No abstract

As a sustainable transportation system, car-sharing schemes have been attracting increasing attention. A large amount of research and practice has proved that the application and promotion of car sharing can help reduce the number of private cars purchased, increase the utilization rate of automobiles, effectively alleviate traffic congestion, save energy, and reduce emissions. Therefore, research on car sharing is imperative. The logit model is widely used in studies on car sharing and is an effective tool for analyzing traffic problems. This study first introduces the status of research into car sharing and analyzes the potential users and market prospects for shared cars. The study then provides the results from a questionnaire survey in Nanjing, China, to obtain sample data. Finally, a mixed logit model is established to analyze the influencing factors of car-sharing selection behavior. The results show that factors such as an individual’s housing situation and income significantly affect car-sharing decisions and that respondents who choose to use shared cars are relatively similar to commuters. The main contribution of this study is to use empirical analysis to determine the key influencing factors of car-sharing behavior in China and to provide practical insights for commercial practitioners and traffic planners.

Remote sensing object detection based on the combination of infrared and visible images can effectively adapt to the around-the-clock and changeable illumination conditions. However, most of the existing infrared and visible object detection networks need two backbone networks to extract the features of two modalities, respectively. Compared with the single modality detection network, this greatly increases the amount of calculation, which limits its real-time processing on the vehicle and unmanned aerial vehicle (UAV) platforms. Therefore, this paper proposes a local adaptive illumination-driven input-level fusion module (LAIIFusion). The previous methods for illumination perception only focus on the global illumination, ignoring the local differences. In this regard, we design a new illumination perception submodule, and newly define the value of illumination. With more accurate area selection and label design, the module can more effectively perceive the scene illumination condition. In addition, aiming at the problem of incomplete alignment between infrared and visible images, a submodule is designed for the rapid estimation of slight shifts. The experimental results show that the single modality detection algorithm based on LAIIFusion can ensure a large improvement in accuracy with a small loss of speed. On the DroneVehicle dataset, our module combined with YOLOv5L could achieve the best performance.

Multi-Object Tracking (MOT) is an important topic in computer vision. Recent MOT methods based on the anchor-free paradigm trade complicated hierarchical structures for tracking performance. However, existing anchor-free MOT methods ignore the noise in detection, data association, and trajectory reconnection stages, which results in serious problems, such as missing detection of small objects, insufficient motion information, and trajectory drifting. To solve these problems, this paper proposes Noise-Control Tracker (NCT), which focuses on the noise-control design of detection, association, and reconnection. First, a prior depth denoise method is introduced to suppress the fusion feature redundant noise, which can recover the gradient information of the heatmap fusion features. Then, the Smoothing Gain Kalman filter is designed, which combines the Gaussian function with the adaptive observation coefficient matrix to stabilize the mutation noise of Kalman gain. Finally, to address the drift noise issue, the gradient boosting reconnection context mechanism is designed, which realizes adaptive trajectory reconnection to effectively fill the gaps in trajectories. With the assistance of the plug-and-play noise-control method, the experimental results on MOTChallenge 16 &17 datasets indicate that the NCT can achieve better performance than other state-of-the-art trackers.

Improving traffic efficiency and safety is the goal of all countries due to the increasingly congested road environment worldwide. The progress of intelligence has promoted the development of the transportation industry. As the first step to intelligence, perception technology is an important part to realize intelligent transportation. Accurate and efficient traffic management systems, such as the automatic control of traffic lights at urban intersections or highway emergency disposal, need the support of advanced environmental sensing technology. In the application of traffic perception, millimeter wave radar and camera are two important sensors. Radar has been widely used in traffic incident perception due to its all-weather working capability; however, there are problems such as inability to detect stationary targets and poor target classification performance. Camera has the advantages of accurate target angle information measurement and rich details, but there are problems of inaccurate ranging and speed measurement and performance degradation in harsh weather conditions. Considering the complementary characteristics of the two sensors in information, an improved incident detection method based on radar-camera fusion is proposed. This method combines the advantages of millimeter wave radar and camera and improves the robustness of the traffic incident detection system. The detection performance is verified in the real experiment. The results show that the detection accuracy of the proposed fusion system is better than that of a single millimeter wave radar in all scenarios, and the accuracy is improved by more than 50% in some cases.