论文标题
使用计算机视觉在自动驾驶车辆中检测威胁
Threat Detection In Self-Driving Vehicles Using Computer Vision
论文作者
论文摘要
公路障碍检测是一个重要的研究领域，属于智能运输基础设施系统的范围。基于视觉的方法的使用为此类系统提供了准确且具有成本效益的解决方案。在这篇研究论文中，我们提出了一种威胁检测机制，用于使用仪表板视频进行自动驾驶的自动驾驶汽车，以确保在其视觉范围内的道路上存在任何不必要的障碍物。此信息可以帮助车辆的计划安全。有四个主要组件，即Yolo可以识别对象，高级车道检测算法，多回归模型，用于测量对象与摄像机的距离，测量安全速度的两秒钟规则和限制速度。此外，我们已经使用了车祸数据集（CCD）来计算模型的准确性。 Yolo算法的精度约为93％。我们提出的威胁检测模型（TDM）的最终准确性为82.65％。
On-road obstacle detection is an important field of research that falls in the scope of intelligent transportation infrastructure systems. The use of vision-based approaches results in an accurate and cost-effective solution to such systems. In this research paper, we propose a threat detection mechanism for autonomous self-driving cars using dashcam videos to ensure the presence of any unwanted obstacle on the road that falls within its visual range. This information can assist the vehicle's program to en route safely. There are four major components, namely, YOLO to identify the objects, advanced lane detection algorithm, multi regression model to measure the distance of the object from the camera, the two-second rule for measuring the safety, and limiting speed. In addition, we have used the Car Crash Dataset(CCD) for calculating the accuracy of the model. The YOLO algorithm gives an accuracy of around 93%. The final accuracy of our proposed Threat Detection Model (TDM) is 82.65%.
