vehicular systems. This Special Issue aims to contribute to the study of modern vehicle dynamics, attracting recent experimental and in-simulation advances that are the basis for current technological growth and future mobility. The area involves research, studies, and projects derived from vehicle dynamics that aim to enhance vehicle performance in terms of handling, comfort, and adherence, and to examine safety optimization in the emerging contexts of smart, connected, and autonomous driving.This Special Issue focuses on new findings in the following topics:(1) Experimental and modelling activities that aim to investigate interaction phenomena from the macroscale, analyzing vehicle data, to the microscale, accounting for local contact mechanics; (2) Control strategies focused on vehicle performance enhancement, in terms of handling/grip, comfort and safety for passengers, motorsports, and future mobility scenarios; (3) Innovative technologies to improve the safety and performance of the vehicle and its subsystems; (4) Identification of vehicle and tire/wheel model parameters and status with innovative methodologies and algorithms; (5) Implementation of real-time software, logics, and models in onboard architectures and driving simulators; (6) Studies and analyses oriented toward the correlation among the factors affecting vehicle performance and safety; (7) Application use cases in road and off-road vehicles, e-bikes, motorcycles, buses, trucks, etc
    corecore