Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour.

Appropriate interaction with human-driven vehicles is crucial for advancing autonomous vehicles from demonstrations in controlled environments to applications in open-road scenarios. Accurately recognizing and quantifying driver interaction behaviors is essential for understanding their interaction intentions and subsequently formulating suitable responses. Previous studies have primarily focused on the generation of interaction behaviors. However, insufficient attention has been paid to the recognition and quantification of driver interaction behaviors. We establish a higher-order traffic flow network and present mathematical definitions for driver interaction behaviors and their strengths based on this framework. Furthermore, we find that humans identify interaction behaviors during driving by perceiving conflicts between different drivers’ intentions. Therefore, we propose a driver resistance field model (DRFM) to characterize driving intentions and describe these conflicts through operations within the resistance fields, enabling the recognition of interactions. Through computational social experiments and real-world experiments, the DRFM is demonstrated to accurately quantify the strength of driver-driver interactions. The analysis of extensive cases indicates that the quantification from the DRFM effectively capture the dynamics of interaction behaviors, aligning with human cognition of driver interactions, with remarkably higher correlation coefficients compared to baseline methods.

Appropriate interaction with human-driven vehicles is crucial for advancing autonomous vehicles from demonstrations in controlled environments to applications in openroad scenarios. Accurately recognizing and quantifying driver interaction behaviors is essential for understanding their interaction intentions and subsequently formulating suitable responses. Previous studies have primarily focused on the generation of interaction behaviors. However, insufficient attention has been paid to the recognition and quantification of driver interaction behaviors. We establish a higher-order traffic flow network and present mathematical definitions for driver interaction behaviors and their strengths based on this framework. Furthermore, we find that humans identify interaction behaviors during driving by perceiving conflicts between different drivers' intentions. Therefore, we propose a driver resistance field model (DRFM) to characterize driving intentions and describe these conflicts through operations within the resistance fields, enabling the recognition of interactions. Through computational social experiments and real-world experiments, the DRFM is demonstrated to accurately quantify the strength of driver-driver interactions. The analysis of extensive cases indicates that the quantification from the DRFM effectively capture the dynamics of interaction behaviors, aligning with human cognition of driver interactions, with remarkably higher correlation coefficients compared to baseline methods.