Key points are not available for this paper at this time.
Camel-Vehicle Collisions (CVCs) have been a major cause of public safety hazards in the Gulf countries, in particular, the United Arab Emirates and Saudi Arabia. It is similar to deer-vehicle collisions in the United States and kangaroo-vehicle collisions in Australia. When it comes to road accidents, each millisecond of early warning/detection matters, potentially transforming a life-threatening situation into a manageable incident. Therefore, this study aims to mitigate CVCs by integrating quantum computing and Generative Pretrained Transformer-4Omni (GPT-4o) into autonomous vehicles (AVs). Environmental data was sourced through high-accuracy sensors in conjunction with quantum algorithms and GPT-4o modules handling the processing through a multi-phase approach. The integration allowed for enhanced detection, tracking, and decision-making regarding camel movements. Heatmaps revealed heightened camel activity during low-visibility nighttime conditions and seasonal peaks, particularly during the breeding season. The quantum-enhanced algorithms achieved a mean detection accuracy of 95% and tracking precision of 92%, with computational speeds 10x that of classical methods. GPT-4o vision modules further improved object detection accuracy to 98.7%, securing (ʈ=0.97) seconds as an early warning to the AV’s braking system. This innovative approach offers a novel paradigm in AV technology, setting new standards for safety and efficiency in transportation. Future GPT-5 models can be used in AVs and with sophisticated technologies for safer driving and wildlife protection. The results obtained from this study go beyond updating AV safety technology, opening avenues for application in urban planning and real-time hazard detection systems.
Samer Abaddi (Mon,) studied this question.