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.
Highlights
Automated vehicles have been regarded as one of the most important trends in intelligent transportation systems with rapid developments recently, and are evaluated to enhance vehicular traffic, including increased highway capacity and traffic flow and fewer accidents with collision prevention systems [1,2]
The analysis showed that Faster R-convolutional neural network (CNN) had a similar accuracy, with a mean average precision (mAP) of 76.4, as You Only Look Once (YOLO) V2, with mAP of 78.6, but had a slower processing speed with an Frame Per Second (FPS) of 5 compared to YOLOV2 with an FPS of 40, which had difficulty detecting
20 classes were reduced to three classes which may exist on the highway, as there was a class imbalance problem with incorrect classification when pre-training with a set class of dataset
Summary
Automated vehicles have been regarded as one of the most important trends in intelligent transportation systems with rapid developments recently, and are evaluated to enhance vehicular traffic, including increased highway capacity and traffic flow and fewer accidents with collision prevention systems [1,2]. This study proposes the application of deep learning-based CNN to improve the recognition technique of vehicle-mounted monocular cameras for the design of preventive automated driving systems. To this end, we have introduced a variety of CNN methods and select CNN models suitable for analysis based on the performance and limitations of each model to recognize surrounding vehicles and estimate the distance from them through the suitable model for automated driving systems.
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