High-resolution micro traffic data are important to traffic safety and efficiency analysis. In this study, a roadside LiDAR sensor is used to collect 3D point clouds of surrounding objects. An automatic background construction and object detection method is proposed on the basis of the operation principle of the LiDAR sensor. In the algorithm, the discrete horizontal and vertical angular values can be considered as coordinates of pixels in digital images, and the farthest and mean distance of each azimuth are used to construct the background dataset. Then, vehicle and pedestrian points are extracted on the basis of the distance difference of each point with the same angular value between the target frame dataset and the background dataset. A density-based spatial clustering method is employed to group points into clusters and identify vehicles and pedestrians automatically. Finally, the performance of the algorithm is tested for roadside LiDAR data preprocessing under different traffic conditions. Our algorithm can perform well with high accuracy. Experimental results demonstrate that our algorithm can achieve a larger detection range and exhibit lower time complexity in comparison with a previously proposed algorithm.

Traffic information collection is an important foundation for intelligent transportation systems. In this paper, 3D Light Detection And Ranging (LiDAR) is deployed in the roadside of urban environments to collect vehicle and pedestrian information. A background filtering algorithm, including a mean background modeling to build a background map and a background difference method to filter static background noise points, is proposed for roadside fixed LiDAR facilities. Background points are filtered through the difference between data frames and a multi-level background map, and then there are still a small number of noise points. Aiming to reduce the noise points, a hierarchical maximum density clustering of applications with noise (HMDCAN) algorithm, utilizing both density clustering and hierarchical clustering, is proposed to effectively achieve both noise point filtering and target recognition. We verify our methods in a facility with a 16-channel LiDAR in which background filtering and target recognition are tested with different scenarios, and the accuracy rate is over 97%.

Traffic sign detection and recognition (TSDR) has attracted extensive studies recently due to its broad application prospect in Intelligent Transport Systems. TSDR is still challenging due to the small size of traffic signs in the image. Besides, the traffic signs in the real world exhibit a long-tailed distribution (i.e., data for most categories are scarce while for others are abundant.), which will lead to a significant performance drop of the detection framework. In this paper, we propose a novel traffic sign detection framework to address these challenging problems. In order to detect small traffic signs, we propose an effective adaptive and attentive spatial feature fusion module which learns the spatial attention map to fuse different feature maps at each scale while emphasizing or suppressing the features at different regions. This module can significantly alleviate the inconsistency among features and enhance feature representations of small objects. Furthermore, to address the long-tailed data problem, a hierarchical group softmax head which constructs a label tree to divide categories into different groups is proposed, in this way, categories in each group have relatively similar frequencies, then the softmax is applied in each relatively balanced group to calculate the probability of each category. Extensive experiments conducted on the TT100K and GTSDB datasets demonstrate that the proposed method achieves notable improvement in both the small traffic signs and long-tailed detection problems in TSDR.

Automatic traffic sign detection and recognition (ATDR) plays a crucial role as a sub-module of the intelligent transportation system (ITS) and has gained significant attention in recent years. However, ATDR faces challenges due to two main factors: the small size of traffic signs in images and the long-tailed distribution of sign categories in the real world. (i.e., only a few categories have sufficient samples, and most categories have insufficient samples.). These challenges result in a decline in the performance of existing detection frameworks. In this paper, we propose a novel framework for traffic sign detection, specifically designed to tackle the challenges posed by these issues. Our approach includes an efficient adaptive and transformed spatial feature fusion module for detecting small traffic signs. This module adaptively learns feature map associations to extract and fuse meaningful features through cross-space and cross-scale interactions at different levels. Additionally, to tackle the long-tailed traffic sign problem, we have designed a residual learning group classifier loss, by dividing the traffic signs into two levels, reserving a specific capacity for the long-tailed category from the perspective of the parameter space, the residual fusion mechanism is introduced to enhance and optimize the tail category image. Our experimental results, obtained from rigorous evaluations on the TT100K and GTSDB datasets, demonstrate the effectiveness of our approach. The improvements achieved in small traffic sign detection and long-tailed detection underscore the significance of our framework in advancing ATDR research.

In recent years, the surge of artificial intelligence has propelled autonomous driving technology to the forefront, capturing growing interest and enthusiasm. As a sub-module within autonomous driving, research in traffic sign detection and recognition has significantly advanced with a growing focus on utilizing deep learning methods. Nevertheless, in complex real-world road scenarios, traffic signs often exhibit a long-tailed distribution, with the majority of instances concentrated in a few frequent categories and a scarcity in the remaining ones. Considering conventional Traffic Sign Detection and Recognition methods are crafted using manually curated datasets, the class imbalance may detrimentally impact the efficacy of the detection model. In this paper, we first propose a gradient-guided loss reweighting model that dynamically reweights the loss for positive and negative samples based on the cumulative gradients across each category. Additionally, a classification bias-based refinement module is proposed to fine-tune these weights during training, based on the false positive and false negative rate. This serves to suppress a drop in precision for tail categories resulting from the gradient-guided loss reweighting module, thus further balancing the entire training process for improved results. Extensive experiments are performed on the TT100K and GTSDB datasets, yielding significant advancements surpassing state-of-the-art methods.

Road surface condition is a crucial factor affecting vehicle safety. Accurate and rapid identification of road conditions can enhance the adaptability of active safety control systems to adapt to complex working conditions, while predicting the road adhesion coefficient in advance can help active safety control systems adjust control strategies promptly to ensure driving safety and stability. This paper introduces an algorithm that combines the squeeze-and-excitation network (SENet) and convolutional neural network (CNN) to estimate the road adhesion coefficient based on the reflectance intensity features of LiDAR point clouds. The method mainly comprises the following seven steps: (i) data acquisition; (ii) point cloud segmentation; (iii) selection of the point cloud region of interest (ROI); (iv) filtering processing; (v) grayscale image generation; (vi) classification based on the SE-CNN model; and (vii) combination of the graph method to estimate the road adhesion coefficient. Data were collected on urban roads to construct the dataset. Experimental results indicate that different lighting conditions and speeds have minimal impact on the distribution of reflectance intensity in regional point clouds. The classification accuracy of this research method for dry asphalt, wet asphalt, dry concrete, and wet concrete reached 100%, 99.98%, 99.82%, and 99.23%, respectively. Compared to other machine learning methods, the proposed method exhibits higher accuracy and stability in predicting road surface adhesion coefficients.