Mohammed Hilfi

Effective traffic control using Artificial Intelligence (AI) is essential to ensure safe passage for all road users. AI-based collision detection systems offer advanced mechanisms to prevent accidents and improve highway safety. This research investigates two distinct collision scenarios: vehicle–pedestrian and vehicle–motorcyclist interactions. The proposed method in this research involves the bidirectional Long Short Term Memory (LSTM), Convolutional Neural Network with LSTM (CNN–LSTM), and transformer models. The model is furthermore tuned using random or grid search. For the pedestrian–vehicle scenario, the CNN–LSTM model achieved 99.76% accuracy, 99.77% precision, and 99.76% recall, highlighting its strong classification performance. In the vehicle–motorcyclist scenario, the bidirectional LSTM reached 99.73% accuracy with precision and recall of 99.15%, demonstrating its effectiveness in detecting imminent crashes. The optimized CNN-LSTM by random search has focused on decreasing the false-positive rate and increasing the positive rate. It has achieved superior results compared to previous research. These results suggest that the system could be effectively implemented as an early collision warning solution on edge devices.

Positioning a camera during laparoscopic and robotic procedures is challenging and essential for successful operations. During surgery, if the camera view is not optimal, surgery becomes more complex and potentially error-prone. To address this need, we have developed a voice interface to an autonomous camera system that can trigger behavioral changes and be more of a partner to the surgeon. Similarly to a human operator, the camera can take cues from the surgeon to help create optimized surgical camera views. It has the advantage of nominal behavior that is helpful in most general cases and has a natural language interface that makes it dynamically customizable and on-demand. It permits the control of a camera with a higher level of abstraction. This paper shows the implementation details and usability of a voice-activated autonomous camera system. A voice activation test on a limited set of practiced key phrases was performed using both online and offline voice recognition systems. The results show an on-average greater than 94% recognition accuracy for the online system and 86% accuracy for the offline system. However, the response time of the online system was greater than 1.5 s, whereas the local system was 0.6 s. This work is a step towards cooperative surgical robots that will effectively partner with human operators to enable more robust surgeries. A video link of the system in operation is provided in this paper.