The system supports seven-frequency positioning signal reception and employs Sanlian’s proprietary matching algorithm alongside real-time carrier phase RTK differential positioning technology, ensuring vehicle position accuracy within ±1 cm. By incorporating deep learning concepts, the system enables precise monitoring of the driver’s gaze, behavior, state, and head posture, allowing for automated evaluations that exceed the 44 items mandated by regulations.

The system utilizes industrial-grade embedded ARM chips and collects vehicle status information via OBD (On-Board Diagnostics) bus interface technology. It optimizes GNSS algorithms to effectively address GNSS anomalies, achieving high integration and reliability. This ensures efficient and stable operation even in adverse weather conditions.

Anhui Sanlian Applied Traffic Technology Co., Ltd. has pioneered the establishment of national network security standards. By creating a dedicated wireless network, enhancing access control, and self-managing the system, it prevents unauthorized devices from accessing and compromising the examination system while effectively mitigating the risk of human cheating, ensuring the security of the examination process.

Utilizing advanced video composition technology, the system processes and encodes diverse information from the examination process—including in-vehicle video, external footage, examination project video, candidate information, examination status, vehicle motion trajectories, vehicle signals, scoring, and results—into a single high-definition video for transmission and storage. This effectively reduces bandwidth usage and lowers hardware costs associated with video storage at examination venues, ensuring the integrity and synchronization of various data during post-examination reviews and simplifying the review process.

The system incorporates identity verification functions at various stages of the examination process (examination entrance, waiting area, pre-examination area, examination vehicle, self-service inquiry). After reading candidates’ ID documents, the system captures their images and compares them with identification photos and pre-examination photos to verify their identities. This streamlines the examination process, effectively preventing issues such as entering the wrong vehicle or impersonation. In cases of abnormal identity verification, the system can trigger alerts and upload the information to the examination center for further processing.

The vehicle driver behavior detection technology extracts video data of the driver’s posture using an on-board camera. It employs computer vision algorithms—including facial detection, focus detection, and intrusion detection—processed through deep learning neural networks to analyze behaviors such as checking the dashboard, observing the rearview mirror, and determining if the driver’s body extends outside the vehicle. This enables electronic evaluation of driving behaviors during the Subject 3 examination, effectively enhancing the automation rate of evaluations, improving objectivity and precision, and reducing labor costs.

Examiners can perform real-time video viewing, manual examination evaluation, candidate identity authentication, voice intercom, examination progress, status, re evaluation, video playback and other operations in the backend control center. The software displays the dynamic distribution of examination vehicles and abnormal alarms in real time. The combination of automatic evaluation and remote evaluation makes exam evaluation more accurate and scientific.

The system supports both 2D and 3D GIS maps, allowing for multi-angle viewing, zooming in and out, single vehicle tracking, and simultaneous monitoring of multiple vehicles. The operation is flexible, providing effective supervision during and after the examination and ensuring fairness and transparency in the examination process.