# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 2 Literature Review
### Abnormal Trajectory Recognition  
Microscopic vehicle driving trajectories are closely related to traffic safety analysis. [PERSON] et al. identified abnormal driving states such as overspeed, low speed and stop based on video detection.  
There are many ways to obtain microscopic vehicle driving behavior, including a large number of real event records, driving simulators and microscopic traffic simulation software. Based on obtaining trajectories, the risk factors of traffic accidents can be associated. Among them, [PERSON] et al. [26] systematically studied the relationship between intervention factors and driving behavior safety, providing scientific decision-making basis for traffic safety management. [PERSON] et al. [27] used the trajectory data of floating cars to analyze the colliding-related correlation factors.  
Many scholars have studied traffic control to improve safety based on microscopic vehicle trajectory results. [PERSON] et al. [28] studied the traffic safety guarantee technology. Teams such as Tongji University and Jiangsu University also established spatio-temporal correlation analysis between accidents on multi-dimensional risks, laying a foundation for proactive traffic control [29].  
Despite the advancements achieved in the field of abnormal trajectory recognition in surveillance videos, several challenges and issues persist. Firstly, there is an ongoing discussion regarding the definition of standards and evaluation metrics for abnormal trajectories. Different scenarios and applications may have varying definitions of abnormality, thus necessitating the establishment of a flexible and universally applicable standard to ensure the suitability and comparability of recognition methods. Additionally, for more accurate algorithm performance evaluation, there is a need to develop finer and more objective evaluation metrics that adequately consider the characteristics of different types of anomalies.  
Furthermore, various sources of interference in video data remain a formidable obstacle. Factors such as noise, occlusion, and variations in lighting can disrupt the accurate recognition of abnormal trajectories, necessitating more powerful data preprocessing and enhancement techniques to enhance the system's robustness against these interferences. Moreover, for cases involving occlusions, exploring data fusion methods from multiple cameras or sensors is essential to obtain more complete and accurate information. Thirdly, real-time performance, scalability, and interpretability are pressing concerns in abnormal trajectory detection methods. Prompt detection of anomalies is crucial in many practical applications, thus necessitating the exploration of efficient algorithms and hardware acceleration techniques for achieving real-time processing. Simultaneously, with the increase in the number of monitoring devices, scalability of the system becomes paramount, requiring the design of algorithms and architectures capable of operating in large-scale environments. Fourthly, the fusion of multimodal information is one of the directions for future development. Utilizing various sources of information such as sound and text allows for a more comprehensive scene analysis and anomaly detection, thereby enhancing overall detection efficacy. However, effectively integrating and jointly analyzing such diverse types of data still warrants further research.  
In summary, despite the significant progress achieved in the field of abnormal trajectory recognition in surveillance videos, interdisciplinary collaboration and ongoing research efforts are still required to address the aforementioned challenges and issues, thereby promoting the further advancement and application of this field.