TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm |
| 영문제목(English Title) |
An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm |
| 저자(Author) |
SuRak Son
YiNa Jeong
ByungKwan Lee
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| 원문수록처(Citation) |
VOL 13 NO. 12 PP. 5842 ~ 5861 (2019. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes “An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles” which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.
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| 키워드(Keyword) |
genetic algorithm
V2C
dig data
WAVE messages
optimal driving strategy
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| 파일첨부 |
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