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기계 학습을 활용한 구동 토크 예측 기반 차량 속도 프로파일 최적화
김병건,김기훈,안윤용,성지훈,최석훈,전영호,허건수 한국자동차공학회 2022 한국 자동차공학회논문집 Vol.30 No.6
A number of studies have been proposed in order to obtain the optimal vehicle speed profile for a given route based on dynamic programming(DP). In general, solving optimization problems requires a vehicle dynamics model to accurately calculate energy consumption. However, this model cannot exactly reflect the real characteristics of various vehicles because of the nonlinearity of the rolling resistance, air resistance, and gradient resistance. Therefore, this study proposes vehicle speed optimization by using a machine learning network model that is trained from actual vehicle driving data. The performance of the proposed method is verified by simulation where the driving environment is duplicated corresponding to real driving conditions. The effectiveness of the proposed optimal speed profile is evaluated by comparing with conventional cruise control driving. As a result, driving with the optimal speed profile for a given route of 27.3 km significantly reduces battery energy consumption by 8.4 %.
주변 차량 경로 예측을 통한 트랙터−트레일러 ACC 제어기 개발
조건희(Gunhee Cho),안윤용(Yoonyong Ahn),허건수(Kunsoo Huh) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
Inland cargo transportation mainly uses roads, and commercial vehicles play a major role in such transportation. Research related to safety is mainly conducted on autonomous driving of commercial vehicles. Especially, research to prevent collisions with surround vehicles is an important topic. In this study, after predicting the movement of surrounding vehicle, modeling the possibility of surrounding vehicle changing lanes. When a nearby vehicle changes lanes, lane change path candidates are created and fused with the Constant Turn Rate Velocity(CTRV) model to create a lane change path. Model Predictive Control (MPC) was used to develop an Adaptive Cruise Control (ACC) algorithm because it is used to determine safe and efficient control inputs. This algorithm has been validated by MATLAB/Simulink and the Truckmaker program.