The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

Abstract

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

Image Processing Method of a Visual Communication System Based on Convolutional Neural Network

“…For the issue that closed-loop detection in the conventional Rat SLAM algorithm is easily influenced by illumination, Soochow University proposed an FT model in 2020. This model converts RGB images into saliency maps and extracts images containing more original image feature information from the saliency map (Lee et al, 2021). The visual scene recognition in closed-loop detection is more reliable thanks to the visualization template.…”

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter

“…Combining neural networks with superior predictive capabilities with improved Kalman filtering algorithms can lead to a generalized and seamless fusion strategy that can be extended to other potential future applications with multiple sensor combinations. Examples include: monocular visual-inertial-liDAR simultaneous localization-fusion, a three-level multisensor fusion system that can achieve robust state estimation and globally consistent mapping in perceptually degraded environments [28]; convolutional neural network (CNN)-based faster regions with CNN (faster R-CNN) and you only look once (YOLO) V2 are investigated for improving the recognition technique of in-vehicle monocular cameras for designing preventive autonomous driving systems [29]; the uncertainty function is approximated using a radial basis function neural network (RBFNN). This means that vehicle platoon practical consensus can be achieved under the Zeno-free adaptive event-triggered control scheme, making connected automated vehicles' platoon reach the string stability [30].…”

Accurate real-time obstacle detection of coal mine driverless electric locomotive based on ODEL-YOLOv5s

“…A few domestic coal mines realize the obstacle detection of electric locomotives based on LiDAR technology and, at the same time, realize electric locomotives that are driverless with remote monitoring and an intelligent dispatching system. However, the processing process based on LiDAR detection is complicated and vulnerable to environmental effects 3 , 4 . Narrow coal mine roadway space and uneven roadway walls will make the data collected by LiDAR have more noise, thus resulting in an unsatisfactory detection effect.…”