College of Engineering, Kettering University, Flint, MI 48504-6214, USA.
Sensors (Basel). 2021 Sep 9;21(18):6055. doi: 10.3390/s21186055.
Recent emerging automotive sensors and innovative technologies in Advanced Driver Assistance Systems (ADAS) increase the safety of driving a vehicle on the road. ADAS enhance road safety by providing early warning signals for drivers and controlling a vehicle accordingly to mitigate a collision. A Rear Cross Traffic (RCT) detection system is an important application of ADAS. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions. The RCT detection system detects obstacles at the rear while the car is backing up. In this paper, a robust sensor fused RCT detection system is proposed. By combining the information from two radars and a wide-angle camera, the locations of the target objects are identified using the proposed sensor fused algorithm. Then, the transferred Convolution Neural Network (CNN) model is used to classify the object type. The experiments show that the proposed sensor fused RCT detection system reduced the processing time 15.34 times faster than the camera-only system. The proposed system has achieved 96.42% accuracy. The experimental results demonstrate that the proposed sensor fused system has robust object detection accuracy and fast processing time, which is vital for deploying the ADAS system.
近年来，新兴的汽车传感器和先进驾驶辅助系统（ADAS）中的创新技术提高了车辆在道路上行驶的安全性。ADAS 通过为驾驶员提供预警信号并相应地控制车辆来减轻碰撞，从而提高道路安全性。后方交叉交通（RCT）检测系统是 ADAS 的一个重要应用。追尾事故是一种经常发生的碰撞类型，大约 29.7%的碰撞都是追尾事故。RCT 检测系统在汽车倒车时检测后方的障碍物。在本文中，提出了一种鲁棒的传感器融合 RCT 检测系统。通过结合两个雷达和一个广角摄像头的信息，使用提出的传感器融合算法来识别目标物体的位置。然后，使用迁移卷积神经网络（CNN）模型对物体类型进行分类。实验表明，与仅使用摄像头的系统相比，所提出的传感器融合 RCT 检测系统的处理时间快了 15.34 倍。该系统的准确率达到了 96.42%。实验结果表明，所提出的传感器融合系统具有鲁棒的目标检测准确性和快速的处理时间，这对于部署 ADAS 系统至关重要。
