摘要
主要对汽车自动驾驶算法进行了优化设计。先根据实际情况完成路线的规划,通过使用置于车辆前端的各种传感设备完成图像的获取,然后处理采集到的图像并据此完成对车道线和障碍物的识别与检测,在此基础上实现汽车包括车辆行驶速度与方向在内的自动驾驶操作控制过程。通过对比实验检测该算法的应用效果与安全性,结果表明相比于基于BP神经网络和基于贝叶斯的自动驾驶算法,算法的处理时间得到有效缩短,进一步提升了控制准确度,为优化汽车驾驶安全性能提供参考。
This paper mainly optimizes the auto-driving algorithm.The algorithm first completes the route planning according to the actual situation.The image acquisition is completed by using various sensor devices placed in the front of the vehicle,and then the collected images are processed according to the actual conditions.This completes the identification and detection of lane lines and obstacles,and realizes the automatic driving operation control process of the car including the speed and direction of the vehicle.By comparing experiments to test the application effect and safety of the algorithm,the results show that compared with the BP neural network and Bayesian-based autonomous driving algorithms,the processing time of the algorithm in this paper is effectively shortened,and the control accuracy is further improved.The paper may provide reference for car driving safety performance.
作者
贺翠华
HE Cuihua(Automobile Engineering Department, Yantai Automobile Engineering Professional College, Yantai 265500, China)
出处
《微型电脑应用》
2020年第9期123-125,共3页
Microcomputer Applications
关键词
汽车自动驾驶
驾驶安全性
自动驾驶算法
图像采集
路线规划
autonomous vehicle driving
driving safety
autonomous driving algorithm
image acquisition
route planning
