A Driving Decision Strategy(DDS) Based on Machine learning for an autonomous vehicle

被引：0

作者：

Son, Surak ^{[1
]}

Jeong, Yina ^{[1
]}

Lee, ByungKwan ^{[1
]}

机构：

[1] Catholic Kwandong Univ, Comp Engn, Gangwon Do, South Korea

来源：

2020 34TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2020) | 2020年

基金：

新加坡国家研究基金会;

关键词：

Genetic Algorithm; Driving Strategy; Machine learning; Autonomous Vehicles;

D O I：

10.1109/icoin48656.2020.9016493

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 "A Driving Decision Strategy(DDS) Based on Machine learning for an autonomous vehicle" 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 DDS learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. This paper compared the DDS with MLP and RF neural network models to validate the DDS. In the experiment, the DDS had a loss rate approximately 5% lower than existing vehicle gateways and the DDS determined RPM, speed, steering angle and lane changes 40% faster than the MLP and 22% faster than the RF.

引用

页码：262 / 264

页数：3